An Introduction to LIDAR: The Key Self-Driving Car Sensor
An Introduction to LIDAR: The Key Self-Driving Car Sensor
At Voyage we recently collective the news of Homer, our very first self-driving taxi. Homer is outfitted with a entire range of sensors to aid in understanding and navigating the world, key to which is LIDAR (brief for light detection and ranging). In this post you’ll learn more about LIDAR, its origins in the self-driving car space, and how it stacks against other sensors. Love!
LIDAR enables a self-driving car (or any robot) to observe the world with a few special super powers:
- Continuous three hundred sixty degrees of visibility – Imagine if your human eyes permitted you to see in all directions all of the time
- Insanely accurate depth information – Imagine if, instead of guessing, you could always know the precise distance (to an accuracy of ±2cm) of objects in relation to you
If you’ve seen a self-driving car before, you’ve very likely seen a LIDAR sensor. It’s typically the bulky box mounted on the roof that spins continuously, as seen below on Uber and Baidu self-driving cars.
One of the most popular LIDAR sensors on the market is the high-powered Velodyne HDL-64E, as seen below mounted on Homer.
How Does LIDAR Work?
How does a sensor that has three hundred sixty degree vision and accurate depth information work? Simply put: a LIDAR sensor continually fires off slats of laser light, and then measures how long it takes for the light to come back to the sensor.
By firing off millions of slats of light per 2nd, the measurements from the LIDAR sensor enable a visualization of the world that is truly 3D. You can infer the exact measurement of any object around you (up to around
60m, depending on the sensor)
A Brief History of LIDAR
To understand why there’s so much support behind LIDAR today, it’s significant to look at other similar technologies which have similar goals.
Sonar
The original depth-sensing robot was the modest Bat (50 million years old!). A bat (or dolphin, among others) is able to perform some of the same capabilities as LIDAR using echolocation, otherwise known as Sonar (sound navigation and ranging). Instead of measuring light rafters like LIDAR, Sonar measures distance using sound sways.
After fifty million years of biological exclusivity, World War one advanced the timeline of the very first major deployment of man-made Sonar sensors, with the advent of submarine warfare. Sonar works excellently in water, where sound travels far better than light or radio sways (more on that in a 2nd). Sonar sensors are in active use on cars today, primarily in the form of parking sensors. These short-range (
5m) sensors enable a cheap way to know just how far that wall is behind your car. Sonar hasn’t been proven to work at the kinds of ranges a self-driving car requests (60m+).
Radar
Radar (radio direction and ranging), much like Sonar, was another technology developed during an infamous World War (WW2, this time). Instead of using light or sound flaps, it instead utilizes radio sways to measure distance. We make use of a lot of Radar (using Delphi sensors) on Homer, and it’s a tried-and-tested method that can accurately detect and track objects as far as 200m away.
Radar has very little in terms of downside. It performs well in extreme weather conditions and is available at an affordable pricepoint. Radar is strenuously used not only for detection of objects, but tracking them too (ex: understanding how rapid a car is going and in which direction). Radar doesn’t necessarily give you granularity of LIDAR, but Radar and LIDAR are very complimentary, and it’s certainly not either/or.
LIDAR
LIDAR was born in the 1960s, just after the advent of the laser. During the Apollo fifteen mission in 1971, astronauts mapped the surface of the moon, providing the public the very first peek of what LIDAR could do.
Before LIDAR was even considered for automotive and self-driving use, one of the popular use-cases of LIDAR was archeology. LIDAR provides a ton of value for mapping large-scale swaths of land, and both archeology and agriculture benefitted tremendously from it.
LIDAR technology is becoming increasingly popular, and its applications and uses are in many different fields.grindgis.com
It wasn’t until the 2000s when LIDAR was very first utilized on cars, where it was made famous by Stanley (and later, Junior) in the two thousand five Grand DARPA Challenge.
Stanley, the winner of the two thousand five Grand DARPA Challenge, made use of five SICK LIDAR sensors mounted on the roof, in addition to a military-grade GPS, gyroscopes, accelerometers and a forward-facing camera looking out 80m+. All of this was powered by six 1.6GHz Pentium Linux PCs sitting in the trunk.
The fundamental challenge with the SICK LIDARs (which powered a significant portion of the two thousand five challenge vehicles) is that each laser scan is essentially a cut made by a single plane, and so you had to be methodical in how you pointed them. Many teams mounted them on tilting stages, in order to use them to “sweep” a segment of space. In elementary terms: SICK was a 2D LIDAR (a few rafters of light in one direction) vs. the modern 3D LIDARs (tons of slats of light in all directions) we know today.
Come in Velodyne
Velodyne has long been the market leader in LIDAR, however they didn’t commence out life that way. Velodyne began life as an audio company in 1983, specializing in low-frequency sound and subwoofer technology. The subwoofers contained custom-made sensors, DSPs and custom-made DSP control algorithms. Velodyne became the LIDAR company we know today at the same time as Stanley’s debut. Velodyne founders David and Bruce Hall very first entered the two thousand four DARPA competition as Team DAD (Digital Audio Drive). For the 2nd race In 2005, David Hall invented and patented the 3D laser-based real-time system that laid the foundation for Velodyne’s current LIDAR products today. By the 3rd DARPA challenge in 2007, the majority of teams used this technology as the basis of their perception system. David Hall’s invention is now in the Smithsonian as a foundational breakthrough enabling autonomous driving.
The very first Velodyne LIDAR scanner was about thirty inches in diameter and weighed close to one hundred pounds. Choosing to commercialize the LIDAR scanner instead of contesting in subsequent challenge events, Velodyne was able to dramatically reduce the sensor’s size and weight while also improving spectacle. Velodyne’s HDL-64E LIDAR sensor was the primary means of terrain map construction and obstacle detection for all the top DARPA Urban Challenge teams in two thousand seven and used by five out of six of the completing teams, including the winning and second-place teams. Some teams relied exclusively on the LIDAR for the information about the environment used to navigate an autonomous vehicle through a simulated urban environment. – Wikipedia
Traction of LIDAR in Self-Driving Cars
Why did LIDAR take off with self-driving cars? In a word: mapping. LIDAR permits you to generate ample 3D maps (its original application!), which you can then navigate the car or robot predictably within. By using a LIDAR to map and navigate an environment, you can know ahead of time the bounds of a lane, or that there is a stop sign or traffic light 500m ahead. This kind of predictability is exactly what a technology like self-driving cars requires, and has been a big reason for the progress over the last five years.
Object Detection
As LIDARs have become higher-resolution and operate at longer ranges, a fresh use-case has emerged in object detection and tracking. Not only can a LIDAR map enable you to know precisely where you are in the world and help you navigate it, but it can also detect and track obstacles like cars, pedestrians and according to Waymo, football helmets.
Modern LIDAR enables you to differentiate inbetween a person on a bike or a person walking, and even at what speed and which direction they are going in.
The combination of amazing navigation, predictability and high-resolution object tracking has meant that LIDAR is the key sensor in self-driving cars today, and it’s hard to see that supremacy switching. Unless…
Camera-Powered Cars
There’s a number of startups out there approaching the problem of self-driving cars using purely cameras (and perhaps radar), with no LIDAR in view. Tesla is the fattest company of the bunch, and Elon Musk has repeatedly shoved the idea that if humans can perceive and navigate the world using just eyes, ears and a brain, then why can’t a car? I’m certain that this treatment will achieve amazing results, especially as other talented teams work toward this objective, including Comma and AutoX.
It’s significant to note that Tesla has an interesting constraint that may have factored in to their decision: scale. Tesla hopes to ship 500k cars a year very soon, and can’t wait for LIDAR to come down in cost (or be manufactured in volume) tomorrow, it needed to happen yesterday!
Tesla CEO Elon Musk held a press conference a duo of days ago to explain the Autopilot features included in the…9to5google.com
The Future of LIDAR
The industry is marching ahead with a real concentrate on: cost decrease and resolution and range increase.
Cost Decrease
Solid-state LIDAR opens up the potential of sub-$1k powerful LIDAR units, which today can cost as much as $80k a unit. LeddarTech are one of the leaders in this early market.
Here’s what Velodyne has to say about solid-state:
Solid state, motionless sensors are driven by the idea that you want an embeddable sensor with the smallest size at the lowest possible cost. Naturally, that also means that you have a smaller field of view. Velodyne supports both motionless and surround view sensors. The immovable sensors are miniaturized to be embedded. From a cost standpoint, both contain lenses, lasers and detectors. The lowest cost system is actually via surround view sensors because rotation reuses the lens, lasers and detectors across the field of view, versus using extra sensors each containing individual lenses, lasers and detectors. This reuse is both the most economical, as well as the most powerful, as it reduces the error associated with merging different points of view in real-time — something that truly counts when the vehicle is moving at speed.
Resolution and Range Increase
The fat leap in the number of applications for LIDAR has brought with it a flood of talented founders and teams beginning companies in the space. Higher resolution output and enhanced tracking range (200m in some cases) will provide better object recognition and tracking, and are one of the key differentiators in sensors from startups like Luminar.
At Voyage, we’ve placed a bet on LIDAR. We love all the benefits that it brings, and believe the ecosystem will take care of bringing down the cost just in time for when we need to scale our autonomous taxi service. If you’re a LIDAR startup and want to test your sensors, we’d love to be one of your very first customers. Reach out on our website!
An Introduction to LIDAR: The Key Self-Driving Car Sensor
An Introduction to LIDAR: The Key Self-Driving Car Sensor
At Voyage we recently collective the news of Homer, our very first self-driving taxi. Homer is outfitted with a entire range of sensors to aid in understanding and navigating the world, key to which is LIDAR (brief for light detection and ranging). In this post you’ll learn more about LIDAR, its origins in the self-driving car space, and how it stacks against other sensors. Love!
LIDAR enables a self-driving car (or any robot) to observe the world with a few special super powers:
- Continuous three hundred sixty degrees of visibility – Imagine if your human eyes permitted you to see in all directions all of the time
- Insanely accurate depth information – Imagine if, instead of guessing, you could always know the precise distance (to an accuracy of ±2cm) of objects in relation to you
If you’ve seen a self-driving car before, you’ve very likely seen a LIDAR sensor. It’s typically the bulky box mounted on the roof that spins continuously, as seen below on Uber and Baidu self-driving cars.
One of the most popular LIDAR sensors on the market is the high-powered Velodyne HDL-64E, as seen below mounted on Homer.
How Does LIDAR Work?
How does a sensor that has three hundred sixty degree vision and accurate depth information work? Simply put: a LIDAR sensor continually fires off slats of laser light, and then measures how long it takes for the light to comeback to the sensor.
By firing off millions of slats of light per 2nd, the measurements from the LIDAR sensor enable a visualization of the world that is truly 3D. You can infer the exact measurement of any object around you (up to around
60m, depending on the sensor)
A Brief History of LIDAR
To understand why there’s so much support behind LIDAR today, it’s significant to look at other similar technologies which have similar goals.
Sonar
The original depth-sensing robot was the modest Bat (50 million years old!). A bat (or dolphin, among others) is able to perform some of the same capabilities as LIDAR using echolocation, otherwise known as Sonar (sound navigation and ranging). Instead of measuring light planks like LIDAR, Sonar measures distance using sound flaps.
After fifty million years of biological exclusivity, World War one advanced the timeline of the very first major deployment of man-made Sonar sensors, with the advent of submarine warfare. Sonar works excellently in water, where sound travels far better than light or radio swings (more on that in a 2nd). Sonar sensors are in active use on cars today, primarily in the form of parking sensors. These short-range (
5m) sensors enable a cheap way to know just how far that wall is behind your car. Sonar hasn’t been proven to work at the kinds of ranges a self-driving car requests (60m+).
Radar
Radar (radio direction and ranging), much like Sonar, was another technology developed during an infamous World War (WW2, this time). Instead of using light or sound flaps, it instead utilizes radio flaps to measure distance. We make use of a lot of Radar (using Delphi sensors) on Homer, and it’s a tried-and-tested method that can accurately detect and track objects as far as 200m away.
Radar has very little in terms of downside. It performs well in extreme weather conditions and is available at an affordable pricepoint. Radar is strongly used not only for detection of objects, but tracking them too (ex: understanding how swift a car is going and in which direction). Radar doesn’t necessarily give you granularity of LIDAR, but Radar and LIDAR are very complimentary, and it’s undoubtedly not either/or.
LIDAR
LIDAR was born in the 1960s, just after the advent of the laser. During the Apollo fifteen mission in 1971, astronauts mapped the surface of the moon, providing the public the very first peek of what LIDAR could do.
Before LIDAR was even considered for automotive and self-driving use, one of the popular use-cases of LIDAR was archeology. LIDAR provides a ton of value for mapping large-scale swaths of land, and both archeology and agriculture benefitted tremendously from it.
LIDAR technology is becoming increasingly popular, and its applications and uses are in many different fields.grindgis.com
It wasn’t until the 2000s when LIDAR was very first utilized on cars, where it was made famous by Stanley (and later, Junior) in the two thousand five Grand DARPA Challenge.
Stanley, the winner of the two thousand five Grand DARPA Challenge, made use of five SICK LIDAR sensors mounted on the roof, in addition to a military-grade GPS, gyroscopes, accelerometers and a forward-facing camera looking out 80m+. All of this was powered by six 1.6GHz Pentium Linux PCs sitting in the trunk.
The fundamental challenge with the SICK LIDARs (which powered a significant portion of the two thousand five challenge vehicles) is that each laser scan is essentially a cut made by a single plane, and so you had to be methodical in how you pointed them. Many teams mounted them on tilting stages, in order to use them to “sweep” a segment of space. In plain terms: SICK was a 2D LIDAR (a few slats of light in one direction) vs. the modern 3D LIDARs (tons of bars of light in all directions) we know today.
Come in Velodyne
Velodyne has long been the market leader in LIDAR, however they didn’t begin out life that way. Velodyne began life as an audio company in 1983, specializing in low-frequency sound and subwoofer technology. The subwoofers contained custom-built sensors, DSPs and custom-built DSP control algorithms. Velodyne became the LIDAR company we know today at the same time as Stanley’s debut. Velodyne founders David and Bruce Hall very first entered the two thousand four DARPA competition as Team DAD (Digital Audio Drive). For the 2nd race In 2005, David Hall invented and patented the 3D laser-based real-time system that laid the foundation for Velodyne’s current LIDAR products today. By the 3rd DARPA challenge in 2007, the majority of teams used this technology as the basis of their perception system. David Hall’s invention is now in the Smithsonian as a foundational breakthrough enabling autonomous driving.
The very first Velodyne LIDAR scanner was about thirty inches in diameter and weighed close to one hundred pounds. Choosing to commercialize the LIDAR scanner instead of contesting in subsequent challenge events, Velodyne was able to dramatically reduce the sensor’s size and weight while also improving spectacle. Velodyne’s HDL-64E LIDAR sensor was the primary means of terrain map construction and obstacle detection for all the top DARPA Urban Challenge teams in two thousand seven and used by five out of six of the ending teams, including the winning and second-place teams. Some teams relied exclusively on the LIDAR for the information about the environment used to navigate an autonomous vehicle through a simulated urban environment. – Wikipedia
Traction of LIDAR in Self-Driving Cars
Why did LIDAR take off with self-driving cars? In a word: mapping. LIDAR permits you to generate enormous 3D maps (its original application!), which you can then navigate the car or robot predictably within. By using a LIDAR to map and navigate an environment, you can know ahead of time the bounds of a lane, or that there is a stop sign or traffic light 500m ahead. This kind of predictability is exactly what a technology like self-driving cars requires, and has been a big reason for the progress over the last five years.
Object Detection
As LIDARs have become higher-resolution and operate at longer ranges, a fresh use-case has emerged in object detection and tracking. Not only can a LIDAR map enable you to know precisely where you are in the world and help you navigate it, but it can also detect and track obstacles like cars, pedestrians and according to Waymo, football helmets.
Modern LIDAR enables you to differentiate inbetween a person on a bike or a person walking, and even at what speed and which direction they are going in.
The combination of amazing navigation, predictability and high-resolution object tracking has meant that LIDAR is the key sensor in self-driving cars today, and it’s hard to see that dominance switching. Unless…
Camera-Powered Cars
There’s a number of startups out there approaching the problem of self-driving cars using purely cameras (and perhaps radar), with no LIDAR in view. Tesla is the fattest company of the bunch, and Elon Musk has repeatedly shoved the idea that if humans can perceive and navigate the world using just eyes, ears and a brain, then why can’t a car? I’m certain that this treatment will achieve amazing results, especially as other talented teams work toward this aim, including Comma and AutoX.
It’s significant to note that Tesla has an interesting constraint that may have factored in to their decision: scale. Tesla hopes to ship 500k cars a year very soon, and can’t wait for LIDAR to come down in cost (or be manufactured in volume) tomorrow, it needed to happen yesterday!
Tesla CEO Elon Musk held a press conference a duo of days ago to explain the Autopilot features included in the…9to5google.com
The Future of LIDAR
The industry is marching ahead with a real concentrate on: cost decrease and resolution and range increase.
Cost Decrease
Solid-state LIDAR opens up the potential of sub-$1k powerful LIDAR units, which today can cost as much as $80k a unit. LeddarTech are one of the leaders in this early market.
Here’s what Velodyne has to say about solid-state:
Solid state, immobilized sensors are driven by the idea that you want an embeddable sensor with the smallest size at the lowest possible cost. Naturally, that also means that you have a smaller field of view. Velodyne supports both immobile and surround view sensors. The immovable sensors are miniaturized to be embedded. From a cost standpoint, both contain lenses, lasers and detectors. The lowest cost system is actually via surround view sensors because rotation reuses the lens, lasers and detectors across the field of view, versus using extra sensors each containing individual lenses, lasers and detectors. This reuse is both the most economical, as well as the most powerful, as it reduces the error associated with merging different points of view in real-time — something that indeed counts when the vehicle is moving at speed.
Resolution and Range Increase
The hefty leap in the number of applications for LIDAR has brought with it a flood of talented founders and teams commencing companies in the space. Higher resolution output and enhanced tracking range (200m in some cases) will provide better object recognition and tracking, and are one of the key differentiators in sensors from startups like Luminar.
At Voyage, we’ve placed a bet on LIDAR. We love all the benefits that it brings, and believe the ecosystem will take care of bringing down the cost just in time for when we need to scale our autonomous taxi service. If you’re a LIDAR startup and want to test your sensors, we’d love to be one of your very first customers. Reach out on our website!
An Introduction to LIDAR: The Key Self-Driving Car Sensor
An Introduction to LIDAR: The Key Self-Driving Car Sensor
At Voyage we recently collective the news of Homer, our very first self-driving taxi. Homer is outfitted with a entire range of sensors to aid in understanding and navigating the world, key to which is LIDAR (brief for light detection and ranging). In this post you’ll learn more about LIDAR, its origins in the self-driving car space, and how it stacks against other sensors. Love!
LIDAR enables a self-driving car (or any robot) to observe the world with a few special super powers:
- Continuous three hundred sixty degrees of visibility – Imagine if your human eyes permitted you to see in all directions all of the time
- Insanely accurate depth information – Imagine if, instead of guessing, you could always know the precise distance (to an accuracy of ±2cm) of objects in relation to you
If you’ve seen a self-driving car before, you’ve most likely seen a LIDAR sensor. It’s typically the bulky box mounted on the roof that spins continuously, as seen below on Uber and Baidu self-driving cars.
One of the most popular LIDAR sensors on the market is the high-powered Velodyne HDL-64E, as seen below mounted on Homer.
How Does LIDAR Work?
How does a sensor that has three hundred sixty degree vision and accurate depth information work? Simply put: a LIDAR sensor continually fires off bars of laser light, and then measures how long it takes for the light to comeback to the sensor.
By firing off millions of planks of light per 2nd, the measurements from the LIDAR sensor enable a visualization of the world that is truly 3D. You can infer the exact measurement of any object around you (up to around
60m, depending on the sensor)
A Brief History of LIDAR
To understand why there’s so much support behind LIDAR today, it’s significant to look at other similar technologies which have similar goals.
Sonar
The original depth-sensing robot was the modest Bat (50 million years old!). A bat (or dolphin, among others) is able to perform some of the same capabilities as LIDAR using echolocation, otherwise known as Sonar (sound navigation and ranging). Instead of measuring light rafters like LIDAR, Sonar measures distance using sound swings.
After fifty million years of biological exclusivity, World War one advanced the timeline of the very first major deployment of man-made Sonar sensors, with the advent of submarine warfare. Sonar works excellently in water, where sound travels far better than light or radio swings (more on that in a 2nd). Sonar sensors are in active use on cars today, primarily in the form of parking sensors. These short-range (
5m) sensors enable a cheap way to know just how far that wall is behind your car. Sonar hasn’t been proven to work at the kinds of ranges a self-driving car requests (60m+).
Radar
Radar (radio direction and ranging), much like Sonar, was another technology developed during an infamous World War (WW2, this time). Instead of using light or sound flaps, it instead utilizes radio sways to measure distance. We make use of a lot of Radar (using Delphi sensors) on Homer, and it’s a tried-and-tested method that can accurately detect and track objects as far as 200m away.
Radar has very little in terms of downside. It performs well in extreme weather conditions and is available at an affordable pricepoint. Radar is strenuously used not only for detection of objects, but tracking them too (ex: understanding how prompt a car is going and in which direction). Radar doesn’t necessarily give you granularity of LIDAR, but Radar and LIDAR are very complimentary, and it’s undoubtedly not either/or.
LIDAR
LIDAR was born in the 1960s, just after the advent of the laser. During the Apollo fifteen mission in 1971, astronauts mapped the surface of the moon, providing the public the very first peek of what LIDAR could do.
Before LIDAR was even considered for automotive and self-driving use, one of the popular use-cases of LIDAR was archeology. LIDAR provides a ton of value for mapping large-scale swaths of land, and both archeology and agriculture benefitted tremendously from it.
LIDAR technology is becoming increasingly popular, and its applications and uses are in many different fields.grindgis.com
It wasn’t until the 2000s when LIDAR was very first utilized on cars, where it was made famous by Stanley (and later, Junior) in the two thousand five Grand DARPA Challenge.
Stanley, the winner of the two thousand five Grand DARPA Challenge, made use of five SICK LIDAR sensors mounted on the roof, in addition to a military-grade GPS, gyroscopes, accelerometers and a forward-facing camera looking out 80m+. All of this was powered by six 1.6GHz Pentium Linux PCs sitting in the trunk.
The fundamental challenge with the SICK LIDARs (which powered a significant portion of the two thousand five challenge vehicles) is that each laser scan is essentially a cut made by a single plane, and so you had to be methodical in how you pointed them. Many teams mounted them on tilting stages, in order to use them to “sweep” a segment of space. In elementary terms: SICK was a 2D LIDAR (a few bars of light in one direction) vs. the modern 3D LIDARs (tons of planks of light in all directions) we know today.
Come in Velodyne
Velodyne has long been the market leader in LIDAR, however they didn’t commence out life that way. Velodyne began life as an audio company in 1983, specializing in low-frequency sound and subwoofer technology. The subwoofers contained custom-built sensors, DSPs and custom-made DSP control algorithms. Velodyne became the LIDAR company we know today at the same time as Stanley’s debut. Velodyne founders David and Bruce Hall very first entered the two thousand four DARPA competition as Team DAD (Digital Audio Drive). For the 2nd race In 2005, David Hall invented and patented the 3D laser-based real-time system that laid the foundation for Velodyne’s current LIDAR products today. By the 3rd DARPA challenge in 2007, the majority of teams used this technology as the basis of their perception system. David Hall’s invention is now in the Smithsonian as a foundational breakthrough enabling autonomous driving.
The very first Velodyne LIDAR scanner was about thirty inches in diameter and weighed close to one hundred pounds. Choosing to commercialize the LIDAR scanner instead of contesting in subsequent challenge events, Velodyne was able to dramatically reduce the sensor’s size and weight while also improving spectacle. Velodyne’s HDL-64E LIDAR sensor was the primary means of terrain map construction and obstacle detection for all the top DARPA Urban Challenge teams in two thousand seven and used by five out of six of the ending teams, including the winning and second-place teams. Some teams relied exclusively on the LIDAR for the information about the environment used to navigate an autonomous vehicle through a simulated urban environment. – Wikipedia
Traction of LIDAR in Self-Driving Cars
Why did LIDAR take off with self-driving cars? In a word: mapping. LIDAR permits you to generate meaty 3D maps (its original application!), which you can then navigate the car or robot predictably within. By using a LIDAR to map and navigate an environment, you can know ahead of time the bounds of a lane, or that there is a stop sign or traffic light 500m ahead. This kind of predictability is exactly what a technology like self-driving cars requires, and has been a big reason for the progress over the last five years.
Object Detection
As LIDARs have become higher-resolution and operate at longer ranges, a fresh use-case has emerged in object detection and tracking. Not only can a LIDAR map enable you to know precisely where you are in the world and help you navigate it, but it can also detect and track obstacles like cars, pedestrians and according to Waymo, football helmets.
Modern LIDAR enables you to differentiate inbetween a person on a bike or a person walking, and even at what speed and which direction they are going in.
The combination of amazing navigation, predictability and high-resolution object tracking has meant that LIDAR is the key sensor in self-driving cars today, and it’s hard to see that predominance switching. Unless…
Camera-Powered Cars
There’s a number of startups out there approaching the problem of self-driving cars using purely cameras (and perhaps radar), with no LIDAR in glance. Tesla is the fattest company of the bunch, and Elon Musk has repeatedly shoved the idea that if humans can perceive and navigate the world using just eyes, ears and a brain, then why can’t a car? I’m certain that this treatment will achieve amazing results, especially as other talented teams work toward this purpose, including Comma and AutoX.
It’s significant to note that Tesla has an interesting constraint that may have factored in to their decision: scale. Tesla hopes to ship 500k cars a year very soon, and can’t wait for LIDAR to come down in cost (or be manufactured in volume) tomorrow, it needed to happen yesterday!
Tesla CEO Elon Musk held a press conference a duo of days ago to explain the Autopilot features included in the…9to5google.com
The Future of LIDAR
The industry is marching ahead with a real concentrate on: cost decrease and resolution and range increase.
Cost Decrease
Solid-state LIDAR opens up the potential of sub-$1k powerful LIDAR units, which today can cost as much as $80k a unit. LeddarTech are one of the leaders in this early market.
Here’s what Velodyne has to say about solid-state:
Solid state, immobilized sensors are driven by the idea that you want an embeddable sensor with the smallest size at the lowest possible cost. Naturally, that also means that you have a smaller field of view. Velodyne supports both motionless and surround view sensors. The immovable sensors are miniaturized to be embedded. From a cost standpoint, both contain lenses, lasers and detectors. The lowest cost system is actually via surround view sensors because rotation reuses the lens, lasers and detectors across the field of view, versus using extra sensors each containing individual lenses, lasers and detectors. This reuse is both the most economical, as well as the most powerful, as it reduces the error associated with merging different points of view in real-time — something that indeed counts when the vehicle is moving at speed.
Resolution and Range Increase
The massive leap in the number of applications for LIDAR has brought with it a flood of talented founders and teams kicking off companies in the space. Higher resolution output and enlargened tracking range (200m in some cases) will provide better object recognition and tracking, and are one of the key differentiators in sensors from startups like Luminar.
At Voyage, we’ve placed a bet on LIDAR. We love all the benefits that it brings, and believe the ecosystem will take care of bringing down the cost just in time for when we need to scale our autonomous taxi service. If you’re a LIDAR startup and want to test your sensors, we’d love to be one of your very first customers. Reach out on our website!
An Introduction to LIDAR: The Key Self-Driving Car Sensor
An Introduction to LIDAR: The Key Self-Driving Car Sensor
At Voyage we recently collective the news of Homer, our very first self-driving taxi. Homer is outfitted with a entire range of sensors to aid in understanding and navigating the world, key to which is LIDAR (brief for light detection and ranging). In this post you’ll learn more about LIDAR, its origins in the self-driving car space, and how it stacks against other sensors. Love!
LIDAR enables a self-driving car (or any robot) to observe the world with a few special super powers:
- Continuous three hundred sixty degrees of visibility – Imagine if your human eyes permitted you to see in all directions all of the time
- Insanely accurate depth information – Imagine if, instead of guessing, you could always know the precise distance (to an accuracy of ±2cm) of objects in relation to you
If you’ve seen a self-driving car before, you’ve most likely seen a LIDAR sensor. It’s typically the bulky box mounted on the roof that spins continuously, as seen below on Uber and Baidu self-driving cars.
One of the most popular LIDAR sensors on the market is the high-powered Velodyne HDL-64E, as seen below mounted on Homer.
How Does LIDAR Work?
How does a sensor that has three hundred sixty degree vision and accurate depth information work? Simply put: a LIDAR sensor continually fires off bars of laser light, and then measures how long it takes for the light to come back to the sensor.
By firing off millions of slats of light per 2nd, the measurements from the LIDAR sensor enable a visualization of the world that is truly 3D. You can infer the exact measurement of any object around you (up to around
60m, depending on the sensor)
A Brief History of LIDAR
To understand why there’s so much support behind LIDAR today, it’s significant to look at other similar technologies which have similar goals.
Sonar
The original depth-sensing robot was the discreet Bat (50 million years old!). A bat (or dolphin, among others) is able to perform some of the same capabilities as LIDAR using echolocation, otherwise known as Sonar (sound navigation and ranging). Instead of measuring light bars like LIDAR, Sonar measures distance using sound sways.
After fifty million years of biological exclusivity, World War one advanced the timeline of the very first major deployment of man-made Sonar sensors, with the advent of submarine warfare. Sonar works excellently in water, where sound travels far better than light or radio swings (more on that in a 2nd). Sonar sensors are in active use on cars today, primarily in the form of parking sensors. These short-range (
5m) sensors enable a cheap way to know just how far that wall is behind your car. Sonar hasn’t been proven to work at the kinds of ranges a self-driving car requests (60m+).
Radar
Radar (radio direction and ranging), much like Sonar, was another technology developed during an infamous World War (WW2, this time). Instead of using light or sound flaps, it instead utilizes radio sways to measure distance. We make use of a lot of Radar (using Delphi sensors) on Homer, and it’s a tried-and-tested method that can accurately detect and track objects as far as 200m away.
Radar has very little in terms of downside. It performs well in extreme weather conditions and is available at an affordable pricepoint. Radar is strongly used not only for detection of objects, but tracking them too (ex: understanding how quick a car is going and in which direction). Radar doesn’t necessarily give you granularity of LIDAR, but Radar and LIDAR are very complimentary, and it’s certainly not either/or.
LIDAR
LIDAR was born in the 1960s, just after the advent of the laser. During the Apollo fifteen mission in 1971, astronauts mapped the surface of the moon, providing the public the very first peek of what LIDAR could do.
Before LIDAR was even considered for automotive and self-driving use, one of the popular use-cases of LIDAR was archeology. LIDAR provides a ton of value for mapping large-scale swaths of land, and both archeology and agriculture benefitted tremendously from it.
LIDAR technology is becoming increasingly popular, and its applications and uses are in many different fields.grindgis.com
It wasn’t until the 2000s when LIDAR was very first utilized on cars, where it was made famous by Stanley (and later, Junior) in the two thousand five Grand DARPA Challenge.
Stanley, the winner of the two thousand five Grand DARPA Challenge, made use of five SICK LIDAR sensors mounted on the roof, in addition to a military-grade GPS, gyroscopes, accelerometers and a forward-facing camera looking out 80m+. All of this was powered by six 1.6GHz Pentium Linux PCs sitting in the trunk.
The fundamental challenge with the SICK LIDARs (which powered a significant portion of the two thousand five challenge vehicles) is that each laser scan is essentially a cut made by a single plane, and so you had to be methodical in how you pointed them. Many teams mounted them on tilting stages, in order to use them to “sweep” a segment of space. In ordinary terms: SICK was a 2D LIDAR (a few planks of light in one direction) vs. the modern 3D LIDARs (tons of slats of light in all directions) we know today.
Come in Velodyne
Velodyne has long been the market leader in LIDAR, however they didn’t commence out life that way. Velodyne began life as an audio company in 1983, specializing in low-frequency sound and subwoofer technology. The subwoofers contained custom-built sensors, DSPs and custom-built DSP control algorithms. Velodyne became the LIDAR company we know today at the same time as Stanley’s debut. Velodyne founders David and Bruce Hall very first entered the two thousand four DARPA competition as Team DAD (Digital Audio Drive). For the 2nd race In 2005, David Hall invented and patented the 3D laser-based real-time system that laid the foundation for Velodyne’s current LIDAR products today. By the 3rd DARPA challenge in 2007, the majority of teams used this technology as the basis of their perception system. David Hall’s invention is now in the Smithsonian as a foundational breakthrough enabling autonomous driving.
The very first Velodyne LIDAR scanner was about thirty inches in diameter and weighed close to one hundred pounds. Choosing to commercialize the LIDAR scanner instead of contesting in subsequent challenge events, Velodyne was able to dramatically reduce the sensor’s size and weight while also improving spectacle. Velodyne’s HDL-64E LIDAR sensor was the primary means of terrain map construction and obstacle detection for all the top DARPA Urban Challenge teams in two thousand seven and used by five out of six of the ending teams, including the winning and second-place teams. Some teams relied exclusively on the LIDAR for the information about the environment used to navigate an autonomous vehicle through a simulated urban environment. – Wikipedia
Traction of LIDAR in Self-Driving Cars
Why did LIDAR take off with self-driving cars? In a word: mapping. LIDAR permits you to generate thick 3D maps (its original application!), which you can then navigate the car or robot predictably within. By using a LIDAR to map and navigate an environment, you can know ahead of time the bounds of a lane, or that there is a stop sign or traffic light 500m ahead. This kind of predictability is exactly what a technology like self-driving cars requires, and has been a big reason for the progress over the last five years.
Object Detection
As LIDARs have become higher-resolution and operate at longer ranges, a fresh use-case has emerged in object detection and tracking. Not only can a LIDAR map enable you to know precisely where you are in the world and help you navigate it, but it can also detect and track obstacles like cars, pedestrians and according to Waymo, football helmets.
Modern LIDAR enables you to differentiate inbetween a person on a bike or a person walking, and even at what speed and which direction they are going in.
The combination of amazing navigation, predictability and high-resolution object tracking has meant that LIDAR is the key sensor in self-driving cars today, and it’s hard to see that predominance switching. Unless…
Camera-Powered Cars
There’s a number of startups out there approaching the problem of self-driving cars using purely cameras (and perhaps radar), with no LIDAR in view. Tesla is the fattest company of the bunch, and Elon Musk has repeatedly shoved the idea that if humans can perceive and navigate the world using just eyes, ears and a brain, then why can’t a car? I’m certain that this treatment will achieve amazing results, especially as other talented teams work toward this objective, including Comma and AutoX.
It’s significant to note that Tesla has an interesting constraint that may have factored in to their decision: scale. Tesla hopes to ship 500k cars a year very soon, and can’t wait for LIDAR to come down in cost (or be manufactured in volume) tomorrow, it needed to happen yesterday!
Tesla CEO Elon Musk held a press conference a duo of days ago to explain the Autopilot features included in the…9to5google.com
The Future of LIDAR
The industry is marching ahead with a real concentrate on: cost decrease and resolution and range increase.
Cost Decrease
Solid-state LIDAR opens up the potential of sub-$1k powerful LIDAR units, which today can cost as much as $80k a unit. LeddarTech are one of the leaders in this early market.
Here’s what Velodyne has to say about solid-state:
Solid state, immobilized sensors are driven by the idea that you want an embeddable sensor with the smallest size at the lowest possible cost. Naturally, that also means that you have a smaller field of view. Velodyne supports both motionless and surround view sensors. The motionless sensors are miniaturized to be embedded. From a cost standpoint, both contain lenses, lasers and detectors. The lowest cost system is actually via surround view sensors because rotation reuses the lens, lasers and detectors across the field of view, versus using extra sensors each containing individual lenses, lasers and detectors. This reuse is both the most economical, as well as the most powerful, as it reduces the error associated with merging different points of view in real-time — something that truly counts when the vehicle is moving at speed.
Resolution and Range Increase
The large hop in the number of applications for LIDAR has brought with it a flood of talented founders and teams beginning companies in the space. Higher resolution output and enlargened tracking range (200m in some cases) will provide better object recognition and tracking, and are one of the key differentiators in sensors from startups like Luminar.
At Voyage, we’ve placed a bet on LIDAR. We love all the benefits that it brings, and believe the ecosystem will take care of bringing down the cost just in time for when we need to scale our autonomous taxi service. If you’re a LIDAR startup and want to test your sensors, we’d love to be one of your very first customers. Reach out on our website!
An Introduction to LIDAR: The Key Self-Driving Car Sensor
An Introduction to LIDAR: The Key Self-Driving Car Sensor
At Voyage we recently collective the news of Homer, our very first self-driving taxi. Homer is outfitted with a entire range of sensors to aid in understanding and navigating the world, key to which is LIDAR (brief for light detection and ranging). In this post you’ll learn more about LIDAR, its origins in the self-driving car space, and how it stacks against other sensors. Love!
LIDAR enables a self-driving car (or any robot) to observe the world with a few special super powers:
- Continuous three hundred sixty degrees of visibility – Imagine if your human eyes permitted you to see in all directions all of the time
- Insanely accurate depth information – Imagine if, instead of guessing, you could always know the precise distance (to an accuracy of ±2cm) of objects in relation to you
If you’ve seen a self-driving car before, you’ve most likely seen a LIDAR sensor. It’s typically the bulky box mounted on the roof that spins continuously, as seen below on Uber and Baidu self-driving cars.
One of the most popular LIDAR sensors on the market is the high-powered Velodyne HDL-64E, as seen below mounted on Homer.
How Does LIDAR Work?
How does a sensor that has three hundred sixty degree vision and accurate depth information work? Simply put: a LIDAR sensor continually fires off planks of laser light, and then measures how long it takes for the light to come back to the sensor.
By firing off millions of planks of light per 2nd, the measurements from the LIDAR sensor enable a visualization of the world that is truly 3D. You can infer the exact measurement of any object around you (up to around
60m, depending on the sensor)
A Brief History of LIDAR
To understand why there’s so much support behind LIDAR today, it’s significant to look at other similar technologies which have similar goals.
Sonar
The original depth-sensing robot was the discreet Bat (50 million years old!). A bat (or dolphin, among others) is able to perform some of the same capabilities as LIDAR using echolocation, otherwise known as Sonar (sound navigation and ranging). Instead of measuring light planks like LIDAR, Sonar measures distance using sound sways.
After fifty million years of biological exclusivity, World War one advanced the timeline of the very first major deployment of man-made Sonar sensors, with the advent of submarine warfare. Sonar works excellently in water, where sound travels far better than light or radio sways (more on that in a 2nd). Sonar sensors are in active use on cars today, primarily in the form of parking sensors. These short-range (
5m) sensors enable a cheap way to know just how far that wall is behind your car. Sonar hasn’t been proven to work at the kinds of ranges a self-driving car requests (60m+).
Radar
Radar (radio direction and ranging), much like Sonar, was another technology developed during an infamous World War (WW2, this time). Instead of using light or sound swings, it instead utilizes radio swings to measure distance. We make use of a lot of Radar (using Delphi sensors) on Homer, and it’s a tried-and-tested method that can accurately detect and track objects as far as 200m away.
Radar has very little in terms of downside. It performs well in extreme weather conditions and is available at an affordable pricepoint. Radar is intensely used not only for detection of objects, but tracking them too (ex: understanding how prompt a car is going and in which direction). Radar doesn’t necessarily give you granularity of LIDAR, but Radar and LIDAR are very complimentary, and it’s certainly not either/or.
LIDAR
LIDAR was born in the 1960s, just after the advent of the laser. During the Apollo fifteen mission in 1971, astronauts mapped the surface of the moon, providing the public the very first peek of what LIDAR could do.
Before LIDAR was even considered for automotive and self-driving use, one of the popular use-cases of LIDAR was archeology. LIDAR provides a ton of value for mapping large-scale swaths of land, and both archeology and agriculture benefitted tremendously from it.
LIDAR technology is becoming increasingly popular, and its applications and uses are in many different fields.grindgis.com
It wasn’t until the 2000s when LIDAR was very first utilized on cars, where it was made famous by Stanley (and later, Junior) in the two thousand five Grand DARPA Challenge.
Stanley, the winner of the two thousand five Grand DARPA Challenge, made use of five SICK LIDAR sensors mounted on the roof, in addition to a military-grade GPS, gyroscopes, accelerometers and a forward-facing camera looking out 80m+. All of this was powered by six 1.6GHz Pentium Linux PCs sitting in the trunk.
The fundamental challenge with the SICK LIDARs (which powered a significant portion of the two thousand five challenge vehicles) is that each laser scan is essentially a cut made by a single plane, and so you had to be methodical in how you pointed them. Many teams mounted them on tilting stages, in order to use them to “sweep” a segment of space. In plain terms: SICK was a 2D LIDAR (a few bars of light in one direction) vs. the modern 3D LIDARs (tons of planks of light in all directions) we know today.
Inject Velodyne
Velodyne has long been the market leader in LIDAR, however they didn’t embark out life that way. Velodyne began life as an audio company in 1983, specializing in low-frequency sound and subwoofer technology. The subwoofers contained custom-made sensors, DSPs and custom-built DSP control algorithms. Velodyne became the LIDAR company we know today at the same time as Stanley’s debut. Velodyne founders David and Bruce Hall very first entered the two thousand four DARPA competition as Team DAD (Digital Audio Drive). For the 2nd race In 2005, David Hall invented and patented the 3D laser-based real-time system that laid the foundation for Velodyne’s current LIDAR products today. By the 3rd DARPA challenge in 2007, the majority of teams used this technology as the basis of their perception system. David Hall’s invention is now in the Smithsonian as a foundational breakthrough enabling autonomous driving.
The very first Velodyne LIDAR scanner was about thirty inches in diameter and weighed close to one hundred pounds. Choosing to commercialize the LIDAR scanner instead of challenging in subsequent challenge events, Velodyne was able to dramatically reduce the sensor’s size and weight while also improving spectacle. Velodyne’s HDL-64E LIDAR sensor was the primary means of terrain map construction and obstacle detection for all the top DARPA Urban Challenge teams in two thousand seven and used by five out of six of the completing teams, including the winning and second-place teams. Some teams relied exclusively on the LIDAR for the information about the environment used to navigate an autonomous vehicle through a simulated urban environment. – Wikipedia
Traction of LIDAR in Self-Driving Cars
Why did LIDAR take off with self-driving cars? In a word: mapping. LIDAR permits you to generate gigantic 3D maps (its original application!), which you can then navigate the car or robot predictably within. By using a LIDAR to map and navigate an environment, you can know ahead of time the bounds of a lane, or that there is a stop sign or traffic light 500m ahead. This kind of predictability is exactly what a technology like self-driving cars requires, and has been a big reason for the progress over the last five years.
Object Detection
As LIDARs have become higher-resolution and operate at longer ranges, a fresh use-case has emerged in object detection and tracking. Not only can a LIDAR map enable you to know precisely where you are in the world and help you navigate it, but it can also detect and track obstacles like cars, pedestrians and according to Waymo, football helmets.
Modern LIDAR enables you to differentiate inbetween a person on a bike or a person walking, and even at what speed and which direction they are going in.
The combination of amazing navigation, predictability and high-resolution object tracking has meant that LIDAR is the key sensor in self-driving cars today, and it’s hard to see that dominance switching. Unless…
Camera-Powered Cars
There’s a number of startups out there approaching the problem of self-driving cars using purely cameras (and perhaps radar), with no LIDAR in look. Tesla is the fattest company of the bunch, and Elon Musk has repeatedly shoved the idea that if humans can perceive and navigate the world using just eyes, ears and a brain, then why can’t a car? I’m certain that this treatment will achieve amazing results, especially as other talented teams work toward this purpose, including Comma and AutoX.
It’s significant to note that Tesla has an interesting constraint that may have factored in to their decision: scale. Tesla hopes to ship 500k cars a year very soon, and can’t wait for LIDAR to come down in cost (or be manufactured in volume) tomorrow, it needed to happen yesterday!
Tesla CEO Elon Musk held a press conference a duo of days ago to explain the Autopilot features included in the…9to5google.com
The Future of LIDAR
The industry is marching ahead with a real concentrate on: cost decrease and resolution and range increase.
Cost Decrease
Solid-state LIDAR opens up the potential of sub-$1k powerful LIDAR units, which today can cost as much as $80k a unit. LeddarTech are one of the leaders in this early market.
Here’s what Velodyne has to say about solid-state:
Solid state, immovable sensors are driven by the idea that you want an embeddable sensor with the smallest size at the lowest possible cost. Naturally, that also means that you have a smaller field of view. Velodyne supports both motionless and surround view sensors. The immovable sensors are miniaturized to be embedded. From a cost standpoint, both contain lenses, lasers and detectors. The lowest cost system is actually via surround view sensors because rotation reuses the lens, lasers and detectors across the field of view, versus using extra sensors each containing individual lenses, lasers and detectors. This reuse is both the most economical, as well as the most powerful, as it reduces the error associated with merging different points of view in real-time — something that truly counts when the vehicle is moving at speed.
Resolution and Range Increase
The massive leap in the number of applications for LIDAR has brought with it a flood of talented founders and teams beginning companies in the space. Higher resolution output and enlargened tracking range (200m in some cases) will provide better object recognition and tracking, and are one of the key differentiators in sensors from startups like Luminar.
At Voyage, we’ve placed a bet on LIDAR. We love all the benefits that it brings, and believe the ecosystem will take care of bringing down the cost just in time for when we need to scale our autonomous taxi service. If you’re a LIDAR startup and want to test your sensors, we’d love to be one of your very first customers. Reach out on our website!
An Introduction to LIDAR: The Key Self-Driving Car Sensor
An Introduction to LIDAR: The Key Self-Driving Car Sensor
At Voyage we recently collective the news of Homer, our very first self-driving taxi. Homer is outfitted with a entire range of sensors to aid in understanding and navigating the world, key to which is LIDAR (brief for light detection and ranging). In this post you’ll learn more about LIDAR, its origins in the self-driving car space, and how it stacks against other sensors. Love!
LIDAR enables a self-driving car (or any robot) to observe the world with a few special super powers:
- Continuous three hundred sixty degrees of visibility – Imagine if your human eyes permitted you to see in all directions all of the time
- Insanely accurate depth information – Imagine if, instead of guessing, you could always know the precise distance (to an accuracy of ±2cm) of objects in relation to you
If you’ve seen a self-driving car before, you’ve very likely seen a LIDAR sensor. It’s typically the bulky box mounted on the roof that spins continuously, as seen below on Uber and Baidu self-driving cars.
One of the most popular LIDAR sensors on the market is the high-powered Velodyne HDL-64E, as seen below mounted on Homer.
How Does LIDAR Work?
How does a sensor that has three hundred sixty degree vision and accurate depth information work? Simply put: a LIDAR sensor continually fires off rafters of laser light, and then measures how long it takes for the light to comeback to the sensor.
By firing off millions of slats of light per 2nd, the measurements from the LIDAR sensor enable a visualization of the world that is truly 3D. You can infer the exact measurement of any object around you (up to around
60m, depending on the sensor)
A Brief History of LIDAR
To understand why there’s so much support behind LIDAR today, it’s significant to look at other similar technologies which have similar goals.
Sonar
The original depth-sensing robot was the modest Bat (50 million years old!). A bat (or dolphin, among others) is able to perform some of the same capabilities as LIDAR using echolocation, otherwise known as Sonar (sound navigation and ranging). Instead of measuring light planks like LIDAR, Sonar measures distance using sound swings.
After fifty million years of biological exclusivity, World War one advanced the timeline of the very first major deployment of man-made Sonar sensors, with the advent of submarine warfare. Sonar works excellently in water, where sound travels far better than light or radio flaps (more on that in a 2nd). Sonar sensors are in active use on cars today, primarily in the form of parking sensors. These short-range (
5m) sensors enable a cheap way to know just how far that wall is behind your car. Sonar hasn’t been proven to work at the kinds of ranges a self-driving car requests (60m+).
Radar
Radar (radio direction and ranging), much like Sonar, was another technology developed during an infamous World War (WW2, this time). Instead of using light or sound swings, it instead utilizes radio flaps to measure distance. We make use of a lot of Radar (using Delphi sensors) on Homer, and it’s a tried-and-tested method that can accurately detect and track objects as far as 200m away.
Radar has very little in terms of downside. It performs well in extreme weather conditions and is available at an affordable pricepoint. Radar is strongly used not only for detection of objects, but tracking them too (ex: understanding how quick a car is going and in which direction). Radar doesn’t necessarily give you granularity of LIDAR, but Radar and LIDAR are very complimentary, and it’s certainly not either/or.
LIDAR
LIDAR was born in the 1960s, just after the advent of the laser. During the Apollo fifteen mission in 1971, astronauts mapped the surface of the moon, providing the public the very first peek of what LIDAR could do.
Before LIDAR was even considered for automotive and self-driving use, one of the popular use-cases of LIDAR was archeology. LIDAR provides a ton of value for mapping large-scale swaths of land, and both archeology and agriculture benefitted tremendously from it.
LIDAR technology is becoming increasingly popular, and its applications and uses are in many different fields.grindgis.com
It wasn’t until the 2000s when LIDAR was very first utilized on cars, where it was made famous by Stanley (and later, Junior) in the two thousand five Grand DARPA Challenge.
Stanley, the winner of the two thousand five Grand DARPA Challenge, made use of five SICK LIDAR sensors mounted on the roof, in addition to a military-grade GPS, gyroscopes, accelerometers and a forward-facing camera looking out 80m+. All of this was powered by six 1.6GHz Pentium Linux PCs sitting in the trunk.
The fundamental challenge with the SICK LIDARs (which powered a significant portion of the two thousand five challenge vehicles) is that each laser scan is essentially a cut made by a single plane, and so you had to be methodical in how you pointed them. Many teams mounted them on tilting stages, in order to use them to “sweep” a segment of space. In elementary terms: SICK was a 2D LIDAR (a few planks of light in one direction) vs. the modern 3D LIDARs (tons of planks of light in all directions) we know today.
Inject Velodyne
Velodyne has long been the market leader in LIDAR, however they didn’t begin out life that way. Velodyne began life as an audio company in 1983, specializing in low-frequency sound and subwoofer technology. The subwoofers contained custom-made sensors, DSPs and custom-made DSP control algorithms. Velodyne became the LIDAR company we know today at the same time as Stanley’s debut. Velodyne founders David and Bruce Hall very first entered the two thousand four DARPA competition as Team DAD (Digital Audio Drive). For the 2nd race In 2005, David Hall invented and patented the 3D laser-based real-time system that laid the foundation for Velodyne’s current LIDAR products today. By the 3rd DARPA challenge in 2007, the majority of teams used this technology as the basis of their perception system. David Hall’s invention is now in the Smithsonian as a foundational breakthrough enabling autonomous driving.
The very first Velodyne LIDAR scanner was about thirty inches in diameter and weighed close to one hundred pounds. Choosing to commercialize the LIDAR scanner instead of challenging in subsequent challenge events, Velodyne was able to dramatically reduce the sensor’s size and weight while also improving spectacle. Velodyne’s HDL-64E LIDAR sensor was the primary means of terrain map construction and obstacle detection for all the top DARPA Urban Challenge teams in two thousand seven and used by five out of six of the ending teams, including the winning and second-place teams. Some teams relied exclusively on the LIDAR for the information about the environment used to navigate an autonomous vehicle through a simulated urban environment. – Wikipedia
Traction of LIDAR in Self-Driving Cars
Why did LIDAR take off with self-driving cars? In a word: mapping. LIDAR permits you to generate giant 3D maps (its original application!), which you can then navigate the car or robot predictably within. By using a LIDAR to map and navigate an environment, you can know ahead of time the bounds of a lane, or that there is a stop sign or traffic light 500m ahead. This kind of predictability is exactly what a technology like self-driving cars requires, and has been a big reason for the progress over the last five years.
Object Detection
As LIDARs have become higher-resolution and operate at longer ranges, a fresh use-case has emerged in object detection and tracking. Not only can a LIDAR map enable you to know precisely where you are in the world and help you navigate it, but it can also detect and track obstacles like cars, pedestrians and according to Waymo, football helmets.
Modern LIDAR enables you to differentiate inbetween a person on a bike or a person walking, and even at what speed and which direction they are going in.
The combination of amazing navigation, predictability and high-resolution object tracking has meant that LIDAR is the key sensor in self-driving cars today, and it’s hard to see that predominance switching. Unless…
Camera-Powered Cars
There’s a number of startups out there approaching the problem of self-driving cars using purely cameras (and perhaps radar), with no LIDAR in look. Tesla is the fattest company of the bunch, and Elon Musk has repeatedly shoved the idea that if humans can perceive and navigate the world using just eyes, ears and a brain, then why can’t a car? I’m certain that this treatment will achieve amazing results, especially as other talented teams work toward this aim, including Comma and AutoX.
It’s significant to note that Tesla has an interesting constraint that may have factored in to their decision: scale. Tesla hopes to ship 500k cars a year very soon, and can’t wait for LIDAR to come down in cost (or be manufactured in volume) tomorrow, it needed to happen yesterday!
Tesla CEO Elon Musk held a press conference a duo of days ago to explain the Autopilot features included in the…9to5google.com
The Future of LIDAR
The industry is marching ahead with a real concentrate on: cost decrease and resolution and range increase.
Cost Decrease
Solid-state LIDAR opens up the potential of sub-$1k powerful LIDAR units, which today can cost as much as $80k a unit. LeddarTech are one of the leaders in this early market.
Here’s what Velodyne has to say about solid-state:
Solid state, immobilized sensors are driven by the idea that you want an embeddable sensor with the smallest size at the lowest possible cost. Naturally, that also means that you have a smaller field of view. Velodyne supports both stationary and surround view sensors. The immovable sensors are miniaturized to be embedded. From a cost standpoint, both contain lenses, lasers and detectors. The lowest cost system is actually via surround view sensors because rotation reuses the lens, lasers and detectors across the field of view, versus using extra sensors each containing individual lenses, lasers and detectors. This reuse is both the most economical, as well as the most powerful, as it reduces the error associated with merging different points of view in real-time — something that truly counts when the vehicle is moving at speed.
Resolution and Range Increase
The big leap in the number of applications for LIDAR has brought with it a flood of talented founders and teams embarking companies in the space. Higher resolution output and enlargened tracking range (200m in some cases) will provide better object recognition and tracking, and are one of the key differentiators in sensors from startups like Luminar.
At Voyage, we’ve placed a bet on LIDAR. We love all the benefits that it brings, and believe the ecosystem will take care of bringing down the cost just in time for when we need to scale our autonomous taxi service. If you’re a LIDAR startup and want to test your sensors, we’d love to be one of your very first customers. Reach out on our website!