8 ms·

You get sparse point cloud from LIDAR sensors, not accurate 3D maps. This is the main reason why some people think LIDAR may not work well (mostly, only comma.a

by liuliu 5y ago

You get sparse point cloud from LIDAR sensors, not accurate 3D maps. This is the main reason why some people think LIDAR may not work well (mostly, only comma.ai and Tesla folks). Vision can also get you 3D maps, either in active manner (IR floodlight or structured lighting) or not. I will reserve my judgement until see more from either side.

bluepanda928752 5y ago
LIDAR is valuable as a safety feature, i.e. it can (unlike radars or cameras) reliably see (at least in clear weather) if there's anything in the car's path warranting evasion/braking maneuvers. In particular it's important that the LIDAR is dumb, i.e. its failure mores are predictable
- liuliu 5y ago
  LIDAR operates by timing how a photon reflects from a surface, it doesn't guarantee see everything. As you stated already, it cannot see in snow or rain. I am actually on sensor-fusion side, but I don't think we should jump to the conclusion LIDAR is the best 3D mapping method. For one example, a truck has a breaking distance of 600ft, either Velodyne or OS1 LIDAR has range less than that.
  - bluepanda928752 5y ago
    I agree about LIDARs not being the best general 3D mapping method, my point was mostly about using it as a dumb physics-based safety system. For autonomous trucks, limited LIDAR range could be mitigated by reducing speed accordingly and/or employing more powerful (or SWIR) LIDARs since trucks are more expensive and there could be a bigger budget for sensors
ra7 5y ago
> You get sparse point cloud from LIDAR sensors Take a look at how dense the point cloud from Waymo's 5th gen LIDAR is: https://www.youtube.com/watch?v=COgEQuqTAug&t=11599s https://www.youtube.com/watch?v=COgEQuqTAug&t=11599s. They just talked about this a few days ago.
- blueblisters 5y ago
  Any idea why this video went private?
  - ra7 5y ago
    No idea, but here's another video of a different Waymo presentation showing the same point cloud: https://www.youtube.com/watch?v=uOLLrZzljs8&t=5420s https://www.youtube.com/watch?v=uOLLrZzljs8&t=5420s
  - jowday 5y ago
    They're cutting up the original 3 hour long Livestream into separate videos for each presentation.
bobsomers 5y ago
This framing is a common error in the debate. It's not cameras or lidar, it's cameras or cameras + lidar + radar. Nobody is driving on lidar alone. Many others actually have more cameras and are doing substantially more vision than Tesla is, they're just fusing lidar and radar perception with their vision pipeline. It gives you a more robust view of the world than using a single sensor modality.
- darknavi 5y ago
  One of the arguments made in this talk is which sensor to trust when there is a disagreement. I'd love to see the scenarios where this happens with three types of sensors and the logic they use to choose the "right" one.
  - judge2020 5y ago
    That's a problem for deciding which system drives the thing (eg. do you use cameras for detecting oncoming cars and objects or lidar/radar), but if there are major disagreements between them it would make most sense to disengage the autonomous part and ask the human driver to take over, much like current TSLA cars do. Obviously not possible if you want level 4 self driving, but it can be good enough for level 3.
  - bobsomers 5y ago
    You can't avoid this problem by only having a single sensor modality, because you still have to decide if you trust your sensor data or not. In effect, you actually made the problem harder because you have no other information from any other sensor modalities to help understand the world around you. In effect, they solved the disagreement problem by sticking their head in the sand and pretending their sensors are never wrong.
    - jiofih 5y ago
      You’re intent on oversimplifying the issue to support your view, but this comment makes no sense. Of course you can avoid sensor fusion issues by not doing sensor fusion. Regardless of how many sources you have, you still have to decide if you trust the data, having less sensors doesn’t change that. And having more sensors means you have to decide which input to trust.
jowday 5y ago
Point clouds from the next generation of LIDAR are looking less and less sparse. https://youtu.be/COgEQuqTAug?t=11601 https://youtu.be/COgEQuqTAug?t=11601
- lefrancais 5y ago
  This video is private, I cannot access.

by liuliu 5y ago

bluepanda928752 5y ago

LIDAR is valuable as a safety feature, i.e. it can (unlike radars or cameras) reliably see (at least in clear weather) if there's anything in the car's path warranting evasion/braking maneuvers. In particular it's important that the LIDAR is dumb, i.e. its failure mores are predictable

liuliu 5y ago
LIDAR operates by timing how a photon reflects from a surface, it doesn't guarantee see everything. As you stated already, it cannot see in snow or rain. I am actually on sensor-fusion side, but I don't think we should jump to the conclusion LIDAR is the best 3D mapping method. For one example, a truck has a breaking distance of 600ft, either Velodyne or OS1 LIDAR has range less than that.
- bluepanda928752 5y ago
  I agree about LIDARs not being the best general 3D mapping method, my point was mostly about using it as a dumb physics-based safety system. For autonomous trucks, limited LIDAR range could be mitigated by reducing speed accordingly and/or employing more powerful (or SWIR) LIDARs since trucks are more expensive and there could be a bigger budget for sensors

ra7 5y ago

> You get sparse point cloud from LIDAR sensors Take a look at how dense the point cloud from Waymo's 5th gen LIDAR is: https://www.youtube.com/watch?v=COgEQuqTAug&t=11599s https://www.youtube.com/watch?v=COgEQuqTAug&t=11599s. They just talked about this a few days ago.

blueblisters 5y ago
Any idea why this video went private?
- ra7 5y ago
  No idea, but here's another video of a different Waymo presentation showing the same point cloud: https://www.youtube.com/watch?v=uOLLrZzljs8&t=5420s https://www.youtube.com/watch?v=uOLLrZzljs8&t=5420s
- jowday 5y ago
  They're cutting up the original 3 hour long Livestream into separate videos for each presentation.

bobsomers 5y ago

This framing is a common error in the debate. It's not cameras or lidar, it's cameras or cameras + lidar + radar. Nobody is driving on lidar alone. Many others actually have more cameras and are doing substantially more vision than Tesla is, they're just fusing lidar and radar perception with their vision pipeline. It gives you a more robust view of the world than using a single sensor modality.

darknavi 5y ago
One of the arguments made in this talk is which sensor to trust when there is a disagreement. I'd love to see the scenarios where this happens with three types of sensors and the logic they use to choose the "right" one.
- judge2020 5y ago
  That's a problem for deciding which system drives the thing (eg. do you use cameras for detecting oncoming cars and objects or lidar/radar), but if there are major disagreements between them it would make most sense to disengage the autonomous part and ask the human driver to take over, much like current TSLA cars do. Obviously not possible if you want level 4 self driving, but it can be good enough for level 3.
- bobsomers 5y ago
  You can't avoid this problem by only having a single sensor modality, because you still have to decide if you trust your sensor data or not. In effect, you actually made the problem harder because you have no other information from any other sensor modalities to help understand the world around you. In effect, they solved the disagreement problem by sticking their head in the sand and pretending their sensors are never wrong.
  - jiofih 5y ago
    You’re intent on oversimplifying the issue to support your view, but this comment makes no sense. Of course you can avoid sensor fusion issues by not doing sensor fusion. Regardless of how many sources you have, you still have to decide if you trust the data, having less sensors doesn’t change that. And having more sensors means you have to decide which input to trust.

jowday 5y ago

Point clouds from the next generation of LIDAR are looking less and less sparse. https://youtu.be/COgEQuqTAug?t=11601 https://youtu.be/COgEQuqTAug?t=11601

lefrancais 5y ago
This video is private, I cannot access.