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You make lidar sound great, much better than I've heard. I thought lidar was quite sensitive to dust, rain, snow, and fog. This is particularly worrisome beca

by sliken 8y ago

You make lidar sound great, much better than I've heard. I thought lidar was quite sensitive to dust, rain, snow, and fog. This is particularly worrisome because lidar samples at a MUCH lower rate than a camera (from 30M samples per cheap camera to 300,000 or so for an expensive lidar). While lidar is pretty impressive at short ranges, what about at 2 seconds away @ 65 MPH? Will it detect a deaccelerating car faster than a camera that can detect a brake light? Will it be better at detecting perpendicular cars vs parked cars at that range? Will weather cause the lidar to decay in similar ways to human eyes?

KaiserPro 8y ago
>what about at 2 seconds away @ 65 MPH? 65 MPH is about 30m/s, your average lidar should be good upto about 200 meters, which is about 6 seconds at 65 mph. > Will it detect a de-accelerating car faster than a camera that can detect a brake light? Now this is an interesting question. yes and no. A lidar will on its own will not give you object recognition. It will tell you that a reflective surface of size _n_ is directly in you proposed path, and that since last scan its got closer. From that you can make a very accurate obsticle map. I don't think that running a car soely on Lidar is actually all that feasible or a clever idea. Not without a boat load of mapping and processing to create a machine readable semantic map first. Having a camera array _as well as_ lidar is a very good idea, as it can provide blended information from the lidar to the semantic map being generated by the camera and radar. Your example of brake lights is good, as it provides a cue as to what is likely to happen. It also means that the high latency of a visual processing system is less of a problem, as the model can be updated by the lidar. Camera picks up a car, the model attaches it to the pointcloud it thinks is the car from the lidar/radar. When the lidar/radar updates it can create a prediction of where the visual system should look. > detecting perpendicular cars vs parked cars at that range? You don't have to. lidar gives you a point cloud, those points can be roughly translated into hard surfaces. if a hard surface is in your path or predicted path, take action until it isn't. Dealing with pointclouds for object avoidance is much much more computationally simpler, than having to infer 3d from either structure from motion, stereo or both. https://github.com/raulmur/ORB_SLAM2 https://github.com/raulmur/ORB_SLAM2 if you look at this slam system, the pointcloud it generates is very sparse compared to a lidar: https://youtu.be/W3ELziPYn5k?t=13 https://youtu.be/W3ELziPYn5k?t=13 But as I said before, you need other sensors to get other data/corroborate world model. >lidar was quite sensitive to dust, rain, snow, and fog. It is indeed, like any other sensor > Will weather cause the lidar to decay in similar ways to human eyes? Most lidars operate in the far infrared. so will handle decay differently. Depending on frequency it'll either be sensitive to moisture, or not at all.

by sliken 8y ago

KaiserPro 8y ago

>what about at 2 seconds away @ 65 MPH? 65 MPH is about 30m/s, your average lidar should be good upto about 200 meters, which is about 6 seconds at 65 mph. > Will it detect a de-accelerating car faster than a camera that can detect a brake light? Now this is an interesting question. yes and no. A lidar will on its own will not give you object recognition. It will tell you that a reflective surface of size _n_ is directly in you proposed path, and that since last scan its got closer. From that you can make a very accurate obsticle map. I don't think that running a car soely on Lidar is actually all that feasible or a clever idea. Not without a boat load of mapping and processing to create a machine readable semantic map first. Having a camera array _as well as_ lidar is a very good idea, as it can provide blended information from the lidar to the semantic map being generated by the camera and radar. Your example of brake lights is good, as it provides a cue as to what is likely to happen. It also means that the high latency of a visual processing system is less of a problem, as the model can be updated by the lidar. Camera picks up a car, the model attaches it to the pointcloud it thinks is the car from the lidar/radar. When the lidar/radar updates it can create a prediction of where the visual system should look. > detecting perpendicular cars vs parked cars at that range? You don't have to. lidar gives you a point cloud, those points can be roughly translated into hard surfaces. if a hard surface is in your path or predicted path, take action until it isn't. Dealing with pointclouds for object avoidance is much much more computationally simpler, than having to infer 3d from either structure from motion, stereo or both. https://github.com/raulmur/ORB_SLAM2 https://github.com/raulmur/ORB_SLAM2 if you look at this slam system, the pointcloud it generates is very sparse compared to a lidar: https://youtu.be/W3ELziPYn5k?t=13 https://youtu.be/W3ELziPYn5k?t=13 But as I said before, you need other sensors to get other data/corroborate world model. >lidar was quite sensitive to dust, rain, snow, and fog. It is indeed, like any other sensor > Will weather cause the lidar to decay in similar ways to human eyes? Most lidars operate in the far infrared. so will handle decay differently. Depending on frequency it'll either be sensitive to moisture, or not at all.