| First, the optics are not wideband. It is only collecting narrow band NIR light. Saturation is not a problem. The CEO of Ouster explains this in a reddit comment [0]: > We are not sacrificing lidar performance by adding ambient imaging functionality. The lidar subsystem has a short integration time that avoids saturation, and if anything our approach outperforms other lidars. > As proof, the example videos linked to in the article show raw unedited point cloud data with the lidar operating in extremely sunny environments with plenty of specular reflectors. You can see lens flare in the ambient imagery as any camera would exhibit, but the lidar signal and range data are unaffected. In addition, if you point a velodyne directly at the sun its false positive rate increases significantly while our sensor's FPR does not. No lidar will return the distance to the sun so the only thing that matters is FPR in this scenario. > We've independently verified the OS-1's range performance with customers under all levels of solar exposure and I guarantee you can't get a smaller, cheaper lidar with even close to this combination of resolution and performance. If you have any doubts, download the raw pcap files from our github page and play them back yourself. We stand behind our data, our pricing, and our spec! Second, even if you do plan on adding extra cameras, the extrinsic calibration between camera and lidar may become easier if you have good quality ambient light measurement from the lidar. For example Jesse Levinson, cofounder of Zoox, computes extrinsic calibration between camera and Velodyne lidar by assuming that depth discontinuities are correlated with visual features [1]. But obviously the correlation between 850 nm images and visible light images would be way better. [0] https://www.reddit.com/r/SelfDrivingCars/comments/9c60pe/the... [1] http://www.roboticsproceedings.org/rss09/p29.pdf |
They are probably wider band than would be required to read only the sensor self illumination.
>> Second, even if you do plan on adding extra cameras, the extrinsic calibration between camera and lidar may become easier if you have good quality ambient light measurement from the lidar. For example Jesse Levinson, cofounder of Zoox, computes extrinsic calibration between camera and Velodyne lidar by assuming that depth discontinuities are correlated with visual features [1]. But obviously the correlation between 850 nm images and visible light images would be way better.
I agree with that - but you could probably go the other way around and coorelate the LIDAR depth map with depth obtained through stereo imaging. The temporal synchronization between NIR and depth provided by this unit is nice though.
Let me phrase this differently - while the videos are cool to watch, I don't think calibration is the problem in vehicles, and nor are baseline artifacts between sensors when operating at such far ranges (whether your camera and LIDAR are perfectly aligned or translated 10cm apart, it won't matter much looking 10m down the road).
Having moving parts, however, won't get this system into a production model.