question on lidar geometry

[MengshiLi]

There is two roof-mounted LiDAR sensors in Argo's vehicle, but the data set is only giving one Lidar data set. I would like to know which Lidar's data is published. Also, just want to confirm that the lidar point cloud is in ego's coordinate frame, right?

[johnwlambert]

Hello @MengshiLi, that is correct, each LiDAR point cloud is provided in the egovehicle coordinate frame.

We do not provide the corresponding up/down LiDAR sensor from which each LiDAR return originates since we treat the LiDAR as one 64-beam LiDAR, rather than 2 separate 32-beam LiDARs. We've already placed all points into a common reference frame to make the data easier to use for our users, and we've motion compensated the sweeps for vehicle ego-motion.

However, we do provide the LiDAR laser index for each point. Another user has also been working on disambiguating the two LiDAR sweeps; their code can be found here and may be of interest to you.

[MengshiLi]

Thank you so much for the quick reply, @johnwlambert . On top of your clarification, when you combine two Lidar's output as a single 64-beam Lidar, how do you handle the timing difference between these two Lidars? Do you assume that these two Lidars are perfectly matched in time? Or is the timing difference between them so tiny that it can be ignored?

Also in the paper (http://openaccess.thecvf.com/content_CVPR_2019/html/Chang_Argoverse_3D_Tracking_and_Forecasting_With_Rich_Maps_CVPR_2019_paper.html), you mentioned some supplemental material several times, but we can't find it online. Could you pls share its access address? Thanks in advance!

[James-Hays]

Hi @MengshiLi

"how do you handle the timing difference between these two Lidars? Do you assume that these two Lidars are perfectly matched in time? Or is the timing difference between them so tiny that it can be ignored?"

As John said, the points from the two lidars are motion compensated separately. Motion compensation is based entirely on the perceived movement of the AV. So if the AV is static, motion compensation isn't happening, and we're not doing anything different between the two lidar sweeps. If the AV IS moving, then we take into account that the lidar moved perhaps 0.5m between sweeps and adjust the location of the lidar returns accordingly. In practice, this works quite well for static objects in the world. However, when both the AV and an object are moving then the motion compensation won't help. So you can see cases where a moving object is "smeared" out a bit because the returns from that object came back 50ms apart and the object had moved half a meter in that time. It may be advantageous to process the two lidars independently in some cases. This situation isn't really unique to having two lidars, by the way. The entire notion of a lidar "frame" is artificial, because the points are being continuously acquired. Even with a single lidar, you can get a frame that starts and stops on the same object and thus the same "smearing" is observed at the "seam". So even with a single lidar you might want to reason about fine time granularities than "frames".

Regarding supplemental material, CVPR does host that. It's on the page you linked to :). See the "Supp" link at the very bottom.

[MengshiLi]

Hi @James-Hays

Thanks so much for the detailed explanation. So with time granularity being small enough, it is reasonable to align two Lidars' return beams that arrive approximately at the same time, and store them in a combined file, named by the nearest timestamp. Is this understanding correct?

[James-Hays]

I don't think there's a simple answer to your question. For some applications, such as deep lidar object detection, I would imagine the merged, 10hz point clouds are sufficient. If you are instead trying to recover very precise shape models for dynamic objects then I think you want to maintain understanding of the time that each lidar point was detected.