Lidar mapping techniques using multiple sensors • Hacker News Board

Re: Lidar mapping techniques using multiple sensors

#21

Post by dwiel » Mon, Apr 01, 2019, 7:25 PM UTC

dllu wrote: post #19

Dangeranger wrote: post #2

The SLAM approach will work well with a validated point cloud and a new set of points for fixed objects. However if you are mapping movable or alterable objects such as vegetation I am unsure if the algorithm will still yield highly accurate results. Another thing to consider is that if you are basing future measurements on past measurements, you need to be accurate to less than 1cm in the absolute X,Y,Z position of…

I'm the author of this blog post. You are right that the SLAM dead reckoning trajectory will drift. We are developing a mapping back-end where we register trajectories to consumer-grade GPS data, performing loop closure, and then doing a batch ICP-like optimization over multiple drives. This mostly eliminates drift as GPS, noisy as it may be, is mostly zero-mean over large areas. Moving objects are mostly removed or…

Are you planning on integrating these SLAM features into an API available from the device somehow? The spec sheet only mentions point cloud outputs right now.

Re: Lidar mapping techniques using multiple sensors

#22

Post by hellllllllooo » Mon, Apr 01, 2019, 7:53 PM UTC

colkassad wrote: post #14

"Our SLAM algorithm is notable for being able to run in real time with not just one, but three Ouster OS-1 devices at the same time, on a typical desktop computer CPU." What SLAM algorithm is that? Anyone know?

It's using ICP to register sucessive lidar scans. All three lidars are calibrated so the relative positions are known and the data from all three can be combined.

https://en.m.wikipedia.org/wiki/Iterative_closest_point

This alone isn't SLAM but can be used for odometry as part of a SLAM system.

Re: Lidar mapping techniques using multiple sensors

#23

Post by joshu » Mon, Apr 01, 2019, 8:00 PM UTC

does anyone know anyone at ouster? i want to invite them to Self Racing Cars[1] - would love to offer public datasets of a known location so people can compare and contrast different platforms.

[1] http://selfracingcars.com/

Re: Lidar mapping techniques using multiple sensors

#24

Post by jvanderbot » Mon, Apr 01, 2019, 9:06 PM UTC

hellllllllooo wrote: post #22

colkassad wrote: post #14

"Our SLAM algorithm is notable for being able to run in real time with not just one, but three Ouster OS-1 devices at the same time, on a typical desktop computer CPU." What SLAM algorithm is that? Anyone know?

It's using ICP to register sucessive lidar scans. All three lidars are calibrated so the relative positions are known and the data from all three can be combined. https://en.m.wikipedia.org/wiki/Iterative_closest_point This alone isn't SLAM but can be used for odometry as part of a SLAM system.

... which is not surprising they can register in near real time. ICP is not that expensive.

Re: Lidar mapping techniques using multiple sensors

#25

Post by jvanderbot » Mon, Apr 01, 2019, 9:08 PM UTC

Dangeranger wrote: post #2

The SLAM approach will work well with a validated point cloud and a new set of points for fixed objects. However if you are mapping movable or alterable objects such as vegetation I am unsure if the algorithm will still yield highly accurate results. Another thing to consider is that if you are basing future measurements on past measurements, you need to be accurate to less than 1cm in the absolute X,Y,Z position of…

The second point is not absolutely true. Drift can be corrected for across successive scans by including it as a parameter to estimate given the scan data.

Re: Lidar mapping techniques using multiple sensors

#26

Post by pas » Mon, Apr 01, 2019, 9:15 PM UTC

chris_mc wrote: post #16

I wonder if you could position posts or boxes (some physical object) with "weird" shapes that could be used as fixed, recognizable points for this sort of thing? So when your sensor picks it up, it's easy to immediately know that this specific object matches to object ID #1234 which is in a specific, known lat/lon/altitude/rotation/translation position. Something like steganography for these sensors in the real world…

Well, we used this for local calibration: https://github.com/MarekKowalski/LiveScan3D/tree/master/docs... of course this is only to calibrate the feeds relative to each other.

But coupled with GPS almost any shape could work. (Hills, landmarks, buildings.)

Re: Lidar mapping techniques using multiple sensors

#27

Post by dllu » Mon, Apr 01, 2019, 9:35 PM UTC

dwiel wrote: post #21

dllu wrote: post #19

Earlier quoted context omitted.

I'm the author of this blog post. You are right that the SLAM dead reckoning trajectory will drift. We are developing a mapping back-end where we register trajectories to consumer-grade GPS data, performing loop closure, and then doing a batch ICP-like optimization over multiple drives. This mostly eliminates drift as GPS, noisy as it may be, is mostly zero-mean over large areas. Moving objects are mostly removed or…

Are you planning on integrating these SLAM features into an API available from the device somehow? The spec sheet only mentions point cloud outputs right now.

The lidar device is not capable of running SLAM yet. We run SLAM on a computer with an Intel Core i7 processor and currently have not open sourced the algorithm.

Re: Lidar mapping techniques using multiple sensors

#28

Post by brighton36 » Mon, Apr 01, 2019, 9:57 PM UTC

How can we invest in this?

Re: Lidar mapping techniques using multiple sensors

#29

Post by rsp1984 » Mon, Apr 01, 2019, 10:12 PM UTC

Great post! Some questions:

- How strongly does the performance of the SLAM depend on the type of sensor and the amount of sensors being used? I.e. I'm sure the performance using three 128-channel sensors will be better than using one 16-channel sensor.

- Will the software be made available to customers? If yes, as an SDK?

Re: Lidar mapping techniques using multiple sensors

#30

Post by jessriedel » Mon, Apr 01, 2019, 10:25 PM UTC

Dangeranger wrote: post #4

jessriedel wrote: post #3

Earlier quoted context omitted.

> However if you are mapping movable or alterable objects such as vegetation I am unsure if the algorithm will still yield highly accurate results. If most objects are fixed, won't the best solution still be the correct one?

Good question; I am not sure. Imagine if someone were using the SLAM approach to map farm corn fields, in order to determine plant growth rates over the growing season. In that scenario I would think that the majority of the points would be returned from surfaces which were not present in the original point cloud. Of course you could set up ground control stations, surveyed using traditional techniques, and align the…

OK, but that's what I was getting at when I said "most objects are fixed", e.g., if you're driving through a neighborhood a week later, most of the cars have moved and there are some new kids toys on the lawn, but most of the points (streets, houses, poles, etc.) haven't budged.

I agree there are problems in the case of your example though.