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Lidar mapping techniques using multiple sensors

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Re: Lidar mapping techniques using multiple sensors

#11

Post by Dangeranger »

jefft255 wrote: post #6
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 (and OP for that matter) do mapping with sensors with accuracies that are around 2 cm. I don't know where you got that 1 cm requirement from. ICP/SLAM drift will happen even with a perfect sensor. It really depends on the scale of what you are trying to measure. There are ways to work with dynamic environments in lidar SLAM: https://ieeexplore.ieee.org/abstract/document/6907397

This was a back of the napkin estimation of accuracy based on some prior experience from several years ago. If you've used sensors with 2cm accuracy and they've performed well I would be interested to know whether they would perform as well if the survey area increased. For example, would they perform as well if the survey area is 10km^2 vs 1km^2 ? Is there a limit on their performance as the survey area increases?

Re: Lidar mapping techniques using multiple sensors

#12

Post by jefft255 »

Dangeranger wrote: post #11
jefft255 wrote: post #6

Earlier quoted context omitted.

I (and OP for that matter) do mapping with sensors with accuracies that are around 2 cm. I don't know where you got that 1 cm requirement from. ICP/SLAM drift will happen even with a perfect sensor. It really depends on the scale of what you are trying to measure. There are ways to work with dynamic environments in lidar SLAM: https://ieeexplore.ieee.org/abstract/document/6907397

This was a back of the napkin estimation of accuracy based on some prior experience from several years ago. If you've used sensors with 2cm accuracy and they've performed well I would be interested to know whether they would perform as well if the survey area increased. For example, would they perform as well if the survey area is 10km^2 vs 1km^2 ? Is there a limit on their performance as the survey area increases?

Well of course there is a limit, mapping at this scale absolutely requires GPS of loop-closure of some sort. Myself, working in forests, I mapped at most 100 meters by 100 meters but some people in my lab are going as large as 1 kilometer by 300 meters.

Re: Lidar mapping techniques using multiple sensors

#13

Post by Dangeranger »

ei8htyfi5e wrote: post #7
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 funded a paper mapping vegetation in a forest, if you're curious: https://www.philsalesses.com/s/a582379.pdf IIRC, the lidar still lined up mostly because tree stems tend to not move, however, the larger problem was the error rate of the lidar sensor we were using. Readings further than 10m and the Hokuyo we were using tended to underestimate distances, so each scan of the forest looked a little but like the floor…

Thanks for funding this work. Ground based forest mapping is an interesting area.

Re: Lidar mapping techniques using multiple sensors

#15

Post by jeffchuber »

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…

you can mask away the points that are moving or you expect to move (eg ignore features from cars and people)

Re: Lidar mapping techniques using multiple sensors

#16

Post by chris_mc »

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: https://en.wikipedia.org/wiki/Machine_Identification_Code

Re: Lidar mapping techniques using multiple sensors

#17

Post by jefft255 »

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…

This is very much in use when possible. They are called fiducial markers, or sometimes registration targets, registration markers etc.

Re: Lidar mapping techniques using multiple sensors

#18

Post by ei8htyfi5e »

jefft255 wrote: post #9
ei8htyfi5e wrote: post #7

Earlier quoted context omitted.

I funded a paper mapping vegetation in a forest, if you're curious: https://www.philsalesses.com/s/a582379.pdf IIRC, the lidar still lined up mostly because tree stems tend to not move, however, the larger problem was the error rate of the lidar sensor we were using. Readings further than 10m and the Hokuyo we were using tended to underestimate distances, so each scan of the forest looked a little but like the floor…

Hey, I'm familiar with your work! I'm currently submitting similar work using a Husky and a Velodyne HDL-32. I don't have the problem you mention with my sensor. See: https://www.youtube.com/watch?v=V-Q-XWSWT-I&index=2&list=UUo...

Your video looks dope. What a difference 8 years makes. https://vimeo.com/16396416

Re: Lidar mapping techniques using multiple sensors

#19

Post by dllu »

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 ignored.

We are primarily interested in mapping urban environments for now. The SLAM does not work very well in a featureless corn field.

Re: Lidar mapping techniques using multiple sensors

#20

Post by dllu »

ei8htyfi5e wrote: post #7
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 funded a paper mapping vegetation in a forest, if you're curious: https://www.philsalesses.com/s/a582379.pdf IIRC, the lidar still lined up mostly because tree stems tend to not move, however, the larger problem was the error rate of the lidar sensor we were using. Readings further than 10m and the Hokuyo we were using tended to underestimate distances, so each scan of the forest looked a little but like the floor…

Ouster SLAM works okay in a forest environment, such as driving with one Ouster OS-1 at highway speeds in Tahoe [0]. It is definitely much more challenging than an urban environment full of flat planes and right angles.

Calibration, including range biases, is probably the one factor with the greatest impact on mapping quality. For example, range bias may cause curved walls, and beam angle biases may cause curved ground.

I recall that the top scoring lidar SLAM algorithms on the KITTI data set all had to perform some calibration (for example, J. E. Deschaud found that all the beams on the Velodyne HDL-64E were tilted by 0.22 degrees [1]).

The Ouster OS-1 lidars have a slight range bias for highly reflective objects [2] but this will be fixed in a firmware update in the near future.

[0] https://pics.dllu.net/file/dllu-sc/6beea0708a.png [1] https://arxiv.org/abs/1802.08633 [2] https://www.ouster.io/s/OS-1-Datasheet.pdf

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