3 ms·

I funded a paper mapping vegetation in a forest, if you're curious: https://www.philsalesses.com/s/a582379.pdf https://www.philsalesses.com/s/a582379.pdf IIRC,

I funded a paper mapping vegetation in a forest, if you're curious: https://www.philsalesses.com/s/a582379.pdf 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 was curving over like that scene from Inception. Although maybe only 20 degrees. Still enough to be annoying.

jefft255 8y ago
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=UUoAEeqlo7LxoJf6d88JOG6w https://www.youtube.com/watch?v=V-Q-XWSWT-I&index=2&list=UUo...
- ei8htyfi5e 8y ago
  Your video looks dope. What a difference 8 years makes. https://vimeo.com/16396416 https://vimeo.com/16396416
Dangeranger 8y ago
Thanks for funding this work. Ground based forest mapping is an interesting area.
dllu 8y ago
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 https://pics.dllu.net/file/dllu-sc/6beea0708a.png [1] https://arxiv.org/abs/1802.08633 https://arxiv.org/abs/1802.08633 [2] https://www.ouster.io/s/OS-1-Datasheet.pdf https://www.ouster.io/s/OS-1-Datasheet.pdf

jefft255 8y ago

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=UUoAEeqlo7LxoJf6d88JOG6w https://www.youtube.com/watch?v=V-Q-XWSWT-I&index=2&list=UUo...

ei8htyfi5e 8y ago
Your video looks dope. What a difference 8 years makes. https://vimeo.com/16396416 https://vimeo.com/16396416

Dangeranger 8y ago

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

dllu 8y ago

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