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The following link is some captured point cloud data visualized in Rviz following the DRCSIM tutorials. The robot is in the golf cart, set to drive in a circle, with a box placed nearby to try and "see".

http://www.youtube.com/watch?v=lM2UZ-6tRaQ

In the video, the multisense output produces a huge amount of spurious point cloud data. My guess is that it is the approximately synced cameras: when the multisense head moves around any amount, the stere_image_proc picks up too many correspondence points with the not synchronized cameras. I am hoping to do visual odometry and point cloud manipulation asap, but this makes things a bit difficult.

Is this the general experience with the multisense head so far?


Originally posted by klowrey on Gazebo Answers with karma: 41 on 2013-01-15

Post score: 3


Original comments

Comment by dcconner on 2013-01-18:
Yes, this has been our experience as well. That and the fact that stereo processing is a CPU hog. Would we be better off with a simpler simulation of stereo that used depth buffer to generate point clouds from within Gazebo and not try to do separate processing?

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The next version of DRC sim will use synchronized stereo pairs, which will hopefully resolve this issue.

We may offer the depth buffer as an option for the multisense head, but there are no concrete plans for that functionality.


Originally posted by nkoenig with karma: 7676 on 2013-01-18

This answer was ACCEPTED on the original site

Post score: 1

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