viso2 stereo disparity

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Hi,

Does the distance between the cameras matter much?

Yes it does matte, as you increase the baselength B, your ability to reconstruct distant points increases, for indoor purposes baseline should be around ~12/13 cm. (this depends on the camera lenses(zoom level) and the focal length too). for out-door purposes use 25 to 30 cm.

Is this setup good enough?

the set up is good enough. make sure that cameras provide data of the same time, i mean there should not me more 30ms of delay between two camera(left-right) frames(again this depends on your speed of camera)

The above are my calibration results.Is that fine?

Your calibration looks fine! and disparity map too.

Are the disparity maps ok? Any suggestions on improving them? Is it okay to have grey background?

You are getting grey parts in back ground because algorithm is not able to find the disparity values for that region. if you want to improve it, you should use SGBM (again speed is the deciding factor here)

I didn't get any reprojection error as mention in this post. It just showed ? under epi . Is that okay.Around 180 samples were used in calibration.

After the calibration, when you bring your board in the frame the '?' will be replaced by the reprojection error. Try it again :) and 180 samples are more than enough. usually 20 samples will do the job :P

Now the actual question,

Viso2 is very good algorithm for pose estimation, it computes sparse features to get the changes in pose, the code is meant for high resolution and wide-baseline cameras and for out door purposes, though it should work on the low resolution cameras too. Your TF tree and other system level things are fine as far as i can see but the code is not able to find sufficient amount of feature matches thats the reason for "odom lost".

Try running this package outdoor and see how is the performance once. Number of matches will improve. Make sure that you experiment with this package in an environment where number of features are high. Try not to perform in-place rotation of camera, in that case Visual odometry algorithm might not work properly.

Hope I helped Sudeep