Drfit mitigation strategy for low feature environment about open_vins HOT 3 CLOSED

If you have stereo, then you shouldn't have too bad drift while stationary, but it is still possible. When you are stationary it is tough to triangulate features, so you might want to check if feature triangulation is passing and maybe loosen the threshold if your calibration is poor and the triangulation is failing for stereo.

In the monocular case, zero velocity update needs to be used to handle this stationary case since there is no parallax to triangulate features.

Re your 1, using a projected pattern will not work. A key assumption of the environmental features is that they are static, in the case that you are moving and tracking features on a projected pattern which is also moving with you this is invalidated. I am not sure if there is much to do for a white wall besides using a wider FOV camera which is less likely to see a pure white wall without the floor / other parts of a room.

Re your 3, this is likely a function of your shutter speed and motion blur and IMU intrinsic / noise calibration. Sometimes the filter can diverge as the velocity estimate becomes poor and features start to be rejected and thus the system can not recover in these crazy cases. You could try to re-create the class, or re-run the ov_init initializer to try to re-create the system in the case of lost features or super high velocity but I have not tried myself.

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If you shake the camera really fast, features will be difficult to track over the long term. So you should use more msckf features. I usually increase num_pts appropriately and reduce the number of slam features from 50 to 25. This can increase the number of msckf features for robustness.

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Thank you @borongyuan @goldbattle for the detailed response, I will take your suggestions into consideration!

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