Fix cost functions for pose graph optimization #2601
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This only affects a covariance that is non-diagonal so might be overlooked for ages. |
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OK an update to this. While the approach from SVD is more robust to ill-conditioned covariance and gives correct residuals for least squares problems, the resulting submatrix is not necessarily lower-triangular, resulting in potentially different ordering of the residuals (which will not pass the unit test since we are testing each dimension of residuals there). I have reverted the SVD to inverse + llt for now and will leave it to the users to make sure that the covariance is well-conditioned. |
ahojnnes
approved these changes
Jun 19, 2024
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Just found a long-standing bug from #2378 in both cost functions in pose graph optimization:
After LLT it should be
L_matrix.transpose()rather thanL_matrixthat is left multiplied on the residuals.Moreover, SVD is faster and more stable compared to
covariance.inverse().llt(). I have updated this as well.