On analyzing the performance of least squares methods in motion estimation
An algorithm is presented here to obtain the total least squares (TLS) estimates of the motion parameters of an object from range data or perspective views. TLS estimates are suitable when data in both frames are corrupted by noise. We analyze the robustness of different least squares methods for the estimation of motion parameters against the possible mismatches in establishing the feature point correspondence. As the errors in point correspondence increase, the performance of ordinary least squares (LS) estimator was found to deteriorate much faster than that of TLS estimator.