An experimental assessment of the HSM3D algorithm for sparse and colored data

We recently introduced HSM3D, an algorithm to solve the six dimensional scan-matching problem without relying on features in the input, and whose solution does not depend on initial guesses. Building upon these new findings, in this manuscript we present a more detailed experimental study of the algorithm we proposed. In particular, we show how to improve the algorithm´s performance also when matching point clouds produced by stereo cameras, given that this kind of input invalidates some of the assumptions we formerly identified in order to accelerate HSM3D´s performance. We also show that by incorporating color information into the the algorithm it is possible to reduce the number of sporadic outliers in the solution set, thus providing a more reliable algorithm.