An evaluation of robust cost functions for RGB direct mapping

The so-called direct SLAM methods have shown an impressive performance in estimating a dense 3D reconstruction from RGB sequences in real-time [1], [2], [3]. They are based on the minimization of an error function composed of several terms that account for the photometric consistency of corresponding pixels and the smoothness and the planarity priors on the reconstructed surfaces. In this paper we evaluate several robust error functions that reduce the influence of large individual contributions -that most likely correspond to outliers- to the total error. Our experimental results show that the differences between the robust functions are considerable, the best of them reducing the estimation error up to 25%.