Adaptive appearance based loop-closing in heterogeneous environments

The work described in this paper concerns the problem of detecting loop-closure situations whenever an autonomous vehicle returns to previously visited places in the navigation area. An appearance-based perspective is considered by using images gathered by the on-board vision sensors for navigation tasks in heterogeneous environments characterized by the presence of buildings and urban furniture together with pedestrians and different types of vegetation. We propose a novel probabilistic on-line weight updating algorithm for the bag-of-words description of the gathered images which takes into account both prior knowledge derived from an off-line learning stage and the accuracy of the decisions taken by the algorithm along time. An intuitive measure of the ability of a certain word to contribute to the detection of a correct loop-closure is presented. The proposed strategy is extensively tested using well-known datasets obtained from challenging large-scale environments which emphasize the large improvement on its performance over previously reported works in the literature.