Scan- and featurebased multiple hypothesis tracking for mobile robot localization: a data fusion approach

The paper presents first results obtained by a multiple hypothesis approach for mobile robot localization. The purpose of this project is to significantly reduce the accumulation of odometric errors, while an autonomous robot moves through a completely unknown environment. The information of the displacement of the robot is obtained by analyzing the trajectories (tracks) of selected scan-points in the local coordinate frame. The origin of each track is transformed into the reference coordinate system subject to the estimated robot position, and is regarded as a dynamic landmark, as long as it is detected by the laser range-finder. While moving along, the algorithm tries to determine scan-points as local representatives of this landmark. To handle the association ambiguity of scan data to tracks, a multiple hypothesis tracking (MHT) approach is used. Hypotheses for the global robot position are computed by using local tracks and their associated global landmarks. The fusion of these hypotheses is done by using a geometric fusion approach. Both simulation and real data results are presented