Global localization using multiple hypothesis tracking: A real-world approach

Life-long and robust operation are important challenges to be solved towards everyday usability of service robots. Global localization is of particular interest for real-world applications. If a robot would not be able to relocalize itself within a known map, all positions stored by the robot (rooms, objects, etc.) would become obsolete. Although Simultaneous Localization and Mapping (SLAM) allows to initially map new and unknown environments and to keep track of environmental changes, it does not solve the global localization problem. Each time SLAM is restarted at different locations, it introduces a new map and a new frame of reference. In this paper, we propose a solution to the global localization problem which uses a SLAM generated feature map. The approach is demonstrated with an omnicam and bearing-only features. A new way to weight hypotheses and to sort out false hypotheses results in fast convergence even with arbitrary relocalization paths. The combined approach is a further step towards life-long operation of service robots and covers every part of a robot lifecycle, ranging from a setup via SLAM to efficient global localization for reuse of maps and object poses after restart.