LAUSR

Proprioceptive localization (PL) refers to robot or vehicle egocentric localization methods that do not rely on the perception and recognition of external landmarks. These methods depend on a prior map and proprioceptive sensors such as inertial measurement units and/or wheel encoders. PL is intended to be a low-cost and fallback solution when everything else fails due to bad weather or poor environmental conditions. With the development of communication technology, vehicle-to-vehicle (V2V) communication enables information exchange between vehicles. It becomes possible to leverage V2V communication to develop a multiple vehicle/robot collaborative localization scheme. Named as collaborative graph-based proprioceptive localization (C-GBPL), we extract heading-length sequence from the trajectory as features. When rendezvousing with other vehicles, the ego vehicle aggregates the features from others and forms a merged query graph. We match the query graph with a pre-processed heading-length graph (HLG) abstracted from a prior map to localize the vehicle under a graph-to-graph matching approach. We have implemented our algorithm and tested it in both simulated and physical experiments. The C-GBPL algorithm significantly outperforms its single-vehicle counterpart in localization speed and robustness to trajectory and map degeneracy.