Vehicle-to-Vehicle Collaborative Graph-Based Proprioceptive Localization

摘要

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.