Graph Matching Algorithm for the Data Association Problem of Simultaneous Localization and Mapping in Ambiguous and Dynamic Environments

Proposed a graph matching approach RRW&SC to tackle the data association problem inherited in the SLAM. In our framework, the graph theory was utilized to build a mathematical model for data association firstly. Then the shape context feature was extracted for each node. Reweighted random walks was lastly adopted as the optimization engine to obtain the optimal solution for the graph model. The topology structure of the landmarks and the shape of the landmarks was used by RRW&SC algorithm, thus the geometric information of the environment was greatly enhanced which facilitates the data association. Simulation results show that, compared with traditional algorithms, the proposed data association algorithm can effectively handle a variety of complicated scenarios which might occur in SLAM, including enlarged observation noise, robot being kidnapped, or dynamic occlusion.