One-to-one feature matching with inaccurate maps

In the problems of localization using inaccurate maps, navigation agents have to match available information from sensors to maps in order to find their locations. A map contains a set of constraints that can be expressed in the form of a graphical model that matching algorithm has to satisfy. There are two generally categories of constraints: absolute and relative. We propose a relaxation-based algorithm for the NP-hard problem of one-to-one feature matching with absolute and relative constraints. The algorithm is a combination between relaxation labeling and the Kuhn-Munkres method where the former is known for its highly parallel structure imitated the human visual process. To test the performance, we applied the algorithm in a robotics application where the objective is to match range scanner features to those in inaccurate template maps provided by humans. Our experiments show that the proposed algorithm can achieve qualified matching results in artificial and real situations.