Linear-time robot localization and pose tracking using matching signatures

A feature-based method for global localization of mobile robot using a concept of matching signatures is presented. A group of geometric features, their geometric constraints invariant to frame transform, and location dependent constraints, together are utilized in defining signature of a feature. Plausible global poses are found out by matching signatures of observed features with signatures of global map features. The concept of matching signatures is so developed that the proposed method provides a very efficient solution for global localization. Worst-case complexity of the method for estimating and verifying global poses is linear with the size of global reference map. It will also be shown that with the approach of random sampling the proposed algorithm becomes linear with both the size of global map and number of observed features. In order to avoid pose ambiguity, simultaneous tracking of multiple pose hypotheses staying within the same framework of the proposed method is also addressed. Results obtained from simulation as well as from real world experiment demonstrate the performance and effectiveness of the method.