A Novel Genetic Scan-Matching-Based Registration Algorithm for Supporting Moving Objects Tracking Effectively and Efficiently

摘要

In this paper we describe a scan-matching based registration algorithm for tracking moving objects which falls in the emerging area that predicates the integration between robotics and big data applications. The scan matching approaches track paths of a mobile object by comparing maps of the environment seen by the object during its movement. Algorithms described in this paper are hybrid, i.e. they compare maps by using first a genetic pre-alignment based on a novel metrics, and then performing a finer alignment using a deterministic approach. This kind of hybridization is, indeed, not new. However, the novel metrics used in this paper leads to important new properties, namely to correct arbitrary rotational errors and to cover larger search spaces. The proposed algorithm is experimentally compared to other approaches, and better performance in terms of accuracy and robustness are reported. Finally, our algorithm is also very fast thanks to the genetic pre-alignment task and the novel metrics we propose.