In this paper we describe a family of scan-matching based registration algorithms for tracking moving objects which fall 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 family of algorithms is experimentally compared to other approaches, and better performance in terms of accuracy and robustness are reported. Finally, algorithms are also very fast thanks to the genetic pre-alignment task and the novel metrics we propose.
