Fast laser scan matching approach based on adaptive curvature estimation for mobile robots

This paper describes a complete laser-based approach for tracking the pose of a robot in a dynamic environment. The main novelty of this approach is that the matching between consecutively acquired scans is achieved using their associated curvature-based representations. The proposed scan matching algorithm consists of three stages. Firstly, the whole raw laser data is segmented into groups Of consecutive range readings using a distance-based criterion and the curvature function for each group is computed. Then. this set of curvature functions is matched to the set Of Curvature functions associated to the previously acquired laser scan. Finally, characteristic points of pairwise curvature functions are matched and used to correctly obtain the best local alignment between consecutive scans. A closed for-m solution is employed for computing the optimal transformation and minimizing the robot pose shift error without iterations. Thus, the system is Outstanding in terms of accuracy and computation time. The implemented algorithm is evaluated and compared to three state of the art scan matching approaches.