A hybrid approach to RBPF based SLAM with grid mapping enhanced by line matching

In this paper, we present a novel data structure representing the environment with occupancy grid cells while each grid map is associated with a set of line features extracted from laser scan points. Due to the fact that line segments are principal elements of artificial environments, they provide considerable geometric information about the environment which can be used for enhancing the accuracy of localization. Orthogonal characteristic of line features is the key issue to guarantee the consistency of the SLAM algorithm by allowing us to deal with lines that are parallel or perpendicular to each other. This behavior allows us to sample robot poses more correctly. As a result, the proposed algorithm can close bigger loops with the same number of particles. Experimental results are carried out using SICK LMS-100 laser scanner which has a maximum range of 20 m and Pioneer 3DX mobile robot mapping an indoor environment with the size of 40 m × 47 m.