L-SLAM: Reduced dimensionality FastSLAM algorithms

Abstract

In this paper, a new SLAM method is proposed, called L-SLAM. It is a low dimension version of the FastSLAM family algorithms. The proposed method reduces the dimensionality of the particle filter that FastSLAM algorithms use, while achieving better accuracy with less or the same number of particles. Dimensionality reduction of this problem is the key feature for high dimensionality problems, like 3-D SLAM where the L-SLAM can produce better results in less time. In contrast to the FastSLAM algorithms that uses Extended Kalman Filters (EKF), the L-SLAM algorithm updates the particles using linear Kalman filters. A methodology of linearizing a planar SLAM problem of a front drive car-like robot is presented. Experimental results on a simulated environment demonstrates the advantages of the proposed method in comparison with the FastSLAM 1.0 and 2.0 methods in a planar SLAM problem.