FastSLAM is one of the state-of-the-art approaches to the Simultaneous Localization and Mapping (SLAM) problem. In this paper, a new SLAM method is proposed, called L-SLAM, which is a low dimension version of the FastSLAM family algorithms. Dimensionality reduction of the particle filter is proposed, achieving better accuracy with less or the same number of particles. Dimensionality reduction of this problem renders the algorithm suitable for high dimensionality problems, like 3-D SLAM where the L-SLAM can produce better results in less time. Unlike the FastSLAM algorithms that uses Extended Kalman Filters (EKF), the L-SLAM algorithm updates the particles using Kalman filters. A methodology of linearizing a planar SLAM problem of a rear drive car-like robot is presented. Experimental results based on real case scenarios using the Car Park datasets and simulated environment are presented . The advantages of the proposed method in comparison with the FastSLAM 1.0 and 2.0 methods in the planar SLAM problem are discussed.

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.