Journal of Intelligent & Robotic Systems

, Volume 79, Issue 1, pp 55-72

First online:

6-DoF Low Dimensionality SLAM (L-SLAM)

  • Nikos ZikosAffiliated withDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki Email author 
  • , Vassilios PetridisAffiliated withDepartment of Electrical and Computer Engineering, Aristotle University of Thessaloniki

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In this paper, a new Simultaneous Localization and Mapping (SLAM) method is proposed, called L-SLAM, which is a Low dimension version of the FastSLAM family algorithms. L-SLAM uses a particle filter of lower dimensionality than FastSLAM and achieves better accuracy than FastSLAM 1.0 and 2.0 for a small number of particles. L-SLAM is suitable for high dimensionality problems which exhibit high computational complexity like 6-dof 3D SLAM. Unlike FastSLAM algorithms which use Extended Kalman Filters (EKF), the L-SLAM algorithm updates the particles using linear Kalman filters. A planar SLAM problem of a rear drive car-like robot as well as a three dimensional SLAM problem with 6-dof of an airplane robot is presented. Experimental results based on real case scenarios using the Car Park and Victoria Park datasets are presented for the planar SLAM. Also results based on simulated environment and real case scenario of the Koblenz datasets are presented and compared with the three dimensional version of the FastSLAM 1.0 and 2.0. The experimental results demonstrate the superiority of the proposed method over FastSLAM 1.0 and 2.0.


SLAM L-SLAM FastSLAM Dimensionality reduction