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Paper WeP205.5

Zikos, Nikos (Aristotle University of Thessaloniki), Petridis, Vassilios (Aristotle University of Thessaloniki)

L-SLAM: Reduced Dimensionality FastSLAM with Unknown Data Association

Scheduled for presentation during the Regular Sessions "SLAM IV" (WeP205), Wednesday, May 11, 2011, 16:25−16:40, Room 3G

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on December 8, 2019

Keywords SLAM, Wheeled Robots

Abstract

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.

 

 

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