KLD sampling with Gmapping proposal for Monte Carlo localization of mobile robots

Guan, R, Wang, L, Ristic, B and Palmer, J 2019, 'KLD sampling with Gmapping proposal for Monte Carlo localization of mobile robots', Information Fusion, vol. 49, no. September,2019, pp. 79-88.


Document type: Journal Article
Collection: Journal Articles

Title KLD sampling with Gmapping proposal for Monte Carlo localization of mobile robots
Author(s) Guan, R
Wang, L
Ristic, B
Palmer, J
Year 2019
Journal name Information Fusion
Volume number 49
Issue number September,2019
Start page 79
End page 88
Total pages 10
Publisher Elsevier BV
Abstract The paper proposes an algorithm for mobile robot navigation that integrates the Gmapping proposal distribution with the Kullback-Leibler divergence for adapting the number of particles. This results in a very effective particle filter with adaptive sample size. The algorithm has been evaluated in both simulation and experimental studies, using the standard KLD-sampling MCL as a benchmark. Simulation results show that the proposed algorithm achieves higher localization accuracy with a smaller number of particles compared to the benchmark algorithm. In a more realistic scenario using experimental data and simulated robot odometry with drift, the proposed algorithm again has greater accuracy using a lower number of particles.
Subject Adaptive Agents and Intelligent Robotics
Signal Processing
Keyword(s) Mobile robots
Monte Carlo localization
Particle filter
DOI - identifier 10.1016/j.inffus.2018.09.003
Copyright notice © 2018 Elsevier B.V. Allrightsreserved.
ISSN 1566-2535
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