A New Formation Maintenance Technique for Particle Diversity in RBPF-SLAM

Abstract:

Rao-Blackwellized particle filter (RBPF) has been to the fore as one of methods to solve simultaneous localization and mapping (SLAM) problem, i.e. RBPF-SLAM. The RBPF-SLAM, however, has been suffering from the particle depletion problem and the convergence problem caused by the improper posterior density and brutal rejection and replication of particles during resampling. We present a new technique to overcome those problems in RBPF-SLAM by keeping the particle diversity using particle formation maintenance (PFM). The triangular mesh structure is adaptively generated as a one form of PFM and it completely replaces the resampling part in RBPF-SLAM. Its considerable improvements regarding robot pose and features were shown in simulation by comparing conventional methods, i.e. FastSLAM 2.0 and PSO based FastSLAM.

• Periodical: Applied Mechanics and Materials (Volume 330)
• Pages: 629-634
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.330.629
• Online since: June 2013
• Authors: Seung Hwan Lee, Heon Cheol Lee, Gyu Ho Eoh, Beom Hee Lee
• Keywords: Particle Diversity, RBPF-SLAM, Resampling Technique

References

[1] I. Kim, N. Kwak, H. Lee, and B. Lee: Improved Particle Filter using Geometric Relation between Particles in FastSLAM. Robotica.( 2009), 27 (6): 853-859.

DOI: 10.1017/s0263574708005250

[2] N. Kwak, G. W. Kim, and B.H. Lee: A new compensation technique based on analysis of resampling process n FastSLAM. Robotica. (2008), 26 (2): 205-217.

DOI: 10.1017/s0263574707003773

[3] H. C. Lee, S. Park, J. S. Choi, and B. H. Lee: PSO-FastSLAM: An improved FastSLAM framework using particle swarm optimization. Proc. of IEEE International Conference on Systems, Man and Cybernetics, (2009), pp.2842-2847.

DOI: 10.1109/icsmc.2009.5346572

[4] N. Kwak, B. H. Lee, and K. Yokoi: Representation of the Results from Rao-Blackwellized Particle Filtering for SLAM. Proc. of International Conference on Control, Automation and Systems, (2008), pp.698-703.

DOI: 10.1109/iccas.2008.4694591

[5] G. Lee, N. Y. Chong, and H. I. Christensen: Adaptive triangular mesh generation of self-configuring robot swarms. Proc. of IEEE International Conference on Robotics and Automation, (2009), pp.2737-2742.

DOI: 10.1109/robot.2009.5152748

[6] C. Kim, R. Sakthivel, and W. K. Chung: Unscented FastSLAM: A robust and efficient solution to the SLAM problem. IEEE Transactions on Robotics, (2008), 24 (4): 808-820.

DOI: 10.1109/tro.2008.924946

[7] M. Montemerlo, S. Thrun, D. Koller, and B. Wegbreit: FastSLAM 2.0: An improved particle filtering algorithm for simultaneous localization and mapping that provably converges. Proc. of International Joint Conference on Artificial Intelligence, (2003), pp.1151-1156.

[8] G. Grisetti, C. Stachniss, and W. Burgard: Improving grid-based SLAM with Rao-Blackwellized particle filters by adaptive proposals and selective resampling. Proc. of IEEE International Conference on Robotics and Automation, (2005), pp.2432-2437.

DOI: 10.1109/robot.2005.1570477

[9] K. Murphy: Bayesian map learning in dynamic environments. Proc. of Conference on Neural Information Processing Systems, (1999), pp.1015-1021.