Proceedings of the third international conference on Genetic algorithms
Adaptive Selection Methods for Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Fastslam: a factored solution to the simultaneous localization and mapping problem with unknown data association
Fast and accurate SLAM with Rao-Blackwellized particle filters
Robotics and Autonomous Systems
Resampling algorithms for particle filters: a computational complexity perspective
EURASIP Journal on Applied Signal Processing
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
A survey of convergence results on particle filtering methods forpractitioners
IEEE Transactions on Signal Processing
Improved Techniques for Grid Mapping With Rao-Blackwellized Particle Filters
IEEE Transactions on Robotics
PSO-FastSLAM: an improved FastSLAM framework using particle swarm optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
| Hi-index | 0.00 |
The state-of-the-art FastSLAM algorithm has been shown to cause a particle depletion problem while performing simultaneous localization and mapping for mobile robots. As a result, it always produces over-confident estimates of uncertainty as time progresses. This particle depletion problem is mainly due to the resampling process in FastSLAM, which tends to eliminate particles with low weights. Therefore, the number of particles to conduct loop-closure decreases, which makes the performance of FastSLAM degenerate. The resampling process has not been thoroughly analyzed even though it is the main reason for the particle depletion problem. In this paper, standard resampling algorithms (systematic residual and partial resampling), a rank-based resampling adopting genetic algorithms are analyzed using computer simulations. Several performance measures such as the effective sample size, the number of distinct particles, estimation errors, and complexity are used for the thorough analysis of the resampling algorithms. Moreover, a new compensation technique is proposed instead of resampling to resolve the particle depletion problem in FastSLAM. In estimation errors, the compensation technique outperformed other resampling algorithms though its run-time was longer than those of others. The most appropriate time to instigate compensation to reduce the run-time was also analyzed with the diminishing number of particles.