Bumptrees for efficient function, constraint, and classification learning
NIPS-3 Proceedings of the 1990 conference on Advances in neural information processing systems 3
Computational geometry: algorithms and applications
Computational geometry: algorithms and applications
Rao-Blackwellised Particle Filtering for Dynamic Bayesian Networks
UAI '00 Proceedings of the 16th Conference on Uncertainty in Artificial Intelligence
Monte Carlo Localization with Mixture Proposal Distribution
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of 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
PSO-FastSLAM: an improved FastSLAM framework using particle swarm optimization
SMC'09 Proceedings of the 2009 IEEE international conference on Systems, Man and Cybernetics
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FastSLAM is a framework for simultaneous localization and mapping using a Rao-Blackwellized particle filter (RBPF). But, FastSLAM is known to degenerate over time due to the loss of particle diversity, mainly caused by the particle depletion problem in resampling phase. In this work, improved particle filter using geometric relation between particles is proposed to restrain particle depletion and to reduce estimation errors and error variances. It uses a KD tree (k-dimensional tree) to derive geometric relation among particles and filters particles with importance weight conditions for resampling. Compared to the original particle filter used in FastSLAM, this technique showed less estimation error with lower error standard deviation in computer simulations.