KLD-Sampling: Adaptive Particle Filters and Mobile Robot Localization
@inproceedings{Fox2001KLDSamplingAP, title={KLD-Sampling: Adaptive Particle Filters and Mobile Robot Localization}, author={Dieter Fox}, booktitle={NIPS 2001}, year={2001} }
We present a statistical approach to adapting the sample set size of particle filters on-thefly. [] Key Result Extensive experiments using mobile robot localization as a test application show that our approach yields drastic imp rovements over particle filters with fixed sample set sizes and over a previously introduced adapt tion technique.
89 Citations
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A novel multi swarm particle filter moves the samples towards region of the state space where the likelihood is significant, without allowing them to go far away from the region of significant values for the proposed distribution.
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A biologically-inspired global localization system using a LiDAR sensor that utilizes a hippocampal model and a landmark-based re-localization approach and demonstrates the high accuracy, applicability, and reliability of the proposed biologicallyinspired localization system in different localization scenarios.
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An improved and optimized particle filter algorithm was put forward in this paper, named as lightweight particle filters (LPF), which overcomes the limitations of the particle filters and provides high localization rate and precision.
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