MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter
<|reference_start|>MegaParticles: Range-based 6-DoF Monte Carlo Localization with GPU-Accelerated Stein Particle Filter: This paper presents a 6-DoF range-based Monte Carlo localization method with a GPU-accelerated Stein particle filter. To update a massive amount of particles, we propose a Gauss-Newton-based Stein variational gradient descent (SVGD) with iterative neighbor particle search. This method uses SVGD to collectively update particle states with gradient and neighborhood information, which provides efficient particle sampling. For an efficient neighbor particle search, it uses locality sensitive hashing and iteratively updates the neighbor list of each particle over time. The neighbor list is then used to propagate the posterior probabilities of particles over the neighbor particle graph. The proposed method is capable of evaluating one million particles in real-time on a single GPU and enables robust pose initialization and re-localization without an initial pose estimate. In experiments, the proposed method showed an extreme robustness to complete sensor occlusion (i.e., kidnapping), and enabled pinpoint sensor localization without any prior information.<|reference_end|>
@article{koide2024megaparticles:,
    title={MegaParticles: Range-based 6-DoF Monte Carlo Localization with
  GPU-Accelerated Stein Particle Filter},
    author={Kenji Koide, Shuji Oishi, Masashi Yokozuka, and Atsuhiko Banno},
    journal={arXiv preprint arXiv:2404.16370},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.16370},
    primaryClass={cs.RO}
}