Abstract
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.
Abstract (translated)
本文提出了一种基于6个自由度的蒙特卡洛局部化方法，采用GPU加速的Stein粒子滤波器。为更新大量粒子，我们提出了一种基于Gauss-Newton的Stein变分梯度下降（SVGD）迭代邻居粒子搜索。该方法使用SVGD共同更新具有梯度和邻居信息的分子的状态，从而实现高效的粒子采样。为了实现高效的邻居粒子搜索，它使用了局部敏感哈希，并随着时间逐个更新每个粒子的邻居列表。邻居列表 then用于在邻居粒子图上传播粒子的后验概率。与传统方法相比，所提出的具有GPU加速的Stein粒子滤波器能够实时评估一百万个粒子，并无需初始姿态估计实现稳健的姿态初始化和重新定位。在实验中，该方法表现出了对完全传感器遮挡（即绑架）的极端鲁棒性，并能在没有任何先前信息的情况下实现精确的传感器局部定位。
URL
https://arxiv.org/abs/2404.16370
