arXiv 2606.175342026-06-17cs.RO

RICH-SLAM：基于增量连续希尔伯特映射的雷达SLAM

RICH-SLAM: Radar SLAM with Incremental and Continuous Hilbert Mapping

State Key Laboratory of Ocean Sensing, Zhejiang University（浙江大学海洋传感国家重点实验室）；Interdisciplinary Student Training Platform for Marine areas, Zhejiang University（浙江大学海洋交叉学科学生培养平台）；Department of Electronic and Computer Engineering, The Hong Kong University of Science and Technology（香港科技大学电子与计算机工程系）；School of AI and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Ocean College, Zhejiang University（浙江大学海洋学院）；Institute of Deep-Sea Science and Engineering, Chinese Academy of Sciences（中国科学院深海科学与工程研究所）

Bingbing Zhang, Huan Yin, Yang Xu, Shuo Liu, Shaojie Shen, Fumin Zhang, Wen Xu

备注：: 12 figures

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AI总结：

提出RICH-SLAM框架，采用Rao-Blackwellized粒子滤波后端和增量希尔伯特空间降秩高斯过程映射，从稀疏雷达测量中构建连续占用地图，并支持不确定性感知规划。

摘要：

由于雷达对恶劣天气和光照条件具有固有的鲁棒性，使用雷达传感器进行同步定位与地图构建（SLAM）越来越受到关注。然而，与激光雷达和视觉数据相比，雷达测量具有稀疏和噪声大的特点，这给实现密集、连续且一致的地图表示带来了重大挑战。在本文中，我们提出了RICH-SLAM，一个旨在解决这些挑战的雷达SLAM框架。我们的方法采用基于Rao-Blackwellized粒子滤波的后端，使用粒子滤波进行位姿估计，卡尔曼滤波进行地图更新。我们提出了一种增量希尔伯特空间降秩高斯过程映射策略，能够在给定稀疏雷达输入的情况下实现连续且具有不确定性感知的地图表示。我们进一步引入了一种后验感知的粒子加权方案，利用地图参数的完整后验分布进行更鲁棒的似然评估。在自采集和公共ColoRadar数据集上的实验表明，RICH-SLAM能够从稀疏雷达测量中构建连续占用地图，并支持移动机器人的不确定性感知规划。

英文摘要：

Simultaneous localization and mapping using radar sensors has gained increasing attention due to radar's inherent robustness to adverse weather and lighting conditions. However, radar measurements are characteristically sparse and noisy compared to LiDAR and visual data, posing significant challenges in achieving dense, continuous, and consistent map representations. In this paper, we present RICH-SLAM, a radar SLAM framework designed to address these challenges. Our approach features a Rao-Blackwellized particle filter-based back end that employs particle filtering for pose estimation and Kalman filtering for map updates. We propose an incremental Hilbert-space reduced-rank Gaussian process mapping strategy that enables continuous and uncertainty-aware map representations given sparse radar inputs. We further introduce a posterior-aware particle weighting scheme that leverages the full posterior distribution of map parameters for more robust likelihood evaluation. Experiments on self-collected and public ColoRadar datasets show that RICH-SLAM constructs continuous occupancy maps from sparse radar measurements and supports uncertainty-aware planning for mobile robots.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。