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.

翻译：利用雷达传感器进行同步定位与地图构建因其在恶劣天气和光照条件下的固有鲁棒性而日益受到关注。然而，与LiDAR和视觉数据相比，雷达测量具有典型的稀疏性和噪声特性，这给实现稠密、连续且一致的地图表示带来了显著挑战。本文提出RICH-SLAM——一个旨在应对这些挑战的雷达SLAM框架。该方法采用基于Rao-Blackwellized粒子滤波的后端，通过粒子滤波进行位姿估计，并利用卡尔曼滤波更新地图。我们提出一种增量式希尔伯特空间降秩高斯过程映射策略，能够在稀疏雷达输入条件下实现连续且具有不确定性感知的地图表示。进一步引入后验感知粒子加权方案，通过充分利用地图参数的完整后验分布实现更鲁棒的似然评估。在自采集数据集与公开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据地图，并支持移动机器人的不确定性感知规划。