用雷达实现连续可量化的地图构建，提升机器人在恶劣环境下的定位精度。
RICH-SLAM: Radar SLAM with Incremental and Continuous Hilbert Mapping
- 基于粒子滤波与卡尔曼滤波结合的后端，实现高鲁棒性位姿估计。
- 提出增量式希尔伯特空间降秩高斯过程映射，从稀疏雷达数据生成连续地图。
- 引入后验感知的粒子权重机制，支持不确定性感知的路径规划，适合移动机器人应用。
使用雷达进行同步定位与建图（SLAM）因其在恶劣天气和光照条件下的固有鲁棒性而受到越来越多关注。然而，相比激光雷达和视觉数据，雷达测量具有稀疏性和噪声大的特点，难以实现稠密、连续且一致的地图表示。本文提出RICH-SLAM，一种针对上述挑战的雷达SLAM框架。该方法采用基于拉奥-布莱克韦尔化粒子滤波的后端，利用粒子滤波进行位姿估计，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，在稀疏雷达输入下实现连续且带不确定性的地图表示。此外，引入后验感知的粒子权重方案，利用地图参数的完整后验分布以增强似然评估的鲁棒性。在自采集数据集及公开的ColoRadar数据集上的实验表明，RICH-SLAM能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。
原文摘要 · Abstract (English)
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.
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