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

2026-06-16

AI Summary

Radar-based SLAM struggles to produce dense, continuous, and consistent maps due to the sparsity and high noise of radar measurements. This work proposes a Rao-Blackwellized particle filter-based backend framework that integrates incremental Hilbert-space reduced-rank Gaussian process mapping to efficiently construct continuous occupancy maps from sparse radar observations while providing uncertainty estimates. A novel posterior-aware particle weighting mechanism is introduced to significantly enhance the robustness of pose estimation. The effectiveness of the approach is validated on both a self-collected dataset and the public ColoRadar dataset, demonstrating that the resulting maps enable uncertainty-aware navigation and planning for mobile robots.

Abstract

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.

Problem

• Radar SLAM
• sparse measurements
• continuous mapping
• uncertainty-aware representation
• dense map

Innovation

• Radar SLAM
• Hilbert-space Gaussian process
• incremental mapping
• uncertainty-aware mapping
• Rao-Blackwellized particle filter