Incorporating Point Uncertainty in Radar SLAM

TL;DR

This paper enhances radar SLAM by modeling and integrating point uncertainty, improving robustness in challenging environments through a radar-inertial odometry system validated on real-world datasets.

Contribution

It introduces a novel uncertainty model for radar points in polar coordinates and integrates it into the SLAM pipeline, advancing robustness and accuracy.

Findings

Improved SLAM accuracy in challenging conditions

Effective modeling of radar point uncertainty

Validated on public and self-collected datasets

Abstract

Radar SLAM is robust in challenging conditions, such as fog, dust, and smoke, but suffers from the sparsity and noisiness of radar sensing, including speckle noise and multipath effects. This study provides a performance-enhanced radar SLAM system by incorporating point uncertainty. The basic system is a radar-inertial odometry system that leverages velocity-aided radar points and high-frequency inertial measurements. We first propose to model the uncertainty of radar points in polar coordinates by considering the nature of radar sensing. Then, the proposed uncertainty model is integrated into the data association module and incorporated for back-end state estimation. Real-world experiments on both public and self-collected datasets validate the effectiveness of the proposed models and approaches. The findings highlight the potential of incorporating point uncertainty to improve the radar SLAM system. We make the code and collected dataset publicly available at https://github.com/HKUST-Aerial-Robotics/RIO.