Incorporating Point Uncertainty in Radar SLAM

2024-02-25

AI Summary

Radar SLAM suffers from degraded localization accuracy and inconsistent mapping in adverse conditions—such as fog, dust, and smoke—due to sparse point clouds, speckle noise, and multipath effects. To address this, we propose a tightly coupled radar-inertial SLAM framework that explicitly models radar point uncertainties in polar coordinates. We establish, for the first time, a systematic uncertainty propagation model for range and azimuth measurements in polar space, and integrate it jointly into both data association and backend weighted least-squares optimization to enhance robustness. Additionally, we introduce velocity-aided feature extraction and multi-sensor fusion strategies. Evaluated on public and custom datasets, our method reduces average trajectory position error by 32% and significantly suppresses drift. The source code and datasets are publicly released to advance the practical deployment of radar-based SLAM systems.

Technology Category

• Intelligent Robots: State Estimation
• Reasoning under Uncertainty: Uncertainty Representations
• Planning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

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.

Problem

• Radar SLAM
• Environmental Interference
• Precision Enhancement

Innovation

• Radar SLAM
• Point Uncertainty
• Polar Coordinate Uncertainty Model