Geometrically-Constrained Radar-Inertial Odometry via Continuous Point-Pose Uncertainty Modeling

TL;DR

This paper presents a novel radar-inertial odometry method that models point and pose uncertainties continuously, enabling more accurate and reliable localization in challenging environments.

Contribution

It introduces a geometrically-constrained, uncertainty-aware framework that propagates and integrates uncertainties for improved radar mapping and odometry accuracy.

Findings

Achieves higher accuracy than existing methods on real-world datasets.

Effectively down-weights uninformative radar points through uncertainty modeling.

Demonstrates robustness in diverse challenging environments.

Abstract

Radar odometry is crucial for robust localization in challenging environments; however, the sparsity of reliable returns and distinctive noise characteristics impede its performance. This paper introduces geometrically-constrained radar-inertial odometry and mapping that jointly consolidates point and pose uncertainty. We employ the continuous trajectory model to estimate the pose uncertainty at any arbitrary timestamp by propagating uncertainties of the control points. These pose uncertainties are continuously integrated with heteroscedastic measurement uncertainty during point projection, thereby enabling dynamic evaluation of observation confidence and adaptive down-weighting of uninformative radar points. By leveraging quantified uncertainties in radar mapping, we construct a high-fidelity map that improves odometry accuracy under imprecise radar measurements. Moreover, we reveal the effectiveness of explicit geometrical constraints in radar-inertial odometry when incorporated with the proposed uncertainty-aware mapping framework. Extensive experiments on diverse real-world datasets demonstrate the superiority of our method, yielding substantial performance improvements in both accuracy and efficiency compared to existing baselines.