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

2026-04-03

AI Summary

This work addresses the challenge of achieving high-precision localization with radar odometry in complex environments, where sparse returns and strong noise severely degrade performance. The authors propose a continuous trajectory optimization framework that tightly fuses radar and inertial measurements, introducing—for the first time in radar-inertial odometry—continuous point-to-pose uncertainty modeling coupled with explicit geometric constraints. By representing the trajectory as a continuous-time model, the method propagates control-point uncertainties to estimate full pose distributions at arbitrary timestamps. Furthermore, it dynamically adjusts radar point weights during point-to-surface projection by incorporating heteroscedastic measurement noise, thereby adaptively downweighting low-informative observations. Evaluated on multiple real-world datasets, the approach significantly outperforms existing methods, delivering substantial improvements in both localization accuracy and computational efficiency.

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.

Problem

• radar odometry
• uncertainty modeling
• geometric constraints
• inertial fusion
• sparse measurements

Innovation

• radar-inertial odometry
• continuous trajectory model
• uncertainty propagation
• heteroscedastic measurement
• geometric constraints