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

arXiv cs.RO / 4/6/2026

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Key Points

The paper proposes a geometrically-constrained radar-inertial odometry and mapping framework that jointly models point and pose uncertainty to handle sparse radar returns and complex noise.
It uses a continuous trajectory model to estimate pose uncertainty at arbitrary timestamps by propagating uncertainties from control points, enabling continuous confidence evaluation.
During point projection, the method integrates pose uncertainty with heteroscedastic measurement uncertainty to adaptively down-weight uninformative radar points.
By incorporating quantified uncertainty into radar mapping, the approach builds higher-fidelity maps that improve odometry accuracy under imprecise radar measurements.
Experiments on multiple real-world datasets show improved accuracy and efficiency over existing baselines, and highlight the value of explicit geometrical constraints within the uncertainty-aware framework.

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.