aiqbench analysis
This research advances robust localization by integrating geometric constraints with continuous uncertainty modeling for radar-inertial systems, directly addressing the sparsity and noise challenges inherent in sensor data. The proposed framework dynamically weights observations to construct high-fidelity maps, offering measurable improvements in accuracy and efficiency over existing baselines for autonomous navigation tasks.
Geometrically-Constrained Radar-Inertial Odometry via Continuous Point-Pose Uncertainty Modeling
arXiv:2604.02745v2 Announce Type: replace 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. We release our open-source implementation at https://github.com/wooseongY/GeoRIO.
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. We release our open-source implementation at this https URL.
Robotics (cs.RO)
arXiv:2604.02745 [cs.RO]
(or arXiv:2604.02745v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2604.02745 Focus to learn more arXiv-issued DOI via DataCite
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