通过动态评估雷达点可信度，提升复杂环境下的定位精度。
Geometrically-Constrained Radar-Inertial Odometry via Continuous Point-Pose Uncertainty Modeling
- 用连续轨迹模型实时估算任意时刻的位姿不确定性
- 融合异方差测量噪声，自适应降低低信息量雷达点权重
- 适合需要高鲁棒性定位的自动驾驶与机器人场景
雷达里程计在复杂环境中对鲁棒定位至关重要；然而，可靠回波稀疏且具有独特噪声特性，限制了其性能。本文提出几何约束的雷达-惯性里程计与建图方法，联合建模点与位姿不确定性。采用连续轨迹模型，通过传播控制点不确定性，实现任意时间戳的位姿不确定性估计。这些位姿不确定性在点投影过程中与异方差测量不确定性持续融合，从而实现观测置信度的动态评估，并自适应地降低无信息雷达点的权重。利用量化后的雷达映射不确定性，构建高保真地图，在雷达测量不精确时仍能提升里程计精度。此外，实验表明，将显式几何约束引入所提出的不确定性感知映射框架中，显著提升了雷达-惯性里程计性能。在多个真实数据集上的大量实验验证了该方法的优越性，相比现有基线在准确性和效率上均有显著提升。
原文摘要 · Abstract (English)
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.
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