WRAP：用于机器人定位的Wasserstein鲁棒自适应插件

中文摘要 AI 辅助

在感知条件变化的情况下，机器人定位可能会出现有偏误差和未校准的协方差。本文提出WRAP，一种与适配器无关的Wasserstein鲁棒插件，适用于非线性扩展卡尔曼滤波（EKF）和误差状态卡尔曼滤波（ESKF）栈。一个因果模块提供时变的有效过程和测量统计量；随后，一个保持均值的Wasserstein局部更新会计算最不利协方差和鲁棒增益，且不改变传播模型、残差或回缩。这将均值自适应与协方差鲁棒化分离，并对传播和传感使用不同的半径。在从适配器训练中留出的18个UWB-IMU序列上，仅适配器和WRAP分别将平均3D位置RMSE相对于标称ESKF降低19.8%和27.4%；各向同性消融研究达到19.5%，表明增量增益与定向过程协方差重新分布相关。样本内GNSS-INS研究显示，均值自适应提供了大部分精度提升，而DR则改善了一致性并缓解了经典协方差估计的过紧问题。该鲁棒求解器在Jetson Orin Nano上处理UWB耗时0.05 ms，处理GNSS耗时2.92 ms。

英文摘要

Robotic localization under changing sensing conditions can suffer from biased errors and miscalibrated covariances. We present WRAP, an adapter-agnostic Wasserstein-robust plug-in for nonlinear extended Kalman filter (EKF) and error-state Kalman filter (ESKF) stacks. A causal module supplies time-varying effective process and measurement statistics; a mean-preserving Wasserstein local update then computes least-favorable covariances and a robust gain without changing the propagation model, residual, or retraction. This separates mean adaptation from covariance robustification and uses distinct radii for propagation and sensing. On 18 UWB--IMU sequences held out from adapter training, adapter-only and WRAP reduce mean 3-D position RMSE by $19.8\%$ and $27.4\%$ relative to the nominal ESKF; an isotropic ablation reaches $19.5\%$, linking the incremental gain to directional process-covariance redistribution. An in-sample GNSS--INS study shows that mean adaptation provides most of the accuracy gain, while DR improves consistency and mitigates over-tightened classical covariance estimates. The robust solve takes 0.05 ms for UWB and 2.92 ms for GNSS on a Jetson Orin Nano.

发表机构

• University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）

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