WRAP提升机器人定位精度，应对传感器变化带来的误差与协方差失准问题。
WRAP: Wasserstein-Robust Adaptive Plug-in for Robot Localization
- 通过因果模块动态获取过程与观测统计，分离均值适应与协方差鲁棒化。
- 在18组UWB--IMU数据上，定位3D RMSE降低27.4%，优于仅适配器方案。
- 适用于需高鲁棒性定位的自动驾驶、无人机等场景，尤其在传感器波动时表现优异。
在感知条件变化下，机器人定位易受偏差误差和协方差失准影响。本文提出一种无需适配器的Wasserstein鲁棒插件WRAP，适用于非线性扩展卡尔曼滤波（EKF）与误差状态卡尔曼滤波（ESKF）系统。其因果模块提供随时间变化的有效过程与测量统计；基于保均值Wasserstein局部更新，不改变传播模型、残差或重构方式，计算最不利协方差与鲁棒增益。该方法区分传播与感知的径向半径，实现均值自适应与协方差鲁棒化解耦。在18组未参与适配训练的UWB--IMU序列中，适配器仅用与WRAP分别使3D位置均方根误差（RMSE）降低19.8%与27.4%（相对于标准ESKF）；各向同性消融实验达19.5%，表明增量收益源于方向性过程协方差重分布。在样本内GNSS--INS研究中，均值自适应贡献主要精度提升，而鲁棒优化改善一致性并缓解经典协方差估计过紧问题。在Jetson Orin Nano上，单次鲁棒求解耗时0.05毫秒（UWB）与2.92毫秒（GNSS）。
原文摘要 · Abstract (English)
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.
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