WRAP: Wasserstein-Robust Adaptive Plug-in for Robot Localization
2026-08-10 • Robotics
Robotics
AI summaryⓘ
The authors present WRAP, a method to improve robotic localization when sensor conditions change. WRAP works as a plug-in for Kalman filter systems, helping to adjust the confidence in sensor data without altering the main model. It separates how the average estimates and uncertainties are updated, making the system more reliable and consistent under different sensing scenarios. Tests show WRAP reduces position errors and runs quickly on typical hardware.
Robotic LocalizationKalman FilterExtended Kalman Filter (EKF)Error-State Kalman Filter (ESKF)Wasserstein DistanceRobust EstimationProcess NoiseMeasurement NoiseUWB (Ultra-Wideband)GNSS (Global Navigation Satellite System)
Authors
Minhyuk Jang, Astghik Hakobyan, Jungjin Lee, Naira Hovakimyan, Insoon Yang
Abstract
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.