Deployment-Ready UWB Localization for Industrial Ground Robots with Automatic Anchor Calibration and Terrain-Aware Fusion

Localization in challenging environments, such as GNSS-denied, geometrically repetitive, or textureless scenes commonly found in offices, hotels, and underground parking facilities, remains an open problem for reliable autonomous mobile robot (AMR) deployment. Single-modality localization methods are inherently limited by the constraints of individual sensors. Although multimodal fusion frameworks have shown improved robustness, most existing approaches still rely heavily on geometric or texture features, or on infrastructure-based beacons, which increase installation and maintenance costs while reducing deployment flexibility. Recently, ambient magnetic field (AMF)-based localization has attracted growing attention because it does not depend on geometric or texture features, nor does it require additional infrastructure, making it a promising complementary modality for AMR localization. However, existing studies have only explored such fusion in pedestrian scenarios using smartphone-mounted sensor suites, and practical solutions for AMR systems remain largely unexplored. To address this gap, this article proposes a magnetometer-inertial-LiDAR fused multimodal localization framework with a custom-designed sensor suite, termed MIL-LC, which provides reliable localization even when LiDAR suffers from geometric degeneration or when the magnetic map changes during long-term deployment. Extensive experiments in both simulation and real-world environments demonstrate that the proposed MIL-LC framework achieves robust and accurate localization performance.

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.

Accurate, tag-free distance estimation with ultrawideband (UWB) radar is essential for applications such as autonomous guided vehicles, robotics, and environment characterization. For tag-based localization systems, phase-based UWB signal processing techniques have demonstrated sub-wavelength ranging precision, but these approaches are not applicable for passive (tagless) radar setups with weak reflections, mixed multipath conditions, and the absence of a known time-of-flight (ToF) first-path reference. This paper demonstrates for the first time that phase information can be effectively exploited in a fully passive UWB radar setting. We introduce a signal processing framework that extracts reliable distance information by combining coarse amplitude-based estimates with high-resolution phase changes across multiple frequency channels. By referencing phase measurements with the line-of-sight component, the method compensates for hardware-induced phase drift, while the use of multichannel frequency diversity enables disambiguation of periodic phase information and improves robustness against frequencyspecific channel degradation such as Fresnel zones. The proposed approach is validated on a robot equipped with a bistatic UWB radar using DW3000 devices and evaluated in a realistic metallic industrial environment. Experimental results show that our work consistently achieves centimeter-level accuracy even at high speeds, with a median error of 1.69 cm, significantly outperforming existing ~10cm accuracy UWB radar approaches relying only on amplitude-information. We further show how multi-channel fusion exploits uncorrelated channel degradation to reduce the error by more than 40% compared to single-channel operation, and outline how phase modeling and fusion can be pushed toward sub-centimeter accuracy.