Adaptive Robot Localization with Ultra-wideband Novelty Detection

Umberto Albertin; Mauro Martini; Alessandro Navone; Marcello Chiaberge

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arXiv · 2505.05903

Adaptive Robot Localization with Ultra-wideband Novelty Detection

Umberto Albertin·Mauro Martini·Alessandro Navone·Marcello Chiaberge

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Abstract

Ultra-wideband (UWB) technology has shown remarkable potential as a low-cost general solution for robot localization. However, limitations of the UWB signal for precise positioning arise from the disturbances caused by the environment itself, due to reflectance, multi-path effect, and Non-Line-of-Sight (NLOS) conditions. This problem is emphasized in cluttered indoor spaces where service robotic platforms usually operate. Both model-based and learning-based methods are currently under investigation to precisely predict the UWB error patterns. Despite the great capability in approximating strong non-linearity, learning-based methods often do not consider environmental factors and require data collection and re-training for unseen data distributions, making them not practically feasible on a large scale. The goal of this research is to develop a robust and adaptive UWB localization method for indoor confined spaces. A novelty detection technique is used to recognize outlier conditions from nominal UWB range data with a semi-supervised autoencoder. Then, the obtained novelty scores are combined with an Extended Kalman filter, leveraging a dynamic estimation of covariance and bias error for each range measurement received from the UWB anchors. The resulting solution is a compact, flexible, and robust system which enables the localization system to adapt the trustworthiness of UWB data spatially and temporally in the environment. The extensive experimentation conducted with a real robot in a wide range of testing scenarios demonstrates the advantages and benefits of the proposed solution in indoor cluttered spaces presenting NLoS conditions, reaching an average improvement of almost 60% and greater than 25cm of absolute positioning error.

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Umberto Albertin, Mauro Martini, Alessandro Navone, Marcello Chiaberge. 2025-05-09. Adaptive Robot Localization with Ultra-wideband Novelty Detection. https://arxiv.org/abs/2505.05903

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