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.

翻译：超宽带（UWB）技术作为一种低成本的通用机器人定位解决方案已展现出显著潜力。然而，环境本身造成的干扰，如反射、多径效应和非视距（NLOS）条件，限制了UWB信号在精确定位中的应用。这一问题在服务机器人平台通常运行的杂乱室内空间中尤为突出。目前，基于模型的方法和基于学习的方法均在研究之中，以期精确预测UWB误差模式。尽管基于学习的方法在逼近强非线性方面能力突出，但其往往未考虑环境因素，且对于未见过的数据分布需要重新进行数据收集和训练，这使其难以在大规模应用中实际可行。本研究的目标是开发一种适用于室内受限空间的鲁棒自适应UWB定位方法。我们采用一种新颖性检测技术，通过半监督自编码器从标称UWB测距数据中识别异常条件。随后，将获得的新颖性评分与扩展卡尔曼滤波器相结合，利用对从UWB锚点接收的每个测距值的协方差和偏差误差进行动态估计。最终形成的解决方案是一个紧凑、灵活且鲁棒的系统，它使定位系统能够在环境中从空间和时间维度自适应地评估UWB数据的可信度。通过在多种测试场景中使用真实机器人进行的大量实验表明，所提解决方案在存在NLoS条件的室内杂乱空间中具有优势与效益，绝对定位误差平均提升近60%，误差减少超过25厘米。