While UWB-based methods can achieve high localization accuracy in small-scale areas, their accuracy and reliability are significantly challenged in large-scale environments. In this paper, we propose a learning-based framework named ULOC for Ultra-Wideband (UWB) based localization in such complex large-scale environments. First, anchors are deployed in the environment without knowledge of their actual position. Then, UWB observations are collected when the vehicle travels in the environment. At the same time, map-consistent pose estimates are developed from registering (onboard self-localization) data with the prior map to provide the training labels. We then propose a network based on MAMBA that learns the ranging patterns of UWBs over a complex large-scale environment. The experiment demonstrates that our solution can ensure high localization accuracy on a large scale compared to the state-of-the-art. We release our source code to benefit the community at https://github.com/brytsknguyen/uloc.

翻译：虽然基于超宽带的方法在小尺度区域可实现高定位精度，但在大规模环境中其精度与可靠性面临显著挑战。本文针对此类复杂大规模环境，提出一种名为ULOC的学习型超宽带定位框架。首先，在环境中部署未知实际位置的锚点。随后，当载具在环境中行进时收集超宽带观测数据。同时，通过将（车载自定位）数据与先验地图配准，生成地图一致的位姿估计值作为训练标签。我们进一步提出一种基于MAMBA的网络，用于学习超宽带在复杂大规模环境中的测距模式。实验表明，相较于现有先进技术，我们的方案能在大规模场景下保证高定位精度。我们在https://github.com/brytsknguyen/uloc开源代码以惠及研究社区。