用学习方法解决大场景下UWB定位不准的问题
ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges
- 基于MAMBA网络学习复杂大场景中的UWB测距模式
- 无需已知锚点位置，仅靠车辆移动数据即可训练
- 适合大场景自动驾驶与机器人定位，代码开源
尽管基于UWB的方法在小范围区域可实现高精度定位，但在大规模环境中其精度和可靠性显著下降。本文提出一种基于学习的框架ULOC，用于复杂大场景下的UWB定位。首先，在环境中部署未知位置的锚点，随后通过车辆行驶采集UWB观测数据；同时，利用地图一致性位姿估计（通过自定位数据与先验地图匹配）生成训练标签。我们设计了一种基于MAMBA的网络，学习复杂大场景中UWB的测距规律。实验表明，相比现有最先进方法，该方案在大规模场景下仍能保持高精度定位。代码已开源：https://github.com/brytsknguyen/uloc。
原文摘要 · Abstract (English)
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.
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