Papers › ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges
ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges
Thien-Minh Nguyen, Yizhuo Yang, Tien-Dat Nguyen, Shenghai Yuan, Lihua Xie
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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