ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges
This addresses localization for vehicles or robots in large-scale environments, but it appears incremental as it builds on existing UWB and learning methods.
The paper tackles the problem of achieving high localization accuracy in complex large-scale environments using Ultra-Wideband (UWB) ranges, which are typically challenged in such settings, and demonstrates that their learning-based framework ULOC ensures high accuracy compared to state-of-the-art methods.
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.