ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges

TL;DR

ULOC is a learning-based framework that improves UWB-based localization accuracy in large-scale environments by using map-consistent pose estimates and a neural network to learn ranging patterns.

Contribution

The paper introduces ULOC, a novel learning-based approach that enhances UWB localization accuracy in large-scale environments without prior anchor position knowledge.

Findings

Achieves higher localization accuracy than state-of-the-art methods

Uses a neural network to learn UWB ranging patterns in complex environments

Provides open-source code for community use

Abstract

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.