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

arXiv: https://arxiv.org/abs/2409.11122 · HTML: https://mlsanity.com/paper/2409.11122/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 28 references that are in the map.
- First posted on arXiv 17 Sep 2024.

## Builds on (most cited first, 18 of 18)

- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [NTU VIRAL: A Visual-Inertial-Ranging-Lidar Dataset, From an Aerial Vehicle Viewpoint](https://mlsanity.com/paper/2202.00379/) (2022)
- [STD: Stable Triangle Descriptor for 3D place recognition](https://mlsanity.com/paper/2209.12435/) (2022)
- [MCD: Diverse Large-Scale Multi-Campus Dataset for Robot Perception](https://mlsanity.com/paper/2403.11496/) (2024)
- [Omni-swarm: A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System for Aerial Swarms](https://mlsanity.com/paper/2103.04131/) (2021)
- [SLICT: Multi-input Multi-scale Surfel-Based Lidar-Inertial Continuous-Time Odometry and Mapping](https://mlsanity.com/paper/2211.03900/) (2022)
- [Outram: One-shot Global Localization via Triangulated Scene Graph and Global Outlier Pruning](https://mlsanity.com/paper/2309.08914/) (2023)
- [UWB Ranging and IMU Data Fusion: Overview and Nonlinear Stochastic Filter for Inertial Navigation](https://mlsanity.com/paper/2308.13393/) (2023)
- [Ultra-Wideband Aided Fast Localization and Mapping System](https://mlsanity.com/paper/1710.00156/) (2017)
- [VR-SLAM: A Visual-Range Simultaneous Localization and Mapping System using Monocular Camera and Ultra-wideband Sensors](https://mlsanity.com/paper/2303.10903/) (2023)
- [Graph Optimization Approach to Range-based Localization](https://mlsanity.com/paper/1802.10276/) (2018)
- [Towards Large-Scale Relative Localization in Multi-Robot Systems with Dynamic UWB Role Allocation](https://mlsanity.com/paper/2203.03893/) (2022)
- [UTIL: An Ultra-wideband Time-difference-of-arrival Indoor Localization Dataset](https://mlsanity.com/paper/2203.14471/) (2022)
- [Finding the Right Place: Sensor Placement for UWB Time Difference of Arrival Localization in Cluttered Indoor Environments](https://mlsanity.com/paper/2204.04508/) (2022)
- [Efficient Planar Pose Estimation via UWB Measurements](https://mlsanity.com/paper/2209.06779/) (2022)
- [Continuous-Time Ultra-Wideband-Inertial Fusion](https://mlsanity.com/paper/2301.09033/) (2023)
- [Continuous-Time Range-Only Pose Estimation](https://mlsanity.com/paper/2304.09043/) (2023)
- [Nonlinear Deterministic Observer for Inertial Navigation using Ultra-wideband and IMU Sensor Fusion](https://mlsanity.com/paper/2308.08023/) (2023)

## Built on by (most cited first, 2 of 2)

- [TAME: Temporal Audio-based Mamba for Enhanced Drone Trajectory Estimation and Classification](https://mlsanity.com/paper/2412.13037/) (2024)
- [NVP-HRI: Zero Shot Natural Voice and Posture-based Human-Robot Interaction via Large Language Model](https://mlsanity.com/paper/2503.09335/) (2025)

## Abstract (arXiv)

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.