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

• URL: http://arxiv.org/abs/2409.11122v1
• Date: Tue, 17 Sep 2024 12:20:46 GMT
• Title: ULOC: Learning to Localize in Complex Large-Scale Environments with Ultra-Wideband Ranges
• Authors: Thien-Minh Nguyen, Yizhuo Yang, Tien-Dat Nguyen, Shenghai Yuan, Lihua Xie,
• Abstract summary: We propose a learning-based framework named ULOC for Ultra-Wideband (UWB) based localization. First, anchors are deployed in the environment without knowledge of their actual position. We then propose a network based on MAMBA that learns the ranging patterns of UWBs over a complex large-scale environment.
• Score: 25.17925693293714
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Into the Unknown: Generating Geospatial Descriptions for New Environments [18.736071151303726]
  Rendezvous task requires reasoning over allocentric spatial relationships. Using opensource descriptions paired with coordinates (e.g., Wikipedia) provides training data but suffers from limited spatially-oriented text. We propose a large-scale augmentation method for generating high-quality synthetic data for new environments.
  arXiv  Detail & Related papers  (2024-06-28T14:56:21Z)
• DBA-Fusion: Tightly Integrating Deep Dense Visual Bundle Adjustment with Multiple Sensors for Large-Scale Localization and Mapping [3.5047603107971397]
  We tightly integrate the trainable deep dense bundle adjustment (DBA) with multi-sensor information through a factor graph. A pipeline for visual-inertial integration is firstly developed, which provides the minimum ability of metric-scale localization and mapping. The results validate the superior localization performance of our approach, which enables real-time dense mapping in large-scale environments.
  arXiv  Detail & Related papers  (2024-03-20T16:20:54Z)
• Neural 5G Indoor Localization with IMU Supervision [63.45775390000508]
  Radio signals are well suited for user localization because they are ubiquitous, can operate in the dark and maintain privacy. Many prior works learn mappings between channel state information (CSI) and position fully-supervised. In this work, this requirement is relaxed by using pseudo-labels during deployment, which are calculated from an inertial measurement unit (IMU)
  arXiv  Detail & Related papers  (2024-02-15T13:51:21Z)
• Device-Free Human State Estimation using UWB Multi-Static Radios [9.097545017048446]
  We present a human state estimation framework that allows us to estimate the location, and even the activities, of people in an indoor environment without the requirement that they carry a specific device with them. To achieve this "device free" localization we use a small number of low-cost Ultra-Wide Band (UWB) sensors distributed across the environment of interest.
  arXiv  Detail & Related papers  (2023-12-26T05:51:22Z)
• LocUNet: Fast Urban Positioning Using Radio Maps and Deep Learning [59.17191114000146]
  LocUNet: A deep learning method for localization, based merely on Received Signal Strength (RSS) from Base Stations (BSs) In the proposed method, the user to be localized reports the RSS from BSs to a Central Processing Unit ( CPU) which may be located in the cloud. Using estimated pathloss radio maps of the BSs, LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the radio maps.
  arXiv  Detail & Related papers  (2022-02-01T20:27:46Z)
• LODE: Deep Local Deblurring and A New Benchmark [106.80269193462712]
  We propose a novel framework, termed BLur-Aware DEblurring network (BladeNet) BladeNet contains three components: the Local Blur Synthesis module generates locally blurred training pairs, the Local Blur Perception module automatically captures the locally blurred region and the Blur-guided Spatial Attention module guides the deblurring network with spatial attention. We carry out extensive experiments on REDS and LODE datasets showing that BladeNet improves PSNR by 2.5dB over SotAs for local deblurring while keeping comparable performance for global deblurring.
  arXiv  Detail & Related papers  (2021-09-19T15:57:28Z)
• Real-time Outdoor Localization Using Radio Maps: A Deep Learning Approach [59.17191114000146]
  LocUNet: A convolutional, end-to-end trained neural network (NN) for the localization task. We show that LocUNet can localize users with state-of-the-art accuracy and enjoys high robustness to inaccuracies in the estimations of radio maps.
  arXiv  Detail & Related papers  (2021-06-23T17:27:04Z)
• Learning-based Bias Correction for Time Difference of Arrival Ultra-wideband Localization of Resource-constrained Mobile Robots [8.016760287602084]
  Ultra-wideband (UWB) time difference of arrival (TDOA)-based localization is a promising lightweight, low-cost solution that can scale to a large number of devices. However, the localization accuracy of standard, commercially available UWB radios is often insufficient due to significant measurement bias and outliers. We propose a robust UWB TDOA localization framework comprising of (i) learning-based bias correction and (ii) M-estimation-based robust filtering to handle outliers.
  arXiv  Detail & Related papers  (2021-03-02T17:31:32Z)
• PGL: Prior-Guided Local Self-supervised Learning for 3D Medical Image Segmentation [87.50205728818601]
  We propose a PriorGuided Local (PGL) self-supervised model that learns the region-wise local consistency in the latent feature space. Our PGL model learns the distinctive representations of local regions, and hence is able to retain structural information.
  arXiv  Detail & Related papers  (2020-11-25T11:03:11Z)
• Real-time Localization Using Radio Maps [59.17191114000146]
  We present a simple yet effective method for localization based on pathloss. In our approach, the user to be localized reports the received signal strength from a set of base stations with known locations.
  arXiv  Detail & Related papers  (2020-06-09T16:51:17Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.