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A Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning†

The indoor environment has brought new challenges for micro Unmanned Aerial Vehicles (UAVs) in terms of their being able to execute tasks with high positioning accuracy. Conventional positioning methods based on GPS are unreliable, although certain circumstances of limited space make it possible to... Full description

Journal Title: Sensors 2017, Vol.17(8), p.1842
Main Author: Wang, Can
Other Authors: Kang, Li , Liang, Guoyuan , Chen, Haoyao , Huang, Sheng , Wu, Xinyu
Format: Electronic Article Electronic Article
Language: English
Subjects:
ID: DOI: 10.3390/s17081842
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recordid: proquest1939789996
title: A Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning†
format: Article
creator:
  • Wang, Can
  • Kang, Li
  • Liang, Guoyuan
  • Chen, Haoyao
  • Huang, Sheng
  • Wu, Xinyu
subjects:
  • Ultrawideband
  • Accuracy
  • Satellite Navigation Systems
  • Scanners
  • Global Positioning System
  • Unmanned Aerial Vehicles
  • Hovering
  • Broadband
  • Unmanned Aerial Vehicles
  • Adaptive Filters
  • New Technology
  • Kalman Filters
  • Indoor Environments
  • Inertial Navigation
ispartof: Sensors, 2017, Vol.17(8), p.1842
description: The indoor environment has brought new challenges for micro Unmanned Aerial Vehicles (UAVs) in terms of their being able to execute tasks with high positioning accuracy. Conventional positioning methods based on GPS are unreliable, although certain circumstances of limited space make it possible to apply new technologies. In this paper, we propose a novel indoor self-positioning system of UAV based on a heterogeneous sensing system, which integrates data from a structured light scanner, ultra-wideband (UWB), and an inertial navigation system (INS). We made the structured light scanner, which is composed of a low-cost structured light and camera, ourselves to improve the positioning accuracy at a specified area. We applied adaptive Kalman filtering to fuse the data from the INS and UWB while the vehicle was moving, as well as Gauss filtering to fuse the data from the UWB and the structured light scanner in a hovering state. The results of our simulations and experiments demonstrate that the proposed strategy significantly improves positioning accuracy in motion and also in the hovering state, as compared to using a single sensor.
language: eng
source:
identifier: DOI: 10.3390/s17081842
fulltext: fulltext_linktorsrc
url: Link


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titleA Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning†
creatorWang, Can ; Kang, Li ; Liang, Guoyuan ; Chen, Haoyao ; Huang, Sheng ; Wu, Xinyu
ispartofSensors, 2017, Vol.17(8), p.1842
identifierDOI: 10.3390/s17081842
subjectUltrawideband ; Accuracy ; Satellite Navigation Systems ; Scanners ; Global Positioning System ; Unmanned Aerial Vehicles ; Hovering ; Broadband ; Unmanned Aerial Vehicles ; Adaptive Filters ; New Technology ; Kalman Filters ; Indoor Environments ; Inertial Navigation
descriptionThe indoor environment has brought new challenges for micro Unmanned Aerial Vehicles (UAVs) in terms of their being able to execute tasks with high positioning accuracy. Conventional positioning methods based on GPS are unreliable, although certain circumstances of limited space make it possible to apply new technologies. In this paper, we propose a novel indoor self-positioning system of UAV based on a heterogeneous sensing system, which integrates data from a structured light scanner, ultra-wideband (UWB), and an inertial navigation system (INS). We made the structured light scanner, which is composed of a low-cost structured light and camera, ourselves to improve the positioning accuracy at a specified area. We applied adaptive Kalman filtering to fuse the data from the INS and UWB while the vehicle was moving, as well as Gauss filtering to fuse the data from the UWB and the structured light scanner in a hovering state. The results of our simulations and experiments demonstrate that the proposed strategy significantly improves positioning accuracy in motion and also in the hovering state, as compared to using a single sensor.
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titleA Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning†
descriptionThe indoor environment has brought new challenges for micro Unmanned Aerial Vehicles (UAVs) in terms of their being able to execute tasks with high positioning accuracy. Conventional positioning methods based on GPS are unreliable, although certain circumstances of limited space make it possible to apply new technologies. In this paper, we propose a novel indoor self-positioning system of UAV based on a heterogeneous sensing system, which integrates data from a structured light scanner, ultra-wideband (UWB), and an inertial navigation system (INS). We made the structured light scanner, which is composed of a low-cost structured light and camera, ourselves to improve the positioning accuracy at a specified area. We applied adaptive Kalman filtering to fuse the data from the INS and UWB while the vehicle was moving, as well as Gauss filtering to fuse the data from the UWB and the structured light scanner in a hovering state. The results of our simulations and experiments demonstrate that the proposed strategy significantly improves positioning accuracy in motion and also in the hovering state, as compared to using a single sensor.
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titleA Heterogeneous Sensing System-Based Method for Unmanned Aerial Vehicle Indoor Positioning†
authorWang, Can ; Kang, Li ; Liang, Guoyuan ; Chen, Haoyao ; Huang, Sheng ; Wu, Xinyu
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abstractThe indoor environment has brought new challenges for micro Unmanned Aerial Vehicles (UAVs) in terms of their being able to execute tasks with high positioning accuracy. Conventional positioning methods based on GPS are unreliable, although certain circumstances of limited space make it possible to apply new technologies. In this paper, we propose a novel indoor self-positioning system of UAV based on a heterogeneous sensing system, which integrates data from a structured light scanner, ultra-wideband (UWB), and an inertial navigation system (INS). We made the structured light scanner, which is composed of a low-cost structured light and camera, ourselves to improve the positioning accuracy at a specified area. We applied adaptive Kalman filtering to fuse the data from the INS and UWB while the vehicle was moving, as well as Gauss filtering to fuse the data from the UWB and the structured light scanner in a hovering state. The results of our simulations and experiments demonstrate that the proposed strategy significantly improves positioning accuracy in motion and also in the hovering state, as compared to using a single sensor.
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