Multilayer Mapping Kit for Autonomous UAV Navigation

Mapping, as the back-end of perception and the front-end of path planning in the modern UAV navigation system, draws our interest. Considering the requirements of UAV navigation and the features of the current embedded computation platforms, we designed and implemented a novel multilayer mapping fra...

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Main Authors: Shengyang Chen, Han Chen, Ching-Wei Chang, Chih-Yung Wen
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Online Access:https://ieeexplore.ieee.org/document/9336584/
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spelling doaj-ef0d071315204ca2a29da6d0a688c0072021-03-30T15:11:16ZengIEEEIEEE Access2169-35362021-01-019314933150310.1109/ACCESS.2021.30550669336584Multilayer Mapping Kit for Autonomous UAV NavigationShengyang Chen0https://orcid.org/0000-0003-1506-0615Han Chen1https://orcid.org/0000-0002-1106-6617Ching-Wei Chang2Chih-Yung Wen3https://orcid.org/0000-0002-1181-8786Department of Mechanical Engineering, The Hong Kong Polytechnic University, Hong KongInterdisciplinary Division of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hong KongDepartment of Mechanical Engineering, The Hong Kong Polytechnic University, Hong KongDepartment of Mechanical Engineering, The Hong Kong Polytechnic University, Hong KongMapping, as the back-end of perception and the front-end of path planning in the modern UAV navigation system, draws our interest. Considering the requirements of UAV navigation and the features of the current embedded computation platforms, we designed and implemented a novel multilayer mapping framework. In this framework, we divided the map into three layers: awareness, local, and global. The awareness map is constructed in cylindrical coordinate, enabling fast raycasting. The local map is a probability-based volumetric map. The global map adopts dynamic memory management, allocating memory for the active mapping area, and recycling the memory from the inactive mapping area. We implemented this mapping framework in three parallel threads: awareness thread, local-global thread, and visualization thread. Finally, we evaluated the mapping kit in both the simulation environment and the real-world scenario with the vision-based sensors. The framework supports different kinds of map outputs for the global or local path planners. The implementation is open-source for the research community.https://ieeexplore.ieee.org/document/9336584/Mappingreconstructionunmanned aerial vehiclenavigationsimultaneous localization and mappingnavigation
collection DOAJ
language English
format Article
sources DOAJ
author Shengyang Chen
Han Chen
Ching-Wei Chang
Chih-Yung Wen
spellingShingle Shengyang Chen
Han Chen
Ching-Wei Chang
Chih-Yung Wen
Multilayer Mapping Kit for Autonomous UAV Navigation
IEEE Access
Mapping
reconstruction
unmanned aerial vehicle
navigation
simultaneous localization and mapping
navigation
author_facet Shengyang Chen
Han Chen
Ching-Wei Chang
Chih-Yung Wen
author_sort Shengyang Chen
title Multilayer Mapping Kit for Autonomous UAV Navigation
title_short Multilayer Mapping Kit for Autonomous UAV Navigation
title_full Multilayer Mapping Kit for Autonomous UAV Navigation
title_fullStr Multilayer Mapping Kit for Autonomous UAV Navigation
title_full_unstemmed Multilayer Mapping Kit for Autonomous UAV Navigation
title_sort multilayer mapping kit for autonomous uav navigation
publisher IEEE
series IEEE Access
issn 2169-3536
publishDate 2021-01-01
description Mapping, as the back-end of perception and the front-end of path planning in the modern UAV navigation system, draws our interest. Considering the requirements of UAV navigation and the features of the current embedded computation platforms, we designed and implemented a novel multilayer mapping framework. In this framework, we divided the map into three layers: awareness, local, and global. The awareness map is constructed in cylindrical coordinate, enabling fast raycasting. The local map is a probability-based volumetric map. The global map adopts dynamic memory management, allocating memory for the active mapping area, and recycling the memory from the inactive mapping area. We implemented this mapping framework in three parallel threads: awareness thread, local-global thread, and visualization thread. Finally, we evaluated the mapping kit in both the simulation environment and the real-world scenario with the vision-based sensors. The framework supports different kinds of map outputs for the global or local path planners. The implementation is open-source for the research community.
topic Mapping
reconstruction
unmanned aerial vehicle
navigation
simultaneous localization and mapping
navigation
url https://ieeexplore.ieee.org/document/9336584/
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AT chingweichang multilayermappingkitforautonomousuavnavigation
AT chihyungwen multilayermappingkitforautonomousuavnavigation
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