TY - JOUR
T1 - Multilayer Mapping Kit for Autonomous UAV Navigation
AU - Chen, Shengyang
AU - Chen, Han
AU - Chang, Ching Wei
AU - Wen, Chih Yung
N1 - Funding Information:
This work was supported by the Research Institute for Sustainable Urban Development, The Hong Kong Polytechnic University, through the Emerging Frontier Area (EFA) Scheme.
Publisher Copyright:
© 2013 IEEE.
PY - 2021/1/27
Y1 - 2021/1/27
N2 - 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.
AB - 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.
KW - Mapping
KW - navigation
KW - reconstruction
KW - simultaneous localization and mapping
KW - unmanned aerial vehicle
UR - http://www.scopus.com/inward/record.url?scp=85100479911&partnerID=8YFLogxK
U2 - 10.1109/ACCESS.2021.3055066
DO - 10.1109/ACCESS.2021.3055066
M3 - Journal article
AN - SCOPUS:85100479911
SN - 2169-3536
VL - 9
SP - 31493
EP - 31503
JO - IEEE Access
JF - IEEE Access
M1 - 2379
ER -