Paper Info
Title
A high efficient multi-robot simultaneous localization and mapping system using partial computing offloading assisted cloud point registration strategy
Abstract
The robots using visual simultaneous localization and mapping (SLAM) system are generally experiencing excessive power consumption and suffer from depletion of battery energy during the course of working. The intensive computation necessary to complete complicated tasks is overwhelming for inexpensive mobile robots with limited on-board resources. To address this problem, a novel task offloading strategy combined with a new dense point cloud map construction method is proposed in this paper, which is firstly used for the improvement of the system especially in indoor scenes. First, we develop a novel strategy to remotely offload computation-intensive tasks to cloud center so that the tasks that could not originally be achieved locally on the resource-limited robot systems become possible. Second, a modified iterative closest point algorithm (ICP), named fitness score hierarchical ICP algorithm (FS-HICP), is developed to accelerate point cloud registration. The correctness, efficiency, and scalability of the proposed strategy are evaluated with both theoretical analysis and experimental simulations. The results show that the proposed method can effectively reduce the energy consumption while increase the computation capability and speed of the multi-robot visual SLAM system, especially in indoor environment.
Year
DOI
Venue
2021
10.1016/j.jpdc.2020.10.012
Journal of Parallel and Distributed Computing
Keywords
DocType
Volume
Energy consumption,Multi-robot system,Computing offloading,SLAM system,ICP algorithm
Journal
149
ISSN
Citations 
PageRank 
0743-7315
1
0.37
References 
Authors
0
5
Name
Order
Citations
PageRank
Biwei Li110.37
Zhenqiang Mi2758.72
Yu Guo365.89
Yang Yang4612174.82
Mohammad S. Obaidat59917.27