Paper Info
Title
OptiMaP: swarm-powered Optimized 3D Mapping Pipeline for emergency response operations
Abstract
A smart application in sensing is mainly powered by a two-stage process comprising sensing (collect data) and computing (process data). While the sensing stage is typically performed locally through a dedicated Internet of Things infrastructure, the computing stage may require a powerful infrastructure in the cloud. However, when connectivity is poor and low latency becomes a requirement — as in emergency response and disaster relief operations — edge computing and ad hoc cloud paradigms come in support to keep the computing stage locally. Being local network connectivity and data processing limited, it is vital to properly optimize how the computing workload will be consumed by the local ad hoc cloud. For this purpose, we present and evaluate the swarm-powered Optimized 3D Mapping Pipeline (OptiMaP) for emergency response 3D mapping missions, which is implemented as a collaborative embedded Robot Operating System (ROS) application integrating an ad hoc telecommunication middleware.We simulate — with Software-In-The-Loop — realistic 3D mapping missions comprising up to 5 drones and 363 images covering 0.293km <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . We show how the completion times of mapping missions carried out in a typical centralized manner can be dramatically reduced by two versions of the OptiMaP framework powered, respectively, by a variable neighborhood search heuristic and a greedy method.
Year
DOI
Venue
2022
10.1109/DCOSS54816.2022.00052
2022 18th International Conference on Distributed Computing in Sensor Systems (DCOSS)
Keywords
DocType
ISSN
Cloud Computing,Swarm,3D Reconstruction,Workload Optimization
Conference
2325-2936
ISBN
Citations 
PageRank 
978-1-6654-9513-4
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Leandro R. Costa100.34
Daniel Aloise200.34
Luca G. Gianoli300.34
Andrea Lodi42198152.51