Talk Resource-Efficiently to Me: Optimal Communication Planning for Distributed Loop Closure Detection
Name
1709.06675.pdf
Description
Submitted version
Size
354.45 KB
Format
Adobe PDF
Checksum (MD5)
669a736629c2d9eea53a403aa301f4b9
Author(s)
Giamou, Matthew
Khosoussi, Kasra
How, Jonathan P
Date Issued
September 2018
Journal
IEEE International Conference on Robotics and Automation (ICRA)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Giamou, Matthew et al. "Talk Resource-Efficiently to Me: Optimal Communication Planning for Distributed Loop Closure Detection." IEEE International Conference on Robotics and Automation (ICRA), May 2018, Institute of Electrical and Electronics Engineers, September 2018. © 2018 IEEE
Version
Original manuscript
Abstract
Due to the distributed nature of cooperative simultaneous localization and mapping (CSLAM), detecting inter-robot loop closures necessitates sharing sensory data with other robots. A naïve approach to data sharing can easily lead to a waste of mission-critical resources. This paper investigates the logistical aspects of CSLAM. Particularly, we present a general resource-efficient communication planning framework that takes into account both the total amount of exchanged data and the induced division of labor between the participating robots. Compared to other state-of-the-art approaches, our framework is able to verify the same set of potential inter-robot loop closures while exchanging considerably less data and influencing the induced workloads. We develop a fast algorithm for finding globally optimal communication policies, and present theoretical analysis to characterize the necessary and sufficient conditions under which simpler strategies are optimal. The proposed framework is extensively evaluated with data from the KITTI odometry benchmark datasets.
MIT Department
Massachusetts Institute of Technology. Laboratory for Information and Decision Systems
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/icra.2018.8460783
