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Data-Efficient Collaborative Decentralized Thermal-Inertial Odometry


Polizzi, Vincenzo; Hewitt, Robert; Hidalgo-Carrio, Javier; Delaune, Jeff; Scaramuzza, Davide (2022). Data-Efficient Collaborative Decentralized Thermal-Inertial Odometry. IEEE Robotics and Automation Letters, 7(4):10681-10688.

Abstract

We propose a system solution to achieve data-efficient, decentralized state estimation for a team of flying robots using thermal images and inertial measurements. Each robot can fly independently, and exchange data when possible to refine its state estimate. Our system front-end applies an online photometric calibration to refine the thermal images so as to enhance feature tracking and place recognition. Our system back-end uses a covariance-intersection fusion strategy to neglect the cross-correlation between agents so as to lower memory usage and computational cost. The communication pipeline uses Vector of Locally Aggregated Descriptors (VLAD) to construct a request-response policy that requires low bandwidth usage. We test our collaborative method on both synthetic and real-world data. Our results show that the proposed method improves by up to 46% trajectory estimation with respect to an individual-agent approach, while reducing up to 89% the communication exchange. Datasets and code are released to the public, extending the already-public JPL xVIO library.

Abstract

We propose a system solution to achieve data-efficient, decentralized state estimation for a team of flying robots using thermal images and inertial measurements. Each robot can fly independently, and exchange data when possible to refine its state estimate. Our system front-end applies an online photometric calibration to refine the thermal images so as to enhance feature tracking and place recognition. Our system back-end uses a covariance-intersection fusion strategy to neglect the cross-correlation between agents so as to lower memory usage and computational cost. The communication pipeline uses Vector of Locally Aggregated Descriptors (VLAD) to construct a request-response policy that requires low bandwidth usage. We test our collaborative method on both synthetic and real-world data. Our results show that the proposed method improves by up to 46% trajectory estimation with respect to an individual-agent approach, while reducing up to 89% the communication exchange. Datasets and code are released to the public, extending the already-public JPL xVIO library.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Scopus Subject Areas:Physical Sciences > Control and Systems Engineering
Physical Sciences > Biomedical Engineering
Physical Sciences > Human-Computer Interaction
Physical Sciences > Mechanical Engineering
Physical Sciences > Computer Vision and Pattern Recognition
Physical Sciences > Computer Science Applications
Physical Sciences > Control and Optimization
Physical Sciences > Artificial Intelligence
Scope:Discipline-based scholarship (basic research)
Language:English
Date:October 2022
Deposited On:26 Feb 2024 16:10
Last Modified:02 May 2024 12:56
Publisher:Institute of Electrical and Electronics Engineers
ISSN:2377-3766
Additional Information:© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
OA Status:Closed
Publisher DOI:https://doi.org/10.1109/LRA.2022.3194675