MAM3SLAM: Towards underwater-robust multi-agent visual SLAM

Some underwater applications involve deploying multiple underwater Remotely Operated Vehicles in a common area. Such applications require the localization of these vehicles, not only with respect to each other but also with respect to a previously unknown environment. To this end, this work presents...

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Bibliographic Details
Published in:Ocean engineering 2024-06, Vol.302, Article 117643
Main Authors: Drupt, Juliette, Comport, Andrew I., Dune, Claire, Hugel, Vincent
Format: Article
Language:English
Subjects:
Automatic
Computer Science
Computer Vision and Pattern Recognition
Engineering Sciences
Multi-robot systems
Remotely Operated Vehicles (ROVs)
Robotics
Visual SLAM
Online Access:Get full text
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Summary:Some underwater applications involve deploying multiple underwater Remotely Operated Vehicles in a common area. Such applications require the localization of these vehicles, not only with respect to each other but also with respect to a previously unknown environment. To this end, this work presents MAM3SLAM, a new fully centralized multi-agent and multi-map monocular Visual Simultaneous Localization And Mapping (VSLAM) framework. Multi-agent evaluation metrics are introduced to provide an extensive evaluation of MAM3SLAM compared to the state-of-the-art multi-agent VSLAM on four two-agent scenarios: one standard airborne dataset and three new underwater datasets recorded in a pool and the sea. The results show that MAM3SLAM is robust to underwater visual conditions and tracking failures, outperforms the other evaluated methods in estimating the individual and relative poses of the agents and in collaborative mapping accuracy. MAM3SLAM successfully estimates the individual and relative localization of the agents with an error lower than 5 cm on three out of the four test sequences, and is twice as accurate as competing multi-agent works in challenging visual conditions with frequent visual dropouts, poor textures, low framerate and fast motion. MAM3SLAM’s source code is made available, as are the underwater datasets. •A new, centralized multi-agent VSLAM solution for ROVs localization is introduced.•Individual, relative and co-mapping-related evaluation metrics are introduced.•Three new underwater two-agent datasets, in pools and in the sea, are released.•Evaluations show the robustness of our approach compared to competing visual works.
ISSN:0029-8018
1873-5258
DOI:10.1016/j.oceaneng.2024.117643