MIMOSA: A Multi-Modal SLAM Framework for Resilient Autonomy against Sensor Degradation
This paper presents a framework for Multi-Modal SLAM (MIMOSA) that utilizes a nonlinear factor graph as the underlying representation to provide loosely-coupled fusion of any number of sensing modalities. Tailored to the goal of enabling resilient robotic autonomy in GPS-denied and perceptually-degr...
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| Veröffentlicht in: | Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems S. 7153 - 7159 |
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23.10.2022
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| ISSN: | 2153-0866 |
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| Abstract | This paper presents a framework for Multi-Modal SLAM (MIMOSA) that utilizes a nonlinear factor graph as the underlying representation to provide loosely-coupled fusion of any number of sensing modalities. Tailored to the goal of enabling resilient robotic autonomy in GPS-denied and perceptually-degraded environments, MIMOSA currently contains modules for pointcloud registration, fusion of multiple odometry estimates relying on visible-light and thermal vision, as well as inertial measurement propagation. A flexible back-end utilizes the estimates from various modalities as relative transformation factors. The method is designed to be robust to degeneracy through the maintenance and tracking of modality-specific health metrics, while also being inherently tolerant to sensor failure. We detail this framework alongside our implementation for handling high-rate asynchronous sensor measurements and evaluate its performance on data from autonomous subterranean robotic exploration missions using legged and aerial robots. |
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| AbstractList | This paper presents a framework for Multi-Modal SLAM (MIMOSA) that utilizes a nonlinear factor graph as the underlying representation to provide loosely-coupled fusion of any number of sensing modalities. Tailored to the goal of enabling resilient robotic autonomy in GPS-denied and perceptually-degraded environments, MIMOSA currently contains modules for pointcloud registration, fusion of multiple odometry estimates relying on visible-light and thermal vision, as well as inertial measurement propagation. A flexible back-end utilizes the estimates from various modalities as relative transformation factors. The method is designed to be robust to degeneracy through the maintenance and tracking of modality-specific health metrics, while also being inherently tolerant to sensor failure. We detail this framework alongside our implementation for handling high-rate asynchronous sensor measurements and evaluate its performance on data from autonomous subterranean robotic exploration missions using legged and aerial robots. |
| Author | Alexis, Kostas Khedekar, Nikhil Kulkarni, Mihir |
| Author_xml | – sequence: 1 givenname: Nikhil surname: Khedekar fullname: Khedekar, Nikhil email: nikhil.v.khedekar@ntnu.no organization: Norwegian University of Science and Technology (NTNU),Autonomous Robots Lab,Trondheim,Norway,7034 – sequence: 2 givenname: Mihir surname: Kulkarni fullname: Kulkarni, Mihir organization: Norwegian University of Science and Technology (NTNU),Autonomous Robots Lab,Trondheim,Norway,7034 – sequence: 3 givenname: Kostas surname: Alexis fullname: Alexis, Kostas organization: Norwegian University of Science and Technology (NTNU),Autonomous Robots Lab,Trondheim,Norway,7034 |
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| Snippet | This paper presents a framework for Multi-Modal SLAM (MIMOSA) that utilizes a nonlinear factor graph as the underlying representation to provide... |
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| SubjectTerms | Current measurement Laser radar Legged locomotion Simultaneous localization and mapping Smoothing methods Thermal sensors Visualization |
| Title | MIMOSA: A Multi-Modal SLAM Framework for Resilient Autonomy against Sensor Degradation |
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