A Common Data Fusion Framework For Space Robotics: Architecture And Data Fusion Methods
Raúl Domínguez, Romain Michalec, Nassir W. Oumer, Fabrice Souvannavong, Mark Post, Shashank Govindaraj, Alexander Fabisch, Bilal Wehbe, Jérémi Gancet, Alessandro Bianco, Simon Lacroix, Andrea De Maio, Quentin Labourey, Vincent Bissonnette, Michal Smí¨ek, Xiu Yan
In Proceedings of the 14th International Symposium on Artificial Intelligence, (iSAIRAS-2018), 04.6.-06.6.2018, Madrid, ESA, Jun/2018.
Abstract
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Data fusion algorithms make it possible to combine data from different sensors into symbolic representations such as environment maps, object models, and position estimates. The software community in space robotics lacks a comprehensive software framework to fuse and contextually store data from multiple sensors while also making it easier to develop, evaluate, and compare algorithms. The InFuse consortium, six partners in the industrial and academic space sector working under the supervision of a Program Support Activity (PSA) consisting of representatives from ESA, ASI, CDTI, CNES, DLR, UKSA, is developing such a framework, complete with a set of data fusion implementations based on state-of-the-art perception, localization and mapping algorithms, and performance metrics to evaluate them. This paper describes the architecture of this Common Data Fusion Framework and overviews the data fusion methods that it will provide for tasks such as localisation, mapping, environment reconstruction, object detection and tracking.
Keywords
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Data Fusion, Frameworks, Space Robotics, Environment Representation
Files:
Data_Fusion_Framework_and_Methods_-_i-SAIRAS_2018.pdf