A common data fusion framework for space robotics: architecture and data fusion methods

Abstract : 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.
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Raúl Dominguez, Romain Michalec, Nassir Oumer, Fabrice Souvannavong, Mark Post, et al.. A common data fusion framework for space robotics: architecture and data fusion methods. 14th International Symposium on Artificial Intelligence, Robotics and Automation in Space (i-SAIRAS 2018), Jun 2018, Madrid, Spain. ⟨hal-02092232⟩

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