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

Dominguez, Raul and Govindaraj, Shashank and Gancet, Jeremi and Post, Mark and Michalec, Romain and Oumer, Nassir and Wehbe, Bilal and Bianco, Alessandro and Fabisch, Alexander and Lacroix, Simon and De Maio, Andrea and Labourey, Quentin and Souvannavong, Fabrice and Bissonnette, Vincent and Smisek, Michal and Yan, Xiu (2018) A common data fusion framework for space robotics : architecture and data fusion methods. In: International Symposium on Artificial Intelligence, Robotics and Automation in Space Symposia, 2018-06-04 - 2018-06-06.

[img]
Preview
Text (Dominguez-etal-I-SAIRAS-2018-A-common-data-fusion-framework-for-space)
Dominguez_etal_I_SAIRAS_2018_A_common_data_fusion_framework_for_space.pdf
Final Published Version

Download (1MB)| Preview

    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.