Introducing a globally consistent orbital‐based localization system |
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| Authors: | Evangelos Boukas Antonios Gasteratos Gianfranco Visentin |
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| Affiliation: | 1. Robotics, Vision and Machine Intelligence (RVMI) Lab., Department of Materials and Production, Aalborg University Copenhagen, Denmark;2. Laboratory of Robotics and Automation, Engineering School, Democritus University of Thrace, Greece;3. Automation and Robotics Section (TEC‐MMA), European Space Agency, The Netherlands |
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| Abstract: | In spite of the good performance of space exploratory missions, open issues still await to be solved. In autonomous or composite semi‐autonomous exploration of planetary land surfaces, rover localization is such an issue. The rovers of these missions (e.g., the MER and MSL) navigate relatively to their landing spot, ignoring their exact position on the coordinate system defined for the celestial body they explore. However, future advanced missions, like the Mars Sample Return, will require the localization of rovers on a global frame rather than the arbitrarily defined landing frame. In this paper we attempt to retrieve the absolute rover's location by identifying matching Regions of Interest (ROIs) between orbital and land images. In particular, we propose a system comprising two parts, an offline and an onboard one, which functions as follows: in advance of the mission a Global ROI Network (GN) is built offline by investigating the satellite images near the predicted touchdown ellipse, while during the mission a Local ROI Network (LN) is constructed counting on the images acquired by the vision system of the rover along its traverse. The last procedure relies on the accurate VO‐based relative rover localization. The LN is then paired with the GN through a modified 2D DARCES algorithm. The system has been assessed on real data collected by the ESA at the Atacama desert. The results demonstrate the system's potential to perform absolute localization, on condition that the area includes discriminative ROIs. The main contribution of this work is the enablement of global localization performed on contemporary rovers without requiring any additional hardware, such as long range LIDARs. |
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| Keywords: | autonomous exploration DARCES feature extraction global localization learning machine learning mobile robotics motion estimation perception planetary exploration planetary robotics position estimation robotics vision vision‐based navigation |
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