Abstract
In the context of robotic space exploration the problem of autonomous global or absolute localization remains unsolved. Current rovers require human in the loop approaches to acquire global positioning. In this paper we assess this problem by refining our previous work in a way that advances the performance of the system while making the procedure feasible for real implementation on rovers. A map of semantic landmarks (the Global Network - GN) is extracted on an area that the rover traverses prior to the mission and, during the exploration, a Local Network (LN) is built and matched to estimate rover’s global location. We have optimized several aspects of the system: the motion estimation, the detection and classification –by benchmarking several classifiers– and we have tested the system in a Mars like scenario. With the aim to achieve realistic terms in our scenario a custom robotic platform was developed, bearing operation features similar to ESA’s ExoMars. Our results indicate that the proposed system is able to perform global localization and converges relatively fast to an accurate solution.
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Boukas, E., Polydoros, A.S., Visentin, G., Nalpantidis, L., Gasteratos, A. (2017). Global Localization for Future Space Exploration Rovers. In: Liu, M., Chen, H., Vincze, M. (eds) Computer Vision Systems. ICVS 2017. Lecture Notes in Computer Science(), vol 10528. Springer, Cham. https://doi.org/10.1007/978-3-319-68345-4_8
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DOI: https://doi.org/10.1007/978-3-319-68345-4_8
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