Abstract
A central clue for implementation of visual memory based navigation strategies relies on efficient point matching between the current image and the key images of the memory. However, the visual memory may become out of date after some times because the appearance of real-world environments keeps changing. It is thus necessary to remove obsolete information and to add new data to the visual memory over time. In this paper, we propose a method based on short-term and long term memory concepts to update the visual memory of mobile robots during navigation. The results of our experiments show that using this method improves the robustness of the localization and path-following steps.
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Courbon, J., Korrapati, H., Mezouar, Y. (2013). Visual Memory Update for Life-Long Mobile Robot Navigation. In: Lee, S., Yoon, KJ., Lee, J. (eds) Frontiers of Intelligent Autonomous Systems. Studies in Computational Intelligence, vol 466. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35485-4_4
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DOI: https://doi.org/10.1007/978-3-642-35485-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35484-7
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