Abstract
This paper describes a new data partitioning technique for used with a visual SLAM system. Combined with the existing SLAM system, the technique surveys areas to which the input image might belong to. It then retrieves matched images from such areas. The proposed technique can run in parallel with a normal SLAM system, such as FAB-MAP, in an unsupervised and incremental manner. We also introduce usage of Position-Invariant Robust Features (PIRFs) to make the system robust to dynamic changes in scenes such as moving objects. Combining our technique with normal SLAM can markedly increase the localization recall rate. Experiment results showed that the FAB-MAP result recall rate can increase to 30% at the same precision.
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© 2009 Springer-Verlag Berlin Heidelberg
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Tongprasit, N., Kawewong, A., Hasegawa, O. (2009). Data Partitioning Technique for Online and Incremental Visual SLAM. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5863. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10677-4_88
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DOI: https://doi.org/10.1007/978-3-642-10677-4_88
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10676-7
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