Abstract
This paper presents an approach to perform data association in a monocular visual SLAM context. The proposed approach is designed to avoid the detection of false associations by means of RANSAC, and is well suited to help in localizing a robot in underwater environments. Experimental results embed the data association in a trajectory-based SLAM in order to evaluate its benefits when localizing an underwater robot. Qualitative and quantitative results are shown evaluating the effects of dead reckoning noise and the frequency of the SLAM updates.
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CITATION STYLE
Burguera, A., González, Y., & Oliver, G. (2014). RANSAC based data association for underwater visual SLAM. In Advances in Intelligent Systems and Computing (Vol. 252, pp. 3–16). Springer Verlag. https://doi.org/10.1007/978-3-319-03413-3_1
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