Abstract
Estimation of camera motion from RGB-D images has been an active research topic in recent years. Several RGB-D visual odometry systems were reported in literature and released under open-source licenses. The objective of this contribution is to evaluate the recently published approaches to motion estimation. A publicly available dataset of RGB-D sequences with precise ground truth data is applied and results are compared and discussed. Experiments on a mobile robot used in the RoboCup@Work league are discussed as well. The system showing the best performance is capable of estimating the motion with drift as small as 1\(^{{cm}\over{s}}\) under special conditions, though it has been proven to be robust against shakey motion and moderately non-static scenes.
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Alexandrov, S., Herpers, R. (2014). Evaluation of Recent Approaches to Visual Odometry from RGB-D Images. In: Behnke, S., Veloso, M., Visser, A., Xiong, R. (eds) RoboCup 2013: Robot World Cup XVII. RoboCup 2013. Lecture Notes in Computer Science(), vol 8371. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44468-9_39
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DOI: https://doi.org/10.1007/978-3-662-44468-9_39
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