Abstract
Trajectory estimation is of pivotal importance for mobile robots. Visual Odometry (VO) allows localizing a robot from passive vision data in frame-to-frame fashion. The VO problem can be solved in different ways, hence an evaluation of these algorithms in the context of real benchmark data is interesting. We focus on feature-based n-point methods based on RGB images. These methods used in monocular vision allow for camera rotation estimation, but only a few of them provide translation estimates up to the unknown scale. In the context of the use of commodity RGB-D cameras, we also compare these methods with the Kabsch algorithm, which uses full depth information.
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Kostusiak, A. (2018). Frame-to-Frame Visual Odometry: The Importance of Local Transformations. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds) Proceedings of the 10th International Conference on Computer Recognition Systems CORES 2017. CORES 2017. Advances in Intelligent Systems and Computing, vol 578. Springer, Cham. https://doi.org/10.1007/978-3-319-59162-9_37
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DOI: https://doi.org/10.1007/978-3-319-59162-9_37
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