Abstract
Due to the recent success of affordable RGBD cameras, solutions to the Visual Simultaneous Localization and Mapping (VSLAM) problem has experienced a huge leap. To enable accurate mapping solutions, most of the proposed solutions expect static environments. Thinking of industrial applications, there is no guarantee for static environments. The SLAM algorithm has to cope with moving objects like human beings. We present an approach to detect moving objects in RGBD camera images. The approach is based on point cloud and image filtering techniques. We present test results using publicly available datasets. We further show the performance and influence of the algorithm on mapping and on the accuracy of a visual SLAM system.
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Klüssendorff, J.H., Ehlers, K., Maehle, E. (2016). Visual Mapping in Light-Crowded Indoor Environments. In: Menegatti, E., Michael, N., Berns, K., Yamaguchi, H. (eds) Intelligent Autonomous Systems 13. Advances in Intelligent Systems and Computing, vol 302. Springer, Cham. https://doi.org/10.1007/978-3-319-08338-4_66
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DOI: https://doi.org/10.1007/978-3-319-08338-4_66
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