Abstract
In planning navigation schemes for social robots, distinguishing between humans and other obstacles is crucial for obtaining a safe and comfortable motion. A Kinect camera is capable of fulfilling such a task but unfortunately can only deliver a limited field of view (FOV). Recently a lens that is capable of improving the Kinect’s FOV has become commercially available from Nyko. However, this lens causes a distortion in the RGB-D data, including the depth values. To address this issue, we propose a two-staged undistortion strategy. Initially, pixel locations in both RGB and depth images are corrected using an inverse radial distortion model. Next, the depth data is post-filtered using 3D point cloud analysis to diminish the noise as a result of the undistorting process and remove the ground/ceiling information. Finally, the depth values are rectified using a neural network filter based on laser-assisted training. Experimental results demonstrate the feasibility of the proposed approach for fixing distorted RGB-D data.
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Tomari, R., Kobayashi, Y., Kuno, Y. (2012). Wide Field of View Kinect Undistortion for Social Navigation Implementation. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2012. Lecture Notes in Computer Science, vol 7432. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33191-6_52
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DOI: https://doi.org/10.1007/978-3-642-33191-6_52
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-33190-9
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