Abstract
This paper proposes a real-time RGB-D (red-green-blue depth) 3D SLAM (simultaneous localization and mapping) system. Kinect style sensors give RGB-D data which contains 2D image and per-pixel depth information. 6-DOF (degree-of-freedom) visual odometry is obtained through the 3D-RANSAC (three-dimensional random sample consensus) algorithm with image features and depth information. For speed up extraction of features, parallel computation is performed on a GPU (graphics processing unit) processor. After a feature manager detects loop closure, a graph-based SLAM algorithm optimizes trajectory of the sensor and 3D map. Experimental results show the processing rate over 20 Hz.
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© 2013 Springer-Verlag Berlin Heidelberg
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Lee, D., Kim, H., Myung, H. (2013). 2D Image Feature-Based Real-Time RGB-D 3D SLAM. In: Kim, JH., Matson, E., Myung, H., Xu, P. (eds) Robot Intelligence Technology and Applications 2012. Advances in Intelligent Systems and Computing, vol 208. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37374-9_47
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DOI: https://doi.org/10.1007/978-3-642-37374-9_47
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