Rapid 3D registration using local subtree caching in iterative closest point (ICP) algorithm

Ryan Uhlenbrock; Kyungnam Kim; Heiko Hoffmann; Jean Dolne

doi:10.1117/12.2276428

6 September 2017 Rapid 3D registration using local subtree caching in iterative closest point (ICP) algorithm

Ryan Uhlenbrock, Kyungnam Kim, Heiko Hoffmann, Jean Dolne

Proceedings Volume 10410, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2017; 104100J (2017) https://doi.org/10.1117/12.2276428
Event: SPIE Optical Engineering + Applications, 2017, San Diego, California, United States

Abstract

This paper describes a method for continuous 3D registration of an object using a 3D sensor and model of the object, significantly speeding up an iterative alignment method by using a 2D array cache. The cache stores local subtrees in a kd-tree search to initialize the processing of subsequent data. The cache is spatially structured to match the projection of the 3D space into the sensor’s field of view, and automatically adapts when points cross discontinuities or occlusion boundaries. Experiments in a simulated 3D tracking and relative maneuvering scenario demonstrate the computational speedup benefits of local subtree caching.

Citation Download Citation

Ryan Uhlenbrock, Kyungnam Kim, Heiko Hoffmann, and Jean Dolne "Rapid 3D registration using local subtree caching in iterative closest point (ICP) algorithm", Proc. SPIE 10410, Unconventional and Indirect Imaging, Image Reconstruction, and Wavefront Sensing 2017, 104100J (6 September 2017); https://doi.org/10.1117/12.2276428

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