24 January 2011 Accelerating robust 3D pose estimation utilizing a graphics processing unit
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A spin-image pose estimation algorithm is an accurate method for estimating pose of three-dimensional objects while being both robust to clutter and sensor noise. Unfortunately, the algorithm has a high computational complexity, thus preventing its use in applications that require a robotic system to interact with a dynamic environment. Upon inspection, the spin-image algorithm can be broken down into five portions where a single portion called spin-image matching commands 96% of the computation time in estimating pose. Because, the matching of individual spin-images can be performed independently regardless of order, this portion of the algorithm is ideal for the massively parallel architecture of the graphics processing unit (GPU). This paper introduces a GPU implementation of the spin-image matching portion of the spin-image algorithm whick makes no modifications to the spin-image algorithm, thus no compromising its robustness and accuracy. This implementation results in a speed-up in spin-image matching of 515x and total algorithmic speed-up of 24.6x out of a theoretical maximum of 26.0x over a MATLAB implementation. This GPU implementation extends the use of the spin-image algorithm towards practical real-time robotic applications.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Adam R. Gerlach, Bruce K. Walker, "Accelerating robust 3D pose estimation utilizing a graphics processing unit", Proc. SPIE 7878, Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, 78780V (24 January 2011); doi: 10.1117/12.876713; https://doi.org/10.1117/12.876713

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