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Optical Engineering

Real-time depth camera tracking with geometrically stable weight algorithm
Author(s): Xingyin Fu; Feng Zhu; Feng Qi; Mingming Wang
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Paper Abstract

We present an approach for real-time camera tracking with depth stream. Existing methods are prone to drift in sceneries without sufficient geometric information. First, we propose a new weight method for an iterative closest point algorithm commonly used in real-time dense mapping and tracking systems. By detecting uncertainty in pose and increasing weight of points that constrain unstable transformations, our system achieves accurate and robust trajectory estimation results. Our pipeline can be fully parallelized with GPU and incorporated into the current real-time depth camera tracking system seamlessly. Second, we compare the state-of-the-art weight algorithms and propose a weight degradation algorithm according to the measurement characteristics of a consumer depth camera. Third, we use Nvidia Kepler Shuffle instructions during warp and block reduction to improve the efficiency of our system. Results on the public TUM RGB-D database benchmark demonstrate that our camera tracking system achieves state-of-the-art results both in accuracy and efficiency.

Paper Details

Date Published: 17 March 2017
PDF: 10 pages
Opt. Eng. 56(3) 033104 doi: 10.1117/1.OE.56.3.033104
Published in: Optical Engineering Volume 56, Issue 3
Show Author Affiliations
Xingyin Fu, Shenyang Institute of Automation (China)
Feng Zhu, Shenyang Institute of Automation (China)
Feng Qi, Shenyang Institute of Automation (China)
Mingming Wang, Shenyang Institute of Automation (China)


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