Imaging Components, Systems, and Processing

Real-time depth camera tracking with geometrically stable weight algorithm

[+] Author Affiliations
Xingyin Fu, Mingming Wang

Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

University of Chinese Academy of Sciences, Beijing, China

Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, China

The Key Lab of Image Understanding and Computer Vision, Liaoning Province, China

Feng Zhu, Feng Qi

Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China

Key Laboratory of Opto-Electronic Information Processing, Chinese Academy of Sciences, Shenyang, China

The Key Lab of Image Understanding and Computer Vision, Liaoning Province, China

Opt. Eng. 56(3), 033104 (Mar 17, 2017). doi:10.1117/1.OE.56.3.033104
History: Received October 1, 2016; Accepted March 1, 2017
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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.

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© 2017 Society of Photo-Optical Instrumentation Engineers

Citation

Xingyin Fu ; Feng Zhu ; Feng Qi and Mingming Wang
"Real-time depth camera tracking with geometrically stable weight algorithm", Opt. Eng. 56(3), 033104 (Mar 17, 2017). ; http://dx.doi.org/10.1117/1.OE.56.3.033104


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