Research
Research Divisions
Research Progress
Achievements
Research Programs
Location: Home>Research>Research Progress
Real-time depth camera tracking with geometrically stable weight algorithm
Author: Update times: 2017-12-07                          | Print | Close | Text Size: A A A
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.

 

This work was published on Optical Engineering,2017,56(3):1-10.titled  Real-time depth camera tracking with geometrically stable weight algorithm.

Copyright © 2003 - 2013. Shenyang Institute of Automation (SIA), Chinese Academy of Sciences
All rights reserved. Reproduction in whole or in part without permission is prohibited.
Phone: 86 24 23970012 Email: siamaster@sia.cn