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http://dx.doi.org/10.3745/KTSDE.2015.4.4.187

Robust Real-Time Visual Odometry Estimation for 3D Scene Reconstruction  

Kim, Joo-Hee (경기대학교 컴퓨터과학과)
Kim, In-Cheol (경기대학교 컴퓨터과학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.4, no.4, 2015 , pp. 187-194 More about this Journal
Abstract
In this paper, we present an effective visual odometry estimation system to track the real-time pose of a camera moving in 3D space. In order to meet the real-time requirement as well as to make full use of rich information from color and depth images, our system adopts a feature-based sparse odometry estimation method. After matching features extracted from across image frames, it repeats both the additional inlier set refinement and the motion refinement to get more accurate estimate of camera odometry. Moreover, even when the remaining inlier set is not sufficient, our system computes the final odometry estimate in proportion to the size of the inlier set, which improves the tracking success rate greatly. Through experiments with TUM benchmark datasets and implementation of the 3D scene reconstruction application, we confirmed the high performance of the proposed visual odometry estimation method.
Keywords
RGB-D Images; Visual Odometry; 3D Scene Reconstruction; Feature-Based Sparse Method;
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1 S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. N ewcombe, and P. Kohli, et al., "KinectFusion: Real-time 3D Reconstruction and Interaction Using a Moving Depth Camera," Proc. of 24th Annual ACM Symp. on User Interface Software and Technology, pp.559-568, 2011.
2 A. J. Davison, I. D. Reid, N. D. Molton, and O. Stasse, "MonoSLAM: Real-Time Single Camera SLAM," IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Vol.29, No 6, pp.1052-1067, 2007.   DOI
3 C. Kerl, J. Sturm, and D. Cremers, "Robust Odometry Estimation for RGB-D Cameras," Proc. of IEEE Intl. Conf. on Robotics and Automation(ICRA), pp.3748-3754, 2013.
4 A. S. Huang, A. Bachrach, P. Henry, M. Krainin, D. Maturana, D. Fox, and N. Roy, "Visual Odometry and Mapping for Autonomous Flight Using an RGB-D Camera," Proc. of Intl. Symp. on Robotics Research(ISRR), 2011.
5 T. Whelan, M. Kaess, M. F. Fallon, H. Johannsson, J. J. Leonard, and J. B. McDonald, "Kintinuous: Spatially Extended Kinect Fusion," Proc. of RSS Workshop on RGB-D: Advanced Reasoning with Depth Cameras, 2012.
6 F. Steinbrucker, C. Kerl, and D. Cremers. "Large-Scale Multi-Resolution Surface Reconstruction from RGB-D Sequences," Proc. of IEEE Intl. Conf. on Computer Vision (ICCV), 2013.
7 J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers, "A Benchmark for the Evaluation of RGB-D SLAM System," Proc. of Intl. Conf. on Intelligent Robot System (IROS), 2012.
8 T. Whelan, H. Johannsson, M. Kaess, J. J. Leonard, and J. McDonald, "Robust Real-Time Visual Odometry for Dense RGB-D Mapping," Proc. of IEEE Intl. Conf. on In Robotics and Automation(ICRA), pp.5724-5731, 2013.
9 F. Steinbrucker, J. Sturm, and D. Cremers, "Real-Time Visual Odometry from Dense RGB-D Images," Proc. of IEEE Intl. Conf. on Computer Vision Workshops(ICCV Workshops), pp. 719-722, 2011.
10 H. Silva, E. Silva, and A. Bernardino, "Combining Sparse and Dense Methods in 6D Visual Odometry," Proc. of IEEE Intl. Conf. on Autonomous Robot Systems(Robotica), pp.1-6, 2013.
11 M. Nowicki, P. Skrzypezynski, "Combining Photometric and Depth Data for Lightweight and Robust Visual Odometry," Proc. of IEEE European Conf. on Mobile Robots(ECMR), pp.125-130, 2013.