Online dense local 3D world reconstruction from stereo image sequences

This paper describes an online 3D reconstruction system from stereo image sequences to obtain a dense local world model for robot navigation. The proposed method consists of three components: 1) stereo depth map calculation, 2) correspondence calculation in time sequential images by tracking raw image features, 3) 6DOF camera motion estimation by RANSAC and integrate depth map into 3D reconstructed model. We examined and evaluated our method in a motion capture environment for comparison. Finally experimental results of a humanoid robot H7 are denoted.

[1]  Takeo Kanade,et al.  A sequential factorization method for recovering shape and motion from image streams , 1997, IEEE Trans. Pattern Anal. Mach. Intell..

[2]  R. Bolles,et al.  Spatiotemporal Consistency Checking of Passive Renge Data , 1993 .

[3]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[4]  Frank Dellaert,et al.  Structure from motion without correspondence , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[5]  Takeo Kanade,et al.  A unified factorization algorithm for points, line segments and planes with uncertainty models , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[6]  H. Hartley Maximum Likelihood Estimation from Incomplete Data , 1958 .

[7]  Pascal Fua,et al.  A parallel stereo algorithm that produces dense depth maps and preserves image features , 1993, Machine Vision and Applications.

[8]  Alonzo Kelly,et al.  Toward Reliable Off Road Autonomous Vehicles Operating in Challenging Environments , 2006, Int. J. Robotics Res..

[9]  Laurent Moll,et al.  Real time correlation-based stereo: algorithm, implementations and applications , 1993 .

[10]  Takeo Kanade,et al.  A Multibody Factorization Method for Independently Moving Objects , 1998, International Journal of Computer Vision.

[11]  David A. Forsyth,et al.  Bayesian structure from motion , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[12]  S. Ullman,et al.  The interpretation of visual motion , 1977 .

[13]  Masayuki Inaba,et al.  Design and implementation of software research platform for humanoid robotics: H6 , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[14]  J. Chestnutt,et al.  Planning Biped Navigation Strategies in Complex Environments , 2003 .

[15]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[16]  Sebastian Thrun,et al.  Large-Scale Robotic 3-D Mapping of Urban Structures , 2004, ISER.

[17]  Takeo Kanade,et al.  Shape and motion from image streams under orthography: a factorization method , 1992, International Journal of Computer Vision.

[18]  C Tomasi,et al.  Shape and motion from image streams: a factorization method. , 1992, Proceedings of the National Academy of Sciences of the United States of America.