Transforming camera geometry to a virtual downward-looking camera: robust ego-motion estimation and ground-layer detection

This paper presents a robust method to solve two coupled problems, ground-layer detection and vehicle ego-motion estimation, which appear in visual navigation. We virtually rotate the camera to the downward-looking pose in order to exploit the fact that the vehicle motion is roughly constrained to be planar motion on the ground. This camera geometry transformation together with the planar motion constraint will: 1) eliminate the ambiguity between rotational and translation ego-motion parameters, and 2) improve the Hessian matrix condition in the direct motion estimation process. The virtual downward-looking camera enables us to estimate the planar ego-motions even for small image patches. Such local measurements are then combined together, by a robust weighting scheme based on both ground plane geometry and motion compensated intensity residuals, for a global ego-motion estimation and ground plane detection. We demonstrate the effectiveness of our method by experiments on both synthetic and real data.

[1]  Allan D. Jepson,et al.  Subspace methods for recovering rigid motion I: Algorithm and implementation , 2004, International Journal of Computer Vision.

[2]  Michael J. Black,et al.  Mixture models for optical flow computation , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Jitendra Malik,et al.  Motion segmentation and tracking using normalized cuts , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[4]  Michal Irani,et al.  Recovery of ego-motion using image stabilization , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[5]  K. Hanna.  Direct multi-resolution estimation of ego-motion and structure from motion , 1991, Proceedings of the IEEE Workshop on Visual Motion.

[6]  Mubarak Shah,et al.  Object based segmentation of video using color, motion and spatial information , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[7]  Harpreet S. Sawhney,et al.  Layered representation of motion video using robust maximum-likelihood estimation of mixture models and MDL encoding , 1995, Proceedings of IEEE International Conference on Computer Vision.

[8]  Michal Irani,et al.  Detecting and Tracking Multiple Moving Objects Using Temporal Integration , 1992, ECCV.

[9]  Hans-Hellmut Nagel,et al.  The coupling of rotation and translation in motion estimation of planar surfaces , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[10]  Richard Szeliski,et al.  An Integrated Bayesian Approach to Layer Extraction from Image Sequences , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Yair Weiss,et al.  Smoothness in layers: Motion segmentation using nonparametric mixture estimation , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[12]  Amnon Shashua,et al.  A robust method for computing vehicle ego-motion , 2000, Proceedings of the IEEE Intelligent Vehicles Symposium 2000 (Cat. No.00TH8511).

[13]  Bill Triggs,et al.  Autocalibration from Planar Scenes , 1998, ECCV.

[14]  Yiannis Aloimonos.  Harmonic Computational Geometry: A new tool for visual correspondence , 2002, BMVC.

[15]  Edward H. Adelson,et al.  Representing moving images with layers , 1994, IEEE Trans. Image Process..

[16]  P. Perona,et al.  Recursive 3-D Visual Motion Estimation Using Subspace Constraints , 1997, International Journal of Computer Vision.

[17]  P. Anandan,et al.  Hierarchical Model-Based Motion Estimation , 1992, ECCV.

[18]  Berthold K. P. Horn,et al.  Direct methods for recovering motion , 1988, International Journal of Computer Vision.

[19]  Edward H. Adelson,et al.  A unified mixture framework for motion segmentation: incorporating spatial coherence and estimating the number of models , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Richard Szeliski,et al.  An integrated Bayesian approach to layer extraction from image sequences , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[21]  Gilad Adiv,et al.  Determining Three-Dimensional Motion and Structure from Optical Flow Generated by Several Moving Objects , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Carlo Tomasi,et al.  Comparison of approaches to egomotion computation , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[23]  Thomas S. Huang,et al.  Theory of Reconstruction from Image Motion , 1992 .

[24]  Gilad Adiv,et al.  Inherent Ambiguities in Recovering 3-D Motion and Structure from a Noisy Flow Field , 1989, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Shahriar Negahdaripour,et al.  IEEE Transactions on Pattern Analysis and Machine Intelligence , 2004 .

[26]  Harpreet S. Sawhney,et al.  Correlation-based estimation of ego-motion and structure from motion and stereo , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[27]  Andrew Zisserman,et al.  Self-Calibration from Image Triplets , 1996, ECCV.

[28]  Takeo Kanade,et al.  A robust subspace approach to layer extraction , 2002, Workshop on Motion and Video Computing, 2002. Proceedings..

[29]  Takeo Kanade,et al.  A subspace approach to layer extraction , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[30]  Richard I. Hartley,et al.  In defence of the 8-point algorithm , 1995, Proceedings of IEEE International Conference on Computer Vision.