Minimal solutions for the multi-camera pose estimation problem

In this paper, we propose a novel formulation to solve the pose estimation problem of a calibrated multi-camera system. The non-central rays that pass through the 3D world points and multi-camera system are elegantly represented as Plücker lines. This allows us to solve for the depth of the points along the Plücker lines with a minimal set of three-point correspondences. We show that the minimal solution for the depth of the points along the Plücker lines is an eight-degree polynomial that gives up to eight real solutions. The coordinates of the 3D world points in the multi-camera frame are computed from the known depths. Consequently, the pose of the multi-camera system, i.e. the rigid transformation between the world and multi-camera frames can be obtained from absolute orientation. We also derive a closed-form minimal solution for the absolute orientation. This removes the need for the computationally expensive singular value decompositions during the evaluations of the possible solutions for the depths. We identify the correct solution and do robust estimation with RANSAC. Finally, the solution is further refined by including all the inlier correspondences in a nonlinear refinement step. We verify our approach by showing comparisons with other existing approaches and results from large-scale real-world datasets.

[1]  Hongdong Li,et al.  A linear approach to motion estimation using generalized camera models , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Marc Pollefeys,et al.  CamOdoCal: Automatic intrinsic and extrinsic calibration of a rig with multiple generic cameras and odometry , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[3]  David Nistér,et al.  A Minimal Solution to the Generalised 3-Point Pose Problem , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[4]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[5]  Frank Dellaert,et al.  A multi-camera 6-DOF pose tracker , 2004, Third IEEE and ACM International Symposium on Mixed and Augmented Reality.

[6]  Axel Pinz,et al.  Globally Optimal O(n) Solution to the PnP Problem for General Camera Models , 2008, BMVC.

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

[8]  Marc Pollefeys,et al.  Structureless pose-graph loop-closure with a multi-camera system on a self-driving car , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  David Nistér,et al.  Scalable Recognition with a Vocabulary Tree , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[10]  David A. Cox,et al.  Ideals, Varieties, and Algorithms: An Introduction to Computational Algebraic Geometry and Commutative Algebra, 3/e (Undergraduate Texts in Mathematics) , 2007 .

[11]  Roland Siegwart,et al.  Using multi-camera systems in robotics: Efficient solutions to the NPnP problem , 2013, 2013 IEEE International Conference on Robotics and Automation.

[12]  Donal O'Shea,et al.  Ideals, varieties, and algorithms - an introduction to computational algebraic geometry and commutative algebra (2. ed.) , 1997, Undergraduate texts in mathematics.

[13]  Vincent Lepetit,et al.  Accurate Non-Iterative O(n) Solution to the PnP Problem , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[14]  Rajiv Gupta,et al.  Linear pushbroom cameras , 1997 .

[15]  Luc Van Gool,et al.  Generalised Linear Pose Estimation , 2007, BMVC.

[16]  Long Quan,et al.  Linear N-Point Camera Pose Determination , 1999, IEEE Trans. Pattern Anal. Mach. Intell..

[17]  Roland Siegwart,et al.  Infrastructure-based calibration of a multi-camera rig , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[18]  Binoy Pinto,et al.  Speeded Up Robust Features , 2011 .

[19]  Robert M. Haralick,et al.  Analysis and solutions of the three point perspective pose estimation problem , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[20]  Rajiv Gupta,et al.  Linear Pushbroom Cameras , 1994, ECCV.

[21]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[22]  Marc Pollefeys,et al.  Relative Pose Estimation for a Multi-camera System with Known Vertical Direction , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[23]  Robert Pless,et al.  Using many cameras as one , 2003, 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings..

[24]  Marc Pollefeys,et al.  Motion Estimation for Self-Driving Cars with a Generalized Camera , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  Wen-Yan Chang,et al.  On pose recovery for generalized visual sensors , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.