A simple, robust and fast method for the perspective-n-point Problem

Abstract In this work, we present a simple, robust and fast method to the perspective-n-point (PnP) problem for determining the position and orientation of a calibrated camera from known reference points. Our method transfers the pose estimation problem into an optimal problem, and only needs to solve a seventh-order and a fourth-order univariate polynomial, respectively, which makes the processes more easily understood and significantly improves the performance. Additionally, the number of solutions of the proposed method is substantially smaller than existing methods. Experiment results show that the proposed method can stably handle all 3D point configurations, including the ordinary 3D case, the quasi-singular case, and the planar case, and it offers accuracy comparable or better than that of the state-of-art methods, but at much lower computational cost.

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

[2]  Francesc Moreno-Noguer,et al.  Exhaustive Linearization for Robust Camera Pose and Focal Length Estimation , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Gregory D. Hager,et al.  Fast and Globally Convergent Pose Estimation from Video Images , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

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

[5]  V. Lepetit,et al.  EPnP: An Accurate O(n) Solution to the PnP Problem , 2009, International Journal of Computer Vision.

[6]  Laurent Kneip,et al.  A Direct Least-Squares Solution to the PnP Problem with Unknown Focal Length , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[7]  Kenji Terabayashi,et al.  A simple indoor self-localization system using infrared LEDs , 2009, 2009 Sixth International Conference on Networked Sensing Systems (INSS).

[8]  Joel A. Hesch,et al.  A Direct Least-Squares (DLS) method for PnP , 2011, 2011 International Conference on Computer Vision.

[9]  L Davidroper,et al.  Numerical recipes: The art of scientific computing , 1987 .

[10]  S. Umeyama,et al.  Least-Squares Estimation of Transformation Parameters Between Two Point Patterns , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[11]  Warren E. Dixon,et al.  Single Camera Structure and Motion , 2012, IEEE Transactions on Automatic Control.

[12]  Shiqi Li,et al.  A Robust O(n) Solution to the Perspective-n-Point Problem , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  H. M. Karara,et al.  Direct Linear Transformation from Comparator Coordinates into Object Space Coordinates in Close-Range Photogrammetry , 2015 .

[14]  Yubin Kuang,et al.  Revisiting the PnP Problem: A Fast, General and Optimal Solution , 2013, 2013 IEEE International Conference on Computer Vision.

[15]  Shiqi Li,et al.  A Stable Direct Solution of Perspective-Three-Point Problem , 2011, Int. J. Pattern Recognit. Artif. Intell..

[16]  Hongdong Li,et al.  UPnP: An Optimal O(n) Solution to the Absolute Pose Problem with Universal Applicability , 2014, ECCV.

[17]  David G. Hull.  Introduction to Optimization , 2003 .

[18]  Gaku Nakano,et al.  A Versatile Approach for Solving PnP, PnPf, and PnPfr Problems , 2016, ECCV.

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

[20]  J. Ryan,et al.  UAV photogrammetry and structure from motion to assess calving dynamics at Store Glacier, a large outlet draining the Greenland ice sheet , 2015 .

[21]  Zuzana Kukelova,et al.  Automatic Generator of Minimal Problem Solvers , 2008, ECCV.

[22]  Stergios I. Roumeliotis,et al.  Vision-Aided Inertial Navigation for Spacecraft Entry, Descent, and Landing , 2009, IEEE Transactions on Robotics.

[23]  Kostas Daniilidis,et al.  Linear Pose Estimation from Points or Lines , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Éric Marchand,et al.  Pose Estimation for Augmented Reality: A Hands-On Survey , 2016, IEEE Transactions on Visualization and Computer Graphics.

[25]  Byung-Ju Yi,et al.  Multi-group localization problem of service robots based on hybrid external localization algorithm with application to shopping mall environment , 2016, Intell. Serv. Robotics.