Linear N-Point Camera Pose Determination
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast and Globally Convergent Pose Estimation from Video Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Global Optimization with Polynomials and the Problem of Moments
SIAM Journal on Optimization
Linear Pose Estimation from Points or Lines
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple View Geometry in Computer Vision
Multiple View Geometry in Computer Vision
Convex Optimization
Robust Pose Estimation from a Planar Target
IEEE Transactions on Pattern Analysis and Machine Intelligence
Globally Optimal Estimates for Geometric Reconstruction Problems
International Journal of Computer Vision
EPnP: An Accurate O(n) Solution to the PnP Problem
International Journal of Computer Vision
Global Optimization through Rotation Space Search
International Journal of Computer Vision
GloptiPoly 3: moments, optimization and semidefinite programming
Optimization Methods & Software - GLOBAL OPTIMIZATION
An efficient two-step solution for vision-based pose determination of a parallel manipulator
Robotics and Computer-Integrated Manufacturing
3D model based tracking for omnidirectional vision: A new spherical approach
Robotics and Autonomous Systems
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In this paper we present a convex relaxation method that globally solves for the camera position and orientation given a set of image pixel measurements associated with a scene of reference points of known three-dimensional positions. The approach formulates the pose optimization problem as a semidefinite positive relaxation (SDR) program. A comprehensive comparative performance analysis, carried out in the computer simulations section, demonstrates the superior performance of the relaxation method over existing approaches. The computational complexity of the method is O(n), where n is the number of reference points, and is applicable to both coplanar and non-coplanar reference point configurations. The average run-time recorded is 50ms for an average point count of 100.