Abstract
Absolute pose estimation from corrupted point correspondences is typically a problem of estimating parameters from outlier-contaminated data. Conventionally, for a fixed dimensionality d and the number of measurements N, a robust estimation problem cannot be solved exactly faster than O ( N d ) . Furthermore, it is almost impossible to remove d from the exponent of the runtime of a globally optimal algorithm. However, absolute pose estimation is a geometric parameter estimation problem, and thus has special constraints. In this paper, we consider pairwise constraints and propose a novel algorithm utilizing global optimization method Branch-and-Bound (BnB) for solving the absolute pose estimation problem. Concretely, we first decouple the rotation and the translation subproblems by utilizing the pairwise constraints, and then we solve the rotation subproblem using the BnB algorithm. Lastly, we estimate the translation based on the optimal rotation by using another BnB algorithm. The proposed algorithm has an approximately linear complexity in the number of correspondences at a given outlier ratio. The advantages of our method were demonstrated via thorough testing on both synthetic and real-world data.
Highlights
Camera pose estimation is a critical and fundamental problem in computer vision [1], robotics [2], photogrammetry [3,4], and many other related areas
We introduce a novel robust and global solution to the absolute pose estimation problem, called Robust and Global PnP (RGPnP)
The camera pose problem has been studied for more than a century, and there is a large body of literature on the absolute pose estimation problem [10]
Summary
Camera pose estimation is a critical and fundamental problem in computer vision [1], robotics [2], photogrammetry [3,4], and many other related areas. The absolute pose estimation problem, i.e., the problem of estimating the pose parameters (rotation and translation) given certain observations (3D points and 2D points), is a typical parameter estimation problem [13] ( a fitting problem [14]) This problem has been studied for more than a century, and researchers have proposed many methods [15,16,17,18] to improve the solution speed, accuracy, and robustness to outliers. We can efficiently obtain an optimal joint solution to the robust pose estimation problem Even though the obtained optimal rotation and translation are not necessarily globally optimal to the joint problem, our proposed method still obtains a satisfactory solution, which is more robust than existing heuristic methods, in real applications. The decoupling scheme contributes by reducing the computational complexity of BnB significantly
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