Abstract
Abstract. To solve the problem of relative camera pose estimation, a method using optimization with respect to the manifold is proposed. Firstly from maximum-a-posteriori (MAP) model to nonlinear least squares (NLS) model, the general state estimation model using optimization is derived. Then the camera pose estimation model is applied to the general state estimation model, while the parameterization of rigid body transformation is represented by Lie group/algebra. The jacobian of point-pose model with respect to Lie group/algebra is derived in detail and thus the optimization model of rigid body transformation is established. Experimental results show that compared with the original algorithms, the approaches with optimization can obtain higher accuracy both in rotation and translation, while avoiding the singularity of Euler angle parameterization of rotation. Thus the proposed method can estimate relative camera pose with high accuracy and robustness.
Highlights
There are three kinds of relative camera pose estimation models: 2D-2D, 3D-2D, and 3D-3D, whereas the kind of 3D-2D model is the most widely used in photogrammetry, incremental structure-from-motion (SFM), visual simultaneous localization and mapping (V-SLAM), augmented reality, autonomous navigation and so on
We present an approach for relative camera pose estimation using optimization method with respect to the Lie group, which can avoid the singularity of Euler angle parameterization of rotation, and make the optimization method such as Gauss-Newton or Levenberg-Marquardt
MAP estimation reduces to maximum likelihood estimation (MLE)
Summary
There are three kinds of relative camera pose estimation models: 2D-2D, 3D-2D, and 3D-3D, whereas the kind of 3D-2D model is the most widely used in photogrammetry, incremental structure-from-motion (SFM), visual simultaneous localization and mapping (V-SLAM), augmented reality, autonomous navigation and so on. The state-of-the-art solutions on PnP problem can be divided into two types – the multi-stage method and the direct minimization method. The well-known direct linear transformation (DLT) is a multi-stage method, because it first estimates the projection matrix and extracts the camera pose. A., 2011) present a direct least squares (DLS) method for computing all solutions of the PnP problem by solving a system of three third-order polynomials. All the mentioned multi-stage methods are generally poor in accuracy, while the direct minimization methods suffer from the risk of getting trapped into local minimum. We present an approach for relative camera pose estimation using optimization method with respect to the Lie group, which can avoid the singularity of Euler angle parameterization of rotation, and make the optimization method such as Gauss-Newton or Levenberg-Marquardt
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
