A New Method for Absolute Pose Estimation with Unknown Focal Length and Radial Distortion

Cao

Tian

et al. 2023

Sensors

Self Cite

This paper proposes a new pose and focal length estimation method using two vanishing points and a known camera position. A vanishing point can determine the unit direction vector of the corresponding parallel lines in the camera frame, and as input, the unit direction vector of the corresponding parallel lines in the world frame is also known. Hence, the two units of direction vectors in camera and world frames, respectively, can be transformed into each other only through the rotation matrix that contains all the information of the camera pose. Then, two transformations can be obtained because there are two vanishing points. The two transformations of the unit direction vectors can be regarded as transformations of 3D points whose coordinates are the values of the corresponding unit direction vectors. The key point in this paper is that our problem with vanishing points is converted to rigid body transformation with 3D–3D point correspondences, which is the usual form in the PnP (perspective-n-point) problem. Additionally, this point simplifies our problem of pose estimation. In addition, in the camera frame, the camera position and two vanishing points can form two lines, respectively, and the angle between the two lines is equal to the angle between the corresponding two sets of parallel lines in the world frame. When using this geometric constraint, the focal length can be estimated quickly. The solutions of pose and focal length are both unique. The experiments show that our proposed method has good performances in numerical stability, noise sensitivity and computational speed with synthetic data and real scenarios and also has strong robustness to camera position noise.

Section: Introductionmentioning

confidence: 99%

Pose and Focal Length Estimation Using Two Vanishing Points with Known Camera Position

Cao

Tian

et al. 2023

Sensors

Self Cite

“…The key steps of stereo vision are calibration and intersection measurement. In some cases, such as VMCs, stereo vision uses large field of view [12]- [14], and has the characteristics of long distance from camera to target and wide measuring range, which increase the difficulty of calibration and is unable to place 3D control points arbitrarily, which will affect the calibration accuracy and then affect the measuring accuracy. What's more, the distance from the target area to 3D control point area has a great influence on the measuring accuracy of intersection measurement.…”

Section: Introductionmentioning

confidence: 99%

Analysis of measuring accuracy for planar and non-planar scenes in photogrammetry

Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022)

et al. 2023

Photogrammetry with stereo vision is widely used in computer vision and SLAM (simultaneous localization and mapping), whose key steps are calibration and intersection measurement. Calibration is to obtain the intrinsic and extrinsic parameters, including the principal point, focal length and pose. Intersection measurement is to obtain the 3D information after calibration, including position, velocity and rotation. In some cases, such as visual monitoring cameras (VMCs), photogrammetry uses large field of view, and has the characteristics of long distance from camera to target and wide measuring range, which increase the difficulty of calibration and is unable to place 3D control points arbitrarily. What's more, the distance from the target area to 3D control point area has a great influence on the measuring accuracy of intersection measurement. In this paper, we proposed a new method to place 3D control points, including planar and non-planar scenes and this method can distinguish the two scenes. Then the planar and non-planar methods can be used to calibrate in different cases respectively. In addition, we analyzed the layout of 3D control points to obtain relation between the measuring accuracy and the distance from the target area to 3D control point area. Experimental results show the longer the distance, the greater the measuring error in synthetic data and real images, and to improve the measuring accuracy, the 3D control points should be planar or non-planar strictly, not quasi-planar.

“…When there are at least six 2D–3D point correspondences, the camera pose can be estimated linearly, which is called DLT (Direct Linear Transformation) [ 32 , 33 ]. In addition, there are some methods that use partial parameters of pose as prior knowledge, such as the known vertical direction [ 34 , 35 , 36 ] or camera position [ 37 , 38 ], and these methods can use fewer 2D–3D point correspondences, simplify the problem and increase the efficiency.…”

Section: Introductionmentioning

confidence: 99%

Fast and Accurate Pose Estimation with Unknown Focal Length Using Line Correspondences

Zhang

et al. 2022

Sensors

Self Cite

Estimating camera pose is one of the key steps in computer vison, photogrammetry and SLAM (Simultaneous Localization and Mapping). It is mainly calculated based on the 2D–3D correspondences of features, including 2D–3D point and line correspondences. If a zoom lens is equipped, the focal length needs to be estimated simultaneously. In this paper, a new method of fast and accurate pose estimation with unknown focal length using two 2D–3D line correspondences and the camera position is proposed. Our core contribution is to convert the PnL (perspective-n-line) problem with 2D–3D line correspondences into an estimation problem with 3D–3D point correspondences. One 3D line and the camera position in the world frame can define a plane, the 2D line projection of the 3D line and the camera position in the camera frame can define another plane, and actually the two planes are the same plane, which is the key geometric characteristic in this paper’s estimation of focal length and pose. We establish the transform between the normal vectors of the two planes with this characteristic, and this transform can be regarded as the camera projection of a 3D point. Then, the pose estimation using 2D–3D line correspondences is converted into pose estimation using 3D–3D point correspondences in intermediate frames, and, lastly, pose estimation can be finished quickly. In addition, using the property whereby the angle between two planes is invariant in both the camera frame and world frame, we can estimate the camera focal length quickly and accurately. Experimental results show that our proposed method has good performance in numerical stability, noise sensitivity and computational speed with synthetic data and real scenarios, and has strong robustness to camera position noise.