Abstract This work is concerned with camera pose estimation from correspondences of 3D 2D lines, i. e. with the Perspective-n-Line (PnL) problem. We focus on large line sets, which can be efficiently solved by methods using linear formulation of PnL. We propose a novel method “DLT-Combined-Lines” based on the Direct Linear Transformation (DLT) algorithm, which benefits from a new combination of two existing DLT methods for pose estimation. The method represents 2D structure by lines, and 3D structure by both points and lines. The redundant 3D information reduces the minimum required line correspondences to 5. A cornerstone of the method is a combined projection matrix estimated by the DLT algorithm. It contains multiple estimates of camera rotation and translation, which can be recovered after enforcing constraints of the matrix. Multiplicity of the estimates is exploited to improve the accuracy of the proposed method. For large line sets (10 and more), the method is comparable to the state-of-the-art in accuracy of orientation estimation. It achieves state-of-the-art accuracy in estimation of camera position and it yields the smallest reprojection error under strong image noise. The method achieves top-3 results on real world data. The proposed method is also highly computationally effective, estimating the pose of 1000 lines in 12 ms on a desktop computer.
The iterative approaches consider pose estimation as a nonlinear least squares problem by iteratively minimizing specific error function, which usually has a geometrical meaning. In the early work of @cite_7 , the authors attempted to estimate the camera position and orientation separately developing a method called R . Later on, @cite_0 introduced a method called R for simultaneous estimation of camera position and orientation, and proved its superior performance to R . Recently, @cite_8 proposed two modifications of the R algorithm exploiting the uncertainty properties of line segment endpoints. Several other iterative methods are also capable of estimation of pose parameters and line correspondences, e. ,g. @cite_5 @cite_16 . They pose an orthogonal approach to the common RANSAC-based correspondence filtering and consecutive separate pose estimation.
@cite_7 @cite_8 @cite_0 @cite_5 @cite_16
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Correspondences between 2D lines in an image and 3D lines in the surrounding environment can be exploited to determine the camera's position and attitude (pose). In this paper, we introduce a novel approach to estimate the camera's pose by directly solving the corresponding least-squares problem algebraically. Specifically, the optimality conditions of the least-squares problem form a system of polynomial equations, which we efficiently solve through the eigendecomposition of a so-called multiplication matrix. Contrary to existing methods, the proposed algorithm (i) is guaranteed to find the globally optimal estimate in the least-squares sense, (ii) does not require initialization, and (iii) has computational cost only linear in the number of measurements. The superior performance of the proposed algorithm compared to previous approaches is demonstrated through extensive simulations and experiments. Correspondences between 3D lines and their 2D images captured by a camera are often used to determine position and orientation of the camera in space. In this work, we propose a novel algebraic algorithm to estimate the camera pose. We parameterize 3D lines using Plucker coordinates that allow linear projection of the lines into the image. A line projection matrix is estimated using Linear Least Squares and the camera pose is then extracted from the matrix. An algebraic approach to handle mismatched line correspondences is also included. The proposed algorithm is an order of magnitude faster yet comparably accurate and robust to the state-of-the-art, it does not require initialization, and it yields only one solution. The described method requires at least 9 lines and is particularly suitable for scenarios with 25 and more lines, as also shown in the results. Abstract This paper mathematically analyzes and proposes new solutions for the problem of estimating the camera 3D location and orientation ( pose determination ) from a matched set of 3D model and 2D image landmark features. Least-squares techniques for line tokens, which minimize both rotation and translation simultaneously, are developed and shown to be far superior to the earlier techniques which solved for rotation first and then translation. However, least-squares techniques fail catastrophically when outliers (or gross errors) are present in the match data. Outliers arise frequently due to incorrect correspondences or gross errors in the 3D model. Robust techniques for pose determination are developed to handle data contaminated by fewer than 50.0 outliers. Finally, the sensitivity of pose determination to incorrect estimates of camera parameters is analyzed. It is shown that for small field of view systems, offsets in the image center do not significantly affect the location of the camera in a world coordinate system. Errors in the focal length significantly affect only the component of translation along the optical axis in the pose computation. In this study, the authors have proposed a new solution for the problem of pose estimation from a set of matched 3D model and 2D image lines. Traditional line-based pose estimation methods utilising the finite information of the observations are based on the assumption that the noises for the two endpoints of the image line segment are statistically independent. However, in this study, the authors prove that these two noises are negatively correlative when the image line segment is fitted by the least-squares technique from the noisy edge points. Moreover, the authors derive the noise model describing the probabilistic relationship between the 3D model line and their finite image observations. Based on the proposed noise model, the maximum-likelihood approach is exploited to estimate the pose parameters. The authors have carried out synthetic experiments to compare the proposed method to other pose optimisation methods in the literature. The experimental results show that the proposed methods yield a clear higher precision than the traditional methods. The authors also use real image sequences to demonstrate the performance of the proposed method.