Results 1 
3 of
3
gDLS: A Scalable Solution to the Generalized Pose and Scale Problem
"... Abstract. In this work, we present a scalable leastsquares solution for computing a seven degreeoffreedom similarity transform. Our method utilizes the generalized camera model to compute relative rotation, translation, and scale from four or more 2D3D correspondences. In particular, structure ..."
Abstract

Cited by 1 (1 self)
 Add to MetaCart
Abstract. In this work, we present a scalable leastsquares solution for computing a seven degreeoffreedom similarity transform. Our method utilizes the generalized camera model to compute relative rotation, translation, and scale from four or more 2D3D correspondences. In particular, structure and motion estimations from monocular cameras lack scale without specific calibration. As such, our methods have applications in loop closure in visual odometry and registering multiple structure from motion reconstructions where scale must be recovered. We formulate the generalized pose and scale problem as a minimization of a least squares cost function and solve this minimization without iterations or initialization. Additionally, we obtain all minima of the cost function. The order of the polynomial system that we solve is independent of the number of points, allowing our overall approach to scale favorably. We evaluate our method experimentally on synthetic and real datasets and demonstrate that our methods produce higher accuracy similarity transform solutions than existing methods. 1
Computing Similarity Transformations from Only Image Correspondences
"... We propose a novel solution for computing the relative pose between two generalized cameras that includes reconciling the internal scale of the generalized cameras. This approach can be used to compute a similarity transformation between two coordinate systems, making it useful for loop closure in ..."
Abstract
 Add to MetaCart
(Show Context)
We propose a novel solution for computing the relative pose between two generalized cameras that includes reconciling the internal scale of the generalized cameras. This approach can be used to compute a similarity transformation between two coordinate systems, making it useful for loop closure in visual odometry and registering multiple structure from motion reconstructions together. In contrast to alternative similarity transformation methods, our approach uses 2D2D image correspondences thus is not subject to the depth uncertainty that often arises with 3D points. We utilize a known vertical direction (which may be easily obtained from IMU data or vertical vanishing point detection) of the generalized cameras to solve the generalized relative pose and scale problem as an efficient Quadratic Eigenvalue Problem. To our knowledge, this is the first method for computing similarity transformations that does not require any 3D information. Our experiments on synthetic and real data demonstrate that this leads to improved performance compared to methods that use 3D3D or 2D3D correspondences, especially as the depth of the scene increases. 1.
A Minimal Solution to the Rolling Shutter Pose Estimation Problem
"... Abstract — Artefacts that are present in images taken from a moving rolling shutter camera degrade the accuracy of absolute pose estimation. To alleviate this problem, we introduce an addition linear velocity in the camera projection matrix to approximate the motion of the rolling shutter camera. In ..."
Abstract
 Add to MetaCart
(Show Context)
Abstract — Artefacts that are present in images taken from a moving rolling shutter camera degrade the accuracy of absolute pose estimation. To alleviate this problem, we introduce an addition linear velocity in the camera projection matrix to approximate the motion of the rolling shutter camera. In particular, we derive a minimal solution using the Gröbner Basis that solves for the absolute pose as well as the motion of a rolling shutter camera. We show that the minimal problem requires 5point correspondences and gives up to 8 real solutions. We also show that our formulation can be extended to use more than 5point correspondences. We use RANSAC to robustly get all the inliers. In the final step, we relax the linear velocity assumption and do a nonlinear refinement on the full motion, i.e. linear and angular velocities, and pose of the rolling shutter camera with all the inliers. We verify the feasibility and accuracy of our algorithm with both simulated and realworld datasets. I.