9,789 research outputs found
A 4-point Algorithm for Relative Pose Estimation of a Calibrated Camera with a Known Relative Rotation Angle
Abstract-We propose an algorithm to estimate the relative camera pose using four feature correspondences and one relative rotation angle measurement. The algorithm can be used for relative pose estimation of a rigid body equipped with a camera and a relative rotation angle sensor which can be either an odometer, an IMU or a GPS/INS system. This algorithm exploits the fact that the relative rotation angles of both the camera and relative rotation angle sensor are the same as the camera and sensor are rigidly mounted to a rigid body. Therefore, knowledge of the extrinsic calibration between the camera and sensor is not required. We carry out a quantitative comparison of our algorithm with the well-known 5-point and 1-point algorithms, and show that our algorithm exhibits the highest level of accuracy
Efficient generic calibration method for general cameras with single centre of projection
Generic camera calibration is a non-parametric calibration technique that is applicable to any type of vision sensor. However, the standard generic calibration method was developed with the goal of generality and it is therefore sub-optimal for the common case of cameras with a single centre of projection (e.g. pinhole, fisheye, hyperboloidal catadioptric). This paper proposes novel improvements to the standard generic calibration method for central cameras that reduce its complexity, and improve its accuracy and robustness. Improvements are achieved by taking advantage of the geometric constraints resulting from a single centre of projection. Input data for the algorithm is acquired using active grids, the performance of which is characterised. A new linear estimation stage to the generic algorithm is proposed incorporating classical pinhole calibration techniques, and it is shown to be significantly more accurate than the linear estimation stage of the standard method. A linear method for pose estimation is also proposed and evaluated against the existing polynomial method. Distortion correction and motion reconstruction experiments are conducted with real data for a hyperboloidal catadioptric sensor for both the standard and proposed methods. Results show the accuracy and robustness of the proposed method to be superior to those of the standard method
Self-Calibration of Cameras with Euclidean Image Plane in Case of Two Views and Known Relative Rotation Angle
The internal calibration of a pinhole camera is given by five parameters that
are combined into an upper-triangular calibration matrix. If the
skew parameter is zero and the aspect ratio is equal to one, then the camera is
said to have Euclidean image plane. In this paper, we propose a non-iterative
self-calibration algorithm for a camera with Euclidean image plane in case the
remaining three internal parameters --- the focal length and the principal
point coordinates --- are fixed but unknown. The algorithm requires a set of point correspondences in two views and also the measured relative
rotation angle between the views. We show that the problem generically has six
solutions (including complex ones).
The algorithm has been implemented and tested both on synthetic data and on
publicly available real dataset. The experiments demonstrate that the method is
correct, numerically stable and robust.Comment: 13 pages, 7 eps-figure
Extrinsic Parameter Calibration for Line Scanning Cameras on Ground Vehicles with Navigation Systems Using a Calibration Pattern
Line scanning cameras, which capture only a single line of pixels, have been
increasingly used in ground based mobile or robotic platforms. In applications
where it is advantageous to directly georeference the camera data to world
coordinates, an accurate estimate of the camera's 6D pose is required. This
paper focuses on the common case where a mobile platform is equipped with a
rigidly mounted line scanning camera, whose pose is unknown, and a navigation
system providing vehicle body pose estimates. We propose a novel method that
estimates the camera's pose relative to the navigation system. The approach
involves imaging and manually labelling a calibration pattern with distinctly
identifiable points, triangulating these points from camera and navigation
system data and reprojecting them in order to compute a likelihood, which is
maximised to estimate the 6D camera pose. Additionally, a Markov Chain Monte
Carlo (MCMC) algorithm is used to estimate the uncertainty of the offset.
Tested on two different platforms, the method was able to estimate the pose to
within 0.06 m / 1.05 and 0.18 m / 2.39. We also propose
several approaches to displaying and interpreting the 6D results in a human
readable way.Comment: Published in MDPI Sensors, 30 October 201
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