Omnidirectional Camera Calibration Research Articles

Catadioptric omnidirectional thermal odometry in dynamic environment

This paper presents a catadioptric omnidirectional thermal odometry (COTO) system that estimates the six degrees of freedom (DoF) pose of a camera using only omnidirectional thermal images in visually degraded, fast-motion, and dynamic environments. First, we design and fabricate a central hyperbolic catadioptric omnidirectional thermal camera that captures surrounding thermal images with 360° horizontal field of view (FoV), and improve the omnidirectional camera model and calibration method to obtain high-precision camera intrinsic parameter. Second, we propose the epipolar curve constraint combining with omnidirectional thermal object detection to significantly reduce the interference of moving objects on pose estimation. Third, the implemented COTO pipeline consists of photometric calibration, dynamic region removal, tracking and mapping to overcome the drawbacks of photometric inconsistency and large distortion in omnidirectional thermal images. Experiments have been conducted on a total of 17 sequences of Lab, Outdoor and Driving, amounting to more than 60,000 omnidirectional thermal images of real environments. The experimental results indicate that the proposed COTO system has excellent localization accuracy and unparalleled robustness over the current state-of-the-art methods. The average localization accuracy measured by the absolute trajectory error (ATE) is less than 15 cm from the ground truth in both Lab and Outdoor sequences. In addition, COTO was the only system with complete and successful tracking in all sequences. The system can be used as an innovative localization solution, particularly in challenging environments with changes in ambient light, rapid vehicle motion, and moving object interference, which can be a difficult problem for visual odometry to solve.

Precision Calibration of Omnidirectional Camera Using a Statistical Approach

Omnidirectional optoelectronic systems (OOES) find applications in many areas where a wide viewing angle is crucial. The disadvantage of these systems is the large distortion of the images, which makes it difficult to make wide use of them. The purpose of this study is the development an algorithm for the precision calibration of an omnidirectional camera using a statistical approach. The calibration approach comprises three basic stages. The first stage is the formation of a cloud of points characterizing the view field of the virtual perspective camera. In the second stage, a calibration procedure that provides the projection function for the camera calibration is performed. The projection functions of traditional perspective lenses and omnidirectional wide-angle fisheye lenses with a viewing angle of no less than 180° are compared. The construction of the corrected image is performed in the third stage. The developed algorithm makes it possible to obtain an image for part of the field of view of an OOES by correcting the distortion from the original omnidirectional image.Using the developed algorithm, a non-mechanical pivoting camera based on an omnidirectional camera is implemented. The achieved mean squared error of the reproducing points from the original omnidirectional image onto the image with corrected distortion is less than the size of a very few pixels.

High-Precision Calibration of Omnidirectional Camera Using an Iterative Method

A distorted checkerboard image affects the precision of omnidirectional camera calibration because of the inaccurate localization of the feature points. To solve this problem, an iterative refinement method is presented. First, the initial parameters are obtained using the traditional calibration method, and the distorted checkerboard is transformed to a distortion-free plane by the projection model that is estimated by initial parameters. Then, the feature point coordinates of the corrected image are extracted. The calibration parameters are recomputed using the refinement of new point locations until the camera parameters converge. This iterative refinement method improves the localization accuracy of feature points and, consequently, of camera calibration. The correctness and effectiveness of the method are verified by a series of simulations and physical experiments. The experiments show that the reprojection errors of feature points are reduced by 39% compared to traditional methods.

Charuco Board-Based Omnidirectional Camera Calibration Method

In this paper, we propose a Charuco board-based omnidirectional camera calibration method to solve the problem of conventional methods requiring overly complicated calibration procedures. Specifically, the proposed method can easily and precisely provide two-dimensional and three-dimensional coordinates of patterned feature points by arranging the omnidirectional camera in the Charuco board-based cube structure. Then, using the coordinate information of the feature points, an intrinsic calibration of each camera constituting the omnidirectional camera can be performed by estimating the perspective projection matrix. Furthermore, without an additional calibration structure, an extrinsic calibration of each camera can be performed, even though only part of the calibration structure is included in the captured image. Compared to conventional methods, the proposed method exhibits increased reliability, because it does not require additional adjustments to the mirror angle or the positions of several pattern boards. Moreover, the proposed method calibrates independently, regardless of the number of cameras comprising the omnidirectional camera or the camera rig structure. In the experimental results, for the intrinsic parameters, the proposed method yielded an average reprojection error of 0.37 pixels, which was better than that of conventional methods. For the extrinsic parameters, the proposed method had a mean absolute error of 0.90° for rotation displacement and a mean absolute error of 1.32 mm for translation displacement.

Improved wide-angle, fisheye and omnidirectional camera calibration

In this paper an improved method for calibrating wide-angle, fisheye and omnidirectional imaging systems is presented. We extend the calibration procedure proposed by Scaramuzza et al. by replacing the residual function and joint refinement of all parameters. In doing so, we achieve a more stable, robust and accurate calibration (up to factor 7) and can reduce the number of necessary calibration steps from five to three. After introducing the camera model and highlighting the differences from the current calibration procedure, we perform a comprehensive performance evaluation using several data sets and show the impact of the proposed calibration procedure on the calibration results.

A Survey of Catadioptric Omnidirectional Camera Calibration

For dozen years, computer vision becomes more popular, in which omnidirectional camera has a larger field of view and widely been used in many fields, such as: robot navigation, visual surveillance, virtual reality, three-dimensional reconstruction, and so on. Camera calibration is an essential step to obtain three- dimensional geometric information from a two- dimensional image. Meanwhile, the omnidirectional camera image has catadioptric distortion, which need to be corrected in many applications, thus the study of such camera calibration method has important theoretical significance and practical applications. This paper firstly introduces the research status of catadioptric omnidirectional imaging system; then the image formation process of catadioptric omnidirectional imaging system has been given; finally a simple classification of omnidirectional imaging method is given, and we discussed the advantages and disadvantages of these methods.

Calibration of central omnidirectional cameras via the viewing sphere

We propose a robust calibration method for central omnidirectional cameras. It makes use of two mutually orthogonal sets of parallel lines. Based on the unified sphere model, the intrinsic parameters are initialized by two pairs of vanishing points in the image plane. Furthermore, the extrinsic parameters are estimated based on two geometric preservations of the two line sets on the viewing sphere. We test the method on both simulated and real data. The simulated experiments show the effectiveness of the proposed method. Experiments with respect to calibration results and validation of extrinsic parameters for real images prove that the proposed method has higher accuracy compared with Mei's toolbox.

Calibration of omnidirectional cameras in practice: A comparison of methods

Omnidirectional cameras are becoming increasingly popular in computer vision and robotics. Camera calibration is a step before performing any task involving metric scene measurement, required in nearly all robotics tasks. In recent years many different methods to calibrate central omnidirectional cameras have been developed, based on different camera models and often limited to a specific mirror shape. In this paper we review the existing methods designed to calibrate any central omnivision system and analyze their advantages and drawbacks doing a deep comparison using simulated and real data. We choose methods available as OpenSource and which do not require a complex pattern or scene. The evaluation protocol of calibration accuracy also considers 3D metric reconstruction combining omnidirectional images. Comparative results are shown and discussed in detail.

Obtaining external parameters of an omnidirectional camera with a fish-eye lens using contour matching

This paper presents a novel omnidirectional camera calibration technique based on automatic contour matching. In this proposed method, the initial parameters, translation and rotation, are computed using the epipolar constraint from matched feature points. By using a normal vector to a 3-D plane connecting the contour with the center of the camera projection model, after contour segmentation, candidate contours are chosen to establish a correspondence between views. In order to find the corresponding end points of the contours among the views, the initial parameters and their active matching windows are utilized. Finally, the camera motions are estimated by minimizing the angular error function between the epipolar plane and the backprojected end-point vectors of the corresponding contours. Experiments on synthetic and real images demonstrate that the proposed algorithm obtains more precise camera parameters than previous methods.