Camera network and multi-camera calibration for external parameters is a necessary step for a variety of contexts in computer vision and robotics, ranging from three-dimensional reconstruction to human activity tracking. This paper describes a method for camera network and or multi-camera calibration suitable for specific contexts: the cameras may not all have a common field of view, or if they do, there may be some views that are 180 degrees from one another, and the network may be asynchronous. The calibration object required is one or more planar calibration patterns, rigidly attached to one another, and are distinguishable from one another, such as aruco or charuco patterns. We formulate the camera network and or multi-camera calibration problem in this context using rigidity constraints, represented as a system of equations, and an approximate solution is found through a two-step process. Synthetic and real experiments, including scenarios of a asynchronous camera network and rotating imaging system, demonstrate the method in a variety of settings. Reconstruction accuracy error was less than 0.5 mm for all datasets. This method is suitable for new users to calibrate a camera network, and the modularity of the calibration object also allows for disassembly, shipping, and the use of this method in a variety of large and small spaces.
Multi-camera calibration or asynchronous camera networks . Liu @cite_7 use a two-step approach to calibrating a variety of configurations, including multi-camera contexts, by using hand-eye calibration to generate an initial solution, and then minimize reprojection error. Joo @cite_18 , working with an asynchronous camera network, used patterns projected onto white cloth to calibrate via bundle adjustment.
@cite_18 @cite_7
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The use of a multi-camera system enables a robot to obtain a surround view, and thus, maximize its perceptual awareness of its environment. If vision-based simultaneous localization and mapping (vSLAM) is expected to provide reliable pose estimates for a micro aerial vehicle (MAV) with a multi-camera system, an accurate calibration of the multi-camera system is a necessary prerequisite. We propose a novel vSLAM-based self-calibration method for a multi-camera system that includes at least one calibrated stereo camera, and an arbitrary number of monocular cameras. We assume overlapping fields of view to only exist within stereo cameras. Our self-calibration estimates the inter-camera transforms with metric scale; metric scale is inferred from calibrated stereo. On our MAV, we set up each camera pair in a stereo configuration which facilitates the estimation of the MAV's pose with metric scale. Once the MAV is calibrated, the MAV is able to estimate its global pose via a multi-camera vSLAM implementation based on the generalized camera model. We propose a novel minimal and linear 3-point algorithm that uses relative rotation angle measurements from a 3-axis gyroscope to recover the relative motion of the MAV with metric scale and from 2D-2D feature correspondences. This relative motion estimation does not involve scene point triangulation. Our constant-time vSLAM implementation with loop closures runs on-board the MAV in real-time. To the best of our knowledge, no published work has demonstrated real-time on-board vSLAM with loop closures. We show experimental results from simulation experiments, and real-world experiments in both indoor and outdoor environments. In this paper we present an automatic method for calibrating a network of cameras that works by analyzing only the motion of silhouettes in the multiple video streams. This is particularly useful for automatic reconstruction of a dynamic event using a camera network in a situation where pre-calibration of the cameras is impractical or even impossible. The key contribution of this work is a RANSAC-based algorithm that simultaneously computes the epipolar geometry and synchronization of a pair of cameras only from the motion of silhouettes in video. Our approach involves first independently computing the fundamental matrix and synchronization for multiple pairs of cameras in the network. In the next stage the calibration and synchronization for the complete network is recovered from the pairwise information. Finally, a visual-hull algorithm is used to reconstruct the shape of the dynamic object from its silhouettes in video. For unsynchronized video streams with sub-frame temporal offsets, we interpolate silhouettes between successive frames to get more accurate visual hulls. We show the effectiveness of our method by remotely calibrating several different indoor camera networks from archived video streams. In this work, we propose a multi-camera system where we combine a main high-quality camera with two low-res auxiliary cameras. The auxiliary cameras are well calibrated and act as a passive depth sensor by generating disparity maps. The main camera has an interchangeable lens and can produce good quality images at high resolution. Our goal is, given the low-res depth map from the auxiliary cameras, generate a depth map from the viewpoint of the main camera. The advantage of our system, compared to other systems such as light-field cameras or RGBD sensors, is the ability to generate a high-resolution color image with a complete depth map, without sacrificing resolution and with minimal auxiliary hardware. Since the main camera has an interchangeable lens, it cannot be calibrated beforehand, and directly applying stereo matching on it and either of the auxiliary cameras often leads to unsatisfactory results. Utilizing both the calibrated cameras at once, we propose a novel approach to better estimate the disparity map of the main camera. Then by combining the defocus cue of the main camera, the disparity map can be further improved. We demonstrate the performance of our algorithm on various scenes. Virtual immersive environments or telepresence setups often consist of multiple cameras that have to be calibrated, We present a convenient method for doing this. The minimum is three cameras, but there is no upper limit. The method is fully automatic and a freely moving bpdght spot is the only calibration object, A set of virtual 3D points is made by waving the bright spot through the working volume. Its projections are found with subpixel precision and verified by a robust RANSAC analysis. The cameras do not have to see all points; only reasonable overlap between camera subgroups is necessary. Projective structures are computed via rank-4 factorization and the Euclidean stratification is done by imposing geometric constraints. This linear estimate initializes a postprocessing computation of nonlinear distortion, which is also fully automatic. We suggest a trick on how to use a very ordinary laser pointer as the calibration object. We show that it is possible to calibrate an immersive virtual environment with 16 cameras in less than 60 minutes reaching about 1 5 pixel reprojection error, The method has been successfully tested on numerous multicamera environments using varying numbers of cameras of varying quality.