Calibration of the Relative Orientation between Multiple Depth Cameras Based on a Three-Dimensional Target

Abstract

Depth cameras play a vital role in three-dimensional (3D) shape reconstruction, machine vision, augmented/virtual reality and other visual information-related fields. However, a single depth camera cannot obtain complete information about an object by itself due to the limitation of the camera’s field of view. Multiple depth cameras can solve this problem by acquiring depth information from different viewpoints. In order to do so, they need to be calibrated to be able to accurately obtain the complete 3D information. However, traditional chessboard-based planar targets are not well suited for calibrating the relative orientations between multiple depth cameras, because the coordinates of different depth cameras need to be unified into a single coordinate system, and the multiple camera systems with a specific angle have a very small overlapping field of view. In this paper, we propose a 3D target-based multiple depth camera calibration method. Each plane of the 3D target is used to calibrate an independent depth camera. All planes of the 3D target are unified into a single coordinate system, which means the feature points on the calibration plane are also in one unified coordinate system. Using this 3D target, multiple depth cameras can be calibrated simultaneously. In this paper, a method of precise calibration using lidar is proposed. This method is not only applicable to the 3D target designed for the purposes of this paper, but it can also be applied to all 3D calibration objects consisting of planar chessboards. This method can significantly reduce the calibration error compared with traditional camera calibration methods. In addition, in order to reduce the influence of the infrared transmitter of the depth camera and improve its calibration accuracy, the calibration process of the depth camera is optimized. A series of calibration experiments were carried out, and the experimental results demonstrated the reliability and effectiveness of the proposed method.

Smart optical coordinate and surface metrology

“…Feng et al [119] proposed a characterisation method based on the use of a transparent glass checkerboard, refractive projection model and ray tracing to reduce calibration errors. Several researchers have proposed flexible methods to solve the inherent complexity of multi-view system characterisation using 3D targets [120,121]. This method allows for the characterisation of all cameras with limited-overlapping or nonoverlapping fields of view in a single operation, unifying the multi-camera coordinates in the same reference frame and avoiding extensive workloads and accuracy losses caused by repeated processes.…”

Optimisation of camera positions for optical coordinate measurement based on visible point analysis

“…Optical coordinate measurement using cameras is now widely used in advanced manufacturing [19]. The use of a single stereo-pair of cameras, is often insufficient for part inspection due to the limited field of view (FOV) and the self-occlusion of surfaces [20]. In order to increase the FOV and maximise coverage of an object's surface, a network of multiple camera viewpoints can be an effective solution [24], which can be achieved using either multiple cameras or a single camera which moves relative to the object.…”

“…Characterisation of the intrinsic and extrinsic camera parameters is critical for accurate measurement [19]. Camera calibration is a research topic in its own right and is beyond the scope of this work (for more details see [19][20][21][22][23]). Barazzetti points out [10] that as optical coordinate measurement, particularly photogrammetry, has become more accessible, inexperienced users with little measurement experience often produce crude reconstructions at the cost of metric accuracy.…”

Camera Calibration Using Multiple Unordered Coplanar Chessboards

“…Thus motivated, this paper is a preliminary study as to whether, and under which conditions, an extended approach based on a set of unordered coplanar chessboards might, in principle, simulate an (infeasibly large) single chessboard, to effectively accommodate longer focusing distances or wider fields of view. Multiple (non-coplanar) or 3D chessboards have already been exploited for camera-to-camera, camera-to-range sensor or multiple depth camera automatic calibration (Geiger et al, 2012;Fuersattel et al, 2017;Yin et al, 2018;Fu et al, 2019;Liu et al 2019). Yet, to the best of our knowledge, the particular use of multiple coplanar chessboards for camera calibration has not been investigated.…”