Multicamera rig calibration by double‐sided thick checkerboard

Abstract

A multi-camera rig calibration algorithm based on a double sided planar target is proposed. Due to their inherently simple realisation, low cost and accuracy, planar calibration targets came out as one of the most largely adopted calibration tools both for intrinsic and extrinsic camera parameters. However, concerning the estimation of extrinsic parameters, one of the major drawbacks of these targets is their requirement for distinct target visibility from both cameras. This prevents many configurations from being adopted where, e.g. two cameras are facing each other. An inexpensive solution could be based on printing/pasting a planar pattern on both target sides, however, the relative misalignment between the patterns on the two sides and the target thickness could be unknown. The authors propose a solution where double-sided target displacement error is estimated together with the extrinsic parameters allowing the reuse of all the available planar calibration tools in less constrained configurations. To assess their approach the authors tested the system in two scenarios, one using two professional 4K cameras and one using two smartphones.

UAV-Assisted Wide Area Multi-Camera Space Alignment Based on Spatiotemporal Feature Map

“…One of the widely used calibration algorithms was proposed by Zhang [16], who used single checkerboard calibration plane to estimate camera external and internal parameters simultaneously. Based on Zhang's approach, many corresponding improved methods [17,18] are presented to optimize different parts such as optimization function and calibration object. To overcome the limited stereo information of 2D calibration object, 3D marker is used to camera imaging parameter estimation.…”

Multi‐camera traffic scene mosaic based on camera calibration

“…For single scene calibration in different scenarios, the calibration error in the near field of scene is found to be about 2.8% in Table 1(line 1-6) and Table 2(line 1-6). Table 1(line [7][8][9][10][11][12] and Table 2(line 7-12) find that the calibration error in the far field of scene is about 3.1%. The data analysis shows that the camera calibration error is small in the near scene and the far-end error is slightly increased.…”

“…The overall accuracy of these methods is relatively accurate, but the calibration object is always required during calibration process and it is impossible to place the calibrators in some scenarios. (2) Active vision camera calibration method which is based on the known camera motion trajectory information or uses the camera to do specific qualitative or quantitative motion to calculate internal and external parameters [10,11]. The advantage of this method is that the algorithm is relatively simple, and its disadvantages are that the cost of this method is high, the experimental equipment is expensive and the application of most traffic surveillance cameras is difficult.…”