Multi-camera vision systems (MVS) have a larger field of view than a single camera and are often applied to acquire geometric information of large-scale objects or scenes. Data integration from multiple cameras is a very important part for vision systems which is usually solved by using a global calibration method. After installing the MVS, in some cases, cameras do not have a common field of view (CFV) or only a narrow CFV. Accurate global calibration of cameras with non-overlapping field of view (FOV) is a very challenging task. A variety of global calibration of non-overlapping multi-camera methods (GCNM) have been proposed to estimate the relative positions and orientations of cameras based on different types of media or techniques such as large-range measuring devices, large-scale calibration targets, optical mirrors, motion model, laser projection, visual measuring instruments, etc. However, the GCNM is not yet a completely solved problem. Choosing which type of GCNM method to use is highly dependent on the specific vision system. Thus, in this paper, we present a comparative review of different GCNM methods and analyze accuracy, range, defects, and applications. Researchers and developers can take it as background information for their future works.

This work was published on State of the art[J]. Optik,2018,158:951-961.titled Global calibration of non-overlapping cameras: State of the art.