## 1 Introduction  
This paper presents research on a camera array which consists of a central camera and up to four side cameras equally distant from a central one. Such an array has a function of a 3D vision system. The array was called Equal Baseline Camera Array (EBCA). It is derived from a stereo camera [1, 2]. In general, researchers improve the quality of 3D data obtained from a stereo camera by designing more precise algorithms for processing pairs of images. The research presented in this paper is focused on improving 3D vision by taking advantage of a greater number of cameras. EBCA preserves benefits of a stereo camera while it provides a higher quality of data. The comparison of the considered array with other 3D imaging techniques such as structured light 3D scanning is presented in Section 2.1. The main application of the research presented in this paper is the development of 3D vision systems for autonomous robots operating in outdoor environments. The research is derived from agricultural applications in which 3D vision sensors were applied to robotic fruit harvesting [2, 3, 4, 5]. However, the array described in this paper can also be used with other kinds of autonomous machines including self-driving cars or unmanned underwater vehicles (UUVs). Section 7 lists applications in which the array can be used. The paper also presents a method for calibrating cameras in the array including its intrinsic and extrinsic parameters [6, 7, 8]. We propose a hybrid method combining self-calibration techniques with a calibration based on taking images of a predefined patterns. The method is adjusted to distinctive features of the array as presented in Sections 2.4, 4 and 6. Another original contribution of this paper is the description of the testbed which was released for testing stereo matching algorithms intended to use with the considered array. The testbed was released under an open-source ([[https://github.com/alkaczma/ebca](https://github.com/alkaczma/ebca)]([https://github.com/alkaczma/ebca](https://github.com/alkaczma/ebca)) (accessed on 9 August 2021)). It is suitable for analyzing the improvement in the quality of results with regard to the number of side cameras in the array in the range between one and four. The testbed presented in this paper is inspired by well-known testbeds for evaluating stereo matching algorithms designed for obtaining disparity maps and depth maps using stereo cameras. The most popular this kind of testbeds are Middlebury Stereo Evaluation ([[http://vision.middlebury.edu/stereo/](http://vision.middlebury.edu/stereo/)]([http://vision.middlebury.edu/stereo/](http://vision.middlebury.edu/stereo/)) (accessed on 9 August 2021)) [9; 10] and KITTI Vision Benchmark ([[http://www.cvlibs.net/datasets/kitti/](http://www.cvlibs.net/datasets/kitti/)]([http://www.cvlibs.net/datasets/kitti/](http://www.cvlibs.net/datasets/kitti/)) (accessed on 9 August 2021)) [11]. In general, there is a lack of openly available testbeds for algorithms using camera arrays. This paper addresses this field of research. This topic is covered in Sections 2.2 and 5. Moreover, the paper describes the usage of Exception Excluding Merging Method (EEMM) with the proposed testbed [3]. This method was designed for obtaining 3D data on the basis of images taken by EBCA. Experiments presented in the paper verify the effects of changing values of parameters which are used in the method. This paper also shows how the quality of results depends on the number of cameras included in the camera array when the EEMM method is used. Section 6 describes tests of the method with four stereo matching algorithms including the algorithm which takes advantage of convolutional neural network (CNN) [12]. The original research presented in this paper includes the following. (1) The design and experiments with the calibration method for EBCA. (2) The description of the testbed designed for testing 3D imaging algorithms using the array.