Improving depth maps of plants by using a set of five cameras

Abstract

Abstract. Obtaining high-quality depth maps and disparity maps with the use of a stereo camera is a challenging task for some kinds of objects. The quality of these maps can be improved by taking advantage of a larger number of cameras. The research on the usage of a set of five cameras to obtain disparity maps is presented. The set consists of a central camera and four side cameras. An algorithm for making disparity maps called multiple similar areas (MSA) is introduced. The algorithm was specially designed for the set of five cameras. Experiments were performed with the MSA algorithm and the stereo matching algorithm based on the sum of sum of squared differences (sum of SSD, SSSD) measure. Moreover, the following measures were included in the experiments: sum of absolute differences (SAD), zero-mean SAD (ZSAD), zero-mean SSD (ZSSD), locally scaled SAD (LSAD), locally scaled SSD (LSSD), normalized cross correlation (NCC), and zero-mean NCC (ZNCC). Algorithms presented were applied to images of plants. Making depth maps of plants is difficult because parts of leaves are similar to each other. The potential usability of the described algorithms is especially high in agricultural applications such as robotic fruit harvesting.

3D Vision System for a Robotic Arm Based on Equal Baseline Camera Array

“…They had a camera matrix build from 16 cameras in 4x4 configuration, however they analyzed the usage of the selected five cameras forming EBCA from the matrix. The author of this paper further developed stereo matching algorithms for EBCA and researched its capabilities [10][11][12]40]. The photo of an operational real EBCA used in the experiments presented in this paper is presented in Fig.…”

“…The author of this paper has also developed methods for obtaining disparity maps with the use of EBCA. Exceptions Excluding Merging Method (EEMM) and the Multiple Similar Areas algorithm (MSA) [10,11] are the most significant ones.…”

“…Multiple Similar Areas is another algorithm for EBCA introduced by the author of this paper [10]. MSA does not merge disparities acquired from different stereo cameras like EEMM.…”

“…This concept leads to poor results in case of having only a pair of cameras but is justified when there are side cameras placed in four different directions from the point of view of the central camera. The algorithm is precisely described in [10].…”

“…One of the most popular are cameras placed along a straight line [8]. This paper is dedicated to a specific kind of a camera array in which there is a central camera and a ring of side cameras equally distant from the central one [9,10]. This camera array was previously called Equal Baseline Multiple Camera Set (EBMCS) [11,12].…”