Depth estimation for a robotic arm based on monocular images

Abstract

Abstract Regaining depth information of objects from two-dimensional images is one of the fundamental issues and essential thing in the field of machine vision. There is a depth estimation method using non-inclined with forward movements that was already presented with feature extraction of image processing based on one direction only. This study proposes a depth estimation approach based on forward and backward movement with non-inclined and inclined orientations. Binary threshold will be used in this study to replace feature extraction. The study can approximate the most effective depth estimation using forward and backward movement in frame of non-inclined and inclined orientations. The images captured by an un-calibrated ordinary monocular camera mounted at the end effector of a robot arm. In this approach, the first image is captured and then the camera parameters remain unchanged. The second image is acquired after moving a distance d along the optical axis. Then image segmentation and binary threshold are implemented on the two images separately, and the numbers of black pixels in the images are counted. The experiments were carried out by simulations using webot software and python programming code. The results showed that the second position of the camera 0.1 meter from its first position demonstrated the best estimation performance, both for non-inclined and inclined movements, while the 0.01 meter distance resulted in the greatest error.