With the rapid development of industrial cameras, binocular stereo vision is widely used in the field of manufacturing. Using two or more image points of one point in space to restore space depth information is called stereo vision, which process is known as three-dimensional reconstruction. Binocular stereo vision is normally used in the static test and has been rarely reported for the dynamic...

Stereo vision is an important feature that enables machine vision systems to perceive their environment in 3D. While machine vision has spawned a variety of software algorithms to solve the stereo-correspondence problem, their implementation and integration in small, fast, and efficient hardware vision systems remains a difficult challenge. Recent advances made in neuromorphic engineering offer...

In this study, stereo vision system is applied to visual servoing of a mobile manipulator. The robot can recognize a target and compute the 3D position of the target by using a stereo vision system. A stereo vision system enables the robot to find the position of a target without additional information while a monocular vision system needs properties such as geometric shape of a target. Many al...

Majority of the existing robot navigation systems, which facilitate the use of laser range finders, sonar sensors or artificial landmarks, has the ability to locate itself in an unknown environment and then build a map of the corresponding environment. Stereo vision,while still being a rapidly developing technique in the field of autonomous mobile robots, are currently less preferable due to it...

Stereo vision systems have been researched for decades and constitute the traditional method for extracting three-dimensional (3D) real-time depth information from images using sensors. However, passive stereo vision systems show a significant error in processing untextured regions, which are frequent in indoor environments. Also, modifications for processing in real time make error by using ap...

We study how autonomous robots can learn by themselves to improve their depth estimation capability. In particular, we investigate a self-supervised learning setup in which stereo vision depth estimates serve as targets for a convolutional neural network (CNN) that transforms a single still image to a dense depth map. After training, the stereo and mono estimates are fused with a novel fusion m...

This paper investigates the integration of feature extraction, object recognition and 3D reconstruction by stereo vision into a unified framework. In doing so, stereo vision can be made more robust by applying feature extraction results to the stereo matching process, and object recognition can be extended through the integration of depth information as another feature of the scene. In this wor...

A novel concept for vision-based robot control is introduced. It eliminates the need for a calibration of the robot and of the vision system and comprises an automatic adaptation to changing parameters. A key point of the concept is the newly proposed method of "objectand behavior-oriented stereo vision". Contrary to conventional stereo vision methods it uses an uncalibrated camera system and a...

Our project starts from a practical specific application of stereo vision (matching) on a robot arm, which is first building up a vision system for a robot arm to make it obtain the capability of detecting the objects 3D information on a plane. The kernel of the vision system is stereo matching. Stereo matching(correspondence) problem has been studied for a few decades; it is one of the most in...

Self-Supervised Learning (SSL) is a reliable learning mechanism in which a robot uses an original, trusted sensor cue for training to recognize an additional, complementary sensor cue. We study for the first time in SSL how a robot’s learning behavior should be organized, so that the robot can keep performing its task in the case that the original cue becomes unavailable. We study this persiste...