Stereo Vision Depth Estimation Methods for Robotic Applications

2011 ◽  
pp. 397-417 ◽  
Author(s):  
Lazaros Nalpantidis ◽  
Antonios Gasteratos
Keyword(s):  
Stereo Vision ◽  
Vision Research ◽  
Depth Estimation ◽  
Research Community ◽  
Estimation Methods ◽  
Autonomous Operation ◽  
Working Environments ◽  
Important Sense ◽  
New Challenges ◽  
Robotic Applications

Vision is undoubtedly the most important sense for humans. Apart from many other low and higher level perception tasks, stereo vision has been proven to provide remarkable results when it comes to depth estimation. As a result, stereo vision is a rather popular and prosperous subject among the computer and machine vision research community. Moreover, the evolution of robotics and the demand for vision-based autonomous behaviors has posed new challenges that need to be tackled. Autonomous operation of robots in real working environments, given limited resources requires effective stereo vision algorithms. This chapter presents suitable depth estimation methods based on stereo vision and discusses potential robotic applications.

scholarly journals Stereo Vision Depth Estimation Methods for Robotic Applications

Robotics ◽  
2013 ◽  
pp. 1461-1481
Author(s):  
Lazaros Nalpantidis ◽  
Antonios Gasteratos
Keyword(s):  
Stereo Vision ◽  
Vision Research ◽  
Depth Estimation ◽  
Research Community ◽  
Estimation Methods ◽  
Autonomous Operation ◽  
Working Environments ◽  
Important Sense ◽  
New Challenges ◽  
Robotic Applications

Vision is undoubtedly the most important sense for humans. Apart from many other low and higher level perception tasks, stereo vision has been proven to provide remarkable results when it comes to depth estimation. As a result, stereo vision is a rather popular and prosperous subject among the computer and machine vision research community. Moreover, the evolution of robotics and the demand for vision-based autonomous behaviors has posed new challenges that need to be tackled. Autonomous operation of robots in real working environments, given limited resources requires effective stereo vision algorithms. This chapter presents suitable depth estimation methods based on stereo vision and discusses potential robotic applications.

scholarly journals Real-Time Single Image Depth Perception in the Wild with Handheld Devices

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 15
Author(s):  
Filippo Aleotti ◽  
Giulio Zaccaroni ◽  
Luca Bartolomei ◽  
Matteo Poggi ◽  
Fabio Tosi ◽  
...  
Keyword(s):  
Real Time ◽  
Depth Perception ◽  
Depth Estimation ◽  
Autonomous Driving ◽  
Estimation Methods ◽  
Handheld Devices ◽  
Single Image ◽  
Handheld Device ◽  
Time Performance ◽  
In The Wild

Depth perception is paramount for tackling real-world problems, ranging from autonomous driving to consumer applications. For the latter, depth estimation from a single image would represent the most versatile solution since a standard camera is available on almost any handheld device. Nonetheless, two main issues limit the practical deployment of monocular depth estimation methods on such devices: (i) the low reliability when deployed in the wild and (ii) the resources needed to achieve real-time performance, often not compatible with low-power embedded systems. Therefore, in this paper, we deeply investigate all these issues, showing how they are both addressable by adopting appropriate network design and training strategies. Moreover, we also outline how to map the resulting networks on handheld devices to achieve real-time performance. Our thorough evaluation highlights the ability of such fast networks to generalize well to new environments, a crucial feature required to tackle the extremely varied contexts faced in real applications. Indeed, to further support this evidence, we report experimental results concerning real-time, depth-aware augmented reality and image blurring with smartphones in the wild.

Why did Karlovich & Wallisch (2021) paste the "Gestalt" label on notions the Gestaltists rejected and disproved?

2021 ◽  
Author(s):  
Lydia Maniatis
Keyword(s):  
Vision Research ◽  
Research Community ◽  
Research Tradition ◽  
The People

“Gestalt” is a fashionable buzzword in the vision research community. The people invoking it tend to have little to no understanding of the groundbreaking ideas the term represents, and which they badly misrepresent. This is the case in Karlovich & Wallisch (2021), who misuse Gestalt citations to cover the theoretical gap left by a dominant vision research tradition uninterested in and incapable of addressing problems of shape and organization in vision.

Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies for Sustainable Farming and Smart Agriculture

2021 ◽  
pp. 40-53
Author(s):  
Vardan Mkrttchian
Keyword(s):  
Sliding Mode ◽  
Research Community ◽  
End User ◽  
Sustainable Farming ◽  
Model Driven ◽  
Blockchain Technology ◽  
Challenges And Opportunities ◽  
Intellectual Control ◽  
New Challenges ◽  
Smart Agriculture

In this chapter, the author describes the main new challenges and opportunities of blockchain technology for digital economy in Russia. The study in Russia showed that the Russian research community has not addressed a majority of these challenges, and he notes that blockchain developer communities actively discuss some of these challenges and suggest myriad potential solutions. Some of them can be addressed by using private or consortium blockchain instead of a fully open network. In general, the technological challenges are limited at this point, in terms of both developer support (lack of adequate tooling) and end-user support (hard to use and understand). The recent advances on developer support include efforts by of the towards model-driven development of blockchain applications sliding mode in intellectual control and communication and help the technological challenges and created tools. The chapter shows how avatars may communicate with each other by utilizing a variety of communications methods for sustainable farming and smart agriculture.

scholarly journals Virtual Reality Based Multi-Modal Teleoperation Using Mixed Autonomy

2008 ◽  
Author(s):  
Muthukkumar S. Kadavasal ◽  
Abhishek Seth ◽  
James H. Oliver
Keyword(s):  
Virtual Reality ◽  
Stereo Vision ◽  
Vision System ◽  
Tracking System ◽  
A Priori ◽  
Obstacle Detection ◽  
Remotely Operated Vehicle ◽  
Autonomous Operation ◽  
Control Interface ◽  
Video Feed

A multi modal teleoperation interface is introduced featuring an integrated virtual reality based simulation augmented by sensors and image processing capabilities on-board the remotely operated vehicle. The proposed virtual reality interface fuses an existing VR model with live video feed and prediction states, thereby creating a multi modal control interface. Virtual reality addresses the typical limitations of video-based teleoperation caused by signal lag and limited field of view thereby allowing the operator to navigate in a continuous fashion. The vehicle incorporates an on-board computer and a stereo vision system to facilitate obstacle detection. A vehicle adaptation system with a priori risk maps and real state tracking system enables temporary autonomous operation of the vehicle for local navigation around obstacles and automatic re-establishment of the vehicle’s teleoperated state. As both the vehicle and the operator share absolute autonomy in stages, the operation is referred to as mixed autonomous. Finally, the system provides real time update of the virtual environment based on anomalies encountered by the vehicle. The system effectively balances the autonomy between human and on board vehicle intelligence. The stereo vision based obstacle avoidance system is initially implemented on video based teleoperation architecture and experimental results are presented. The VR based multi modal teleoperation interface is expected to be more adaptable and intuitive when compared to other interfaces.

Experimental Autonomous Deep Learning-Based 3D Path Planning for a 7-DOF Robot Manipulator

2019 ◽  
Author(s):  
Alex Bertino ◽  
Mostafa Bagheri ◽  
Miroslav Krstić ◽  
Peiman Naseradinmousavi
Keyword(s):  
Deep Learning ◽  
Stereo Vision ◽  
Robot Manipulator ◽  
Control Techniques ◽  
Minimal Error ◽  
Autonomous Operation ◽  
Pick And Place ◽  
Place Task ◽  
Position And Orientation ◽  
And Control

Abstract In this paper, we examine the autonomous operation of a high-DOF robot manipulator. We investigate a pick-and-place task where the position and orientation of an object, an obstacle, and a target pad are initially unknown and need to be autonomously determined. In order to complete this task, we employ a combination of computer vision, deep learning, and control techniques. First, we locate the center of each item in two captured images utilizing HSV-based scanning. Second, we utilize stereo vision techniques to determine the 3D position of each item. Third, we implement a Convolutional Neural Network in order to determine the orientation of the object. Finally, we use the calculated 3D positions of each item to establish an obstacle avoidance trajectory lifting the object over the obstacle and onto the target pad. Through the results of our research, we demonstrate that our combination of techniques has minimal error, is capable of running in real-time, and is able to reliably perform the task. Thus, we demonstrate that through the combination of specialized autonomous techniques, generalization to a complex autonomous task is possible.

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