Applications of Machine Vision in Robotics

Dear Colleagues,

Applications of machine vision in modern robotic systems are still one of the most dynamically growing areas of research in automation and robotics. Modern image analysis methods have become an integral part of many robotic systems, and these methods include video feedback in industrial systems, including robotic arms; vision-based navigation of mobile robots and Video Simultaneous Localization and Mapping (VSLAM) methods; and the use of video analysis in Unmanned Aerial Vehicles (UAVs).

Machine vision systems in robotics have also become one of the leading areas of practical applications for the recently developed artificial intelligence solutions based on computer vision algorithms. Nevertheless, due to the rapid development of deep neural networks, one of the most relevant issues is related to the “explainability” of algorithms as well as their universality and ability to recognize and classify previously unknown objects. Therefore, due to a limited amount of training data, typical for robotics, important challenges are also related to unsupervised learning.

Another key problem that influences the final results of image analysis in robotics is related to the quality of input data; hence, no-reference image and video quality assessment methods play an important role as well. Thanks to their applications, it is possible to prevent the use of some distorted video frames for image analysis utilized for the further control of, e.g., robot motion.

This Special Issue on “Applications of Machine Vision in Robotics” will bring together the research communities interested in computer and machine vision from various departments and universities, focusing on both robotics as well as applied computer science.

Topics of interest for this Special Issue include but are not limited to the following:

• Video simultaneous localization and mapping (VSLAM) solutions;
• Image-based navigation of unmanned aerial vehicles (UAVs) and other mobile robots;
• Applications of computer vision in autonomous vehicles;
• Fast algorithms useful for embedded solutions, e.g., based on the Monte Carlo method;
• No-reference image quality assessment;
• Texture analysis and shape recognition for UAVs and mobile robotics;
• Novel image descriptors that are useful for image-based classification of objects;
• Feature extraction and image registration, including remote sensing images.

Prof. Krzysztof Okarma
Dr. Piotr Lech
Guest Editors

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• image analysis
• machine vision
• video analysis
• visual SLAM
• visual inspection and diagnostics
• robotic vision systems
• video feedback in robotics