Bringing Computer Vision to Robotics

par

Sajad Saeedi

Dyson Research Fellow at Imperial College London UK

Résumé:

Recent advances in AI and machine learning have revolutionized computer vision and robotics. Robots are gradually moving beyond carefully controlled manufacturing facilities into households, executing tasks such as vacuum cleaning and lawn mowing. To extend the capabilities of these systems, robots need to move beyond just reporting ‘what’ is ‘where’ in an image to having the spatial awareness, necessary to interact usefully with their environment. Therefore, there is an urgent need for novel robotic perception systems that can deal with many real-world constraints such as limited resources and uncertain environments. 

In this brief technical talk, several recent projects related to robotics and machine perception are presented. Recent developments such as autonomous quadrotor aircraft, multi-robot systems, and accelerated inference on focal-plane sensor-processor arrays are introduced. These developments have significant economic and scientific impacts on our society and will open up new possibilities for real-time and reliable utilization of AI and computer vision algorithms in robotic systems.

At the end of the talk, future research directions will be outlined. The main goal for future research will be developing reliable, high-speed, and low-power robotic visual perception systems that can be deployed in real-world applications. It has been hypothesized that while machine learning algorithms will give us the required reliability, data processing in the focal plane will help us to achieve the desired energy consumption and run-time speed limits. Reliable, fast, and low-power computation for scene understanding and spatial awareness will be of great interest not only to the robotics community, but also other fields, such as Internet of Things (IoT), privacy-aware devices, and networked-visual devices. These research directions will help entrepreneurs and academic researchers to identify new opportunities in machine learning and its application in robotics and computer vision.

Biographie :

Sajad Saeedi is a Dyson Research Fellow at Imperial College London UK, Department of Computing, and an Associate Fellow of Higher Education Academy. He received his PhD in Electrical and Computer Engineering from the University of New Brunswick, Canada. In a joint £5 million British Research Council project with the University of Manchester and the University of Edinburgh, he was actively involved in the design of the future architecture for robotic perception and accelerated inference. He has developed large-scale datasets, benchmarking frameworks, and high-speed/low-power visual odometry algorithms for visual perception. Additionally, in his collaborations with industry, he has developed several successful products including omnidirectional and stereoscopic vision systems. He is currently working on semantic perception, bringing deep learning advances to computer vision and robotic systems. His research interests span over the design of multi-agent systems, aerial/marine robotics, and machine learning and its applications in computer vision, robotics, and control systems.

Website: www.sajad-saeedi.ca.