Computer Vision for Robotics & Autonomous Systems Training Course

Course Overview

Learning Objectives

• By the end of the course, participants will be able to:
• Understand the fundamental principles of Computer Vision and its applications in robotics.
• Analyze visual perception requirements for robotic and autonomous systems.
• Apply image-processing and image-enhancement techniques.
• Implement object detection, recognition, and tracking approaches.
• Apply image segmentation and visual feature-extraction techniques.
• Use Machine Learning and Deep Learning for Computer Vision applications.
• Apply 3D vision and spatial estimation concepts to robotic systems.
• Integrate camera data with other robotic sensors.
• Apply Computer Vision to navigation, obstacle avoidance, and intelligent inspection.
• Evaluate and improve the accuracy, speed, and reliability of vision models.
• Address Computer Vision challenges in industrial and dynamic environments.
• Design Computer Vision solutions suitable for integration with robotic and autonomous platforms.

Who Should Attend

Learning Outcomes

• Upon completion of the course, participants will be able to:
• Assess Computer Vision requirements for robotic and autonomous applications.
• Design image-processing pipelines for intelligent systems.
• Apply object detection, recognition, and tracking techniques.
• Develop solutions for real-time image and video analysis.
• Apply Deep Learning models to Computer Vision tasks.
• Implement image segmentation and visual feature-extraction techniques.
• Apply fundamental 3D vision and spatial estimation concepts.
• Integrate camera data with other sensors to improve environmental perception.
• Apply Computer Vision to navigation, localization, and obstacle avoidance.
• Develop automated visual inspection and defect-detection solutions.
• Evaluate the accuracy, performance, and reliability of Computer Vision models.
• Develop an integrated Computer Vision solution for a robotic or autonomous system.

Course Outline