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Course Outline
Introduction to Computer Vision for Robotics
- Survey of computer vision applications in robotics
- Major challenges in perception and visual understanding
- Setting up the development environment with OpenCV and Python
Image Processing Fundamentals
- Image representation and manipulation techniques
- Filtering, edge detection, and feature extraction
- Color spaces and segmentation methods
Object Detection and Tracking with OpenCV
- Detecting objects using classical methods (Haar cascades, HOG)
- Tracking moving objects within video streams
- Integrating visual feedback into robotic systems
Deep Learning for Visual Perception
- Overview of convolutional neural networks (CNNs)
- Training and deploying object detection models
- Applying pre-trained models (YOLO, SSD, Faster R-CNN)
Sensor Fusion and Depth Perception
- Integrating camera data with LiDAR and ultrasonic sensors
- Depth estimation and 3D reconstruction
- Perception techniques for obstacle avoidance and navigation
Vision-Based Control and Decision Making
- Applying computer vision to robotic manipulation
- Visual servoing and closed-loop control mechanisms
- Autonomous decision-making driven by visual input
Deploying and Optimizing Vision Models
- Deploying models on embedded systems and edge devices
- Optimizing inference performance for real-time applications
- Troubleshooting and enhancing accuracy
Summary and Next Steps
Requirements
- Understanding of fundamental robotics concepts
- Proficiency in Python programming
- Familiarity with the basics of machine learning
Audience
- Robotics engineers
- Computer vision specialists
- Machine learning engineers
21 Hours
Testimonials (2)
Supply of the materials (virtual machine) to get straight into the excersises, and the explanation of the Ros2 core. Why things work a certain way.
Arjan Bakema
Course - Autonomous Navigation & SLAM with ROS 2
its knowledge and utilization of AI for Robotics in the Future.
