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Duration 21 hours
Course Outline
Fundamentals of Computer Vision in Robotics
- Exploring the scope of computer vision applications in the robotics domain
- Addressing key challenges in visual perception and understanding
- Configuring the development environment with OpenCV and Python
Essentials of Image Processing
- Understanding image representation and manipulation techniques
- Applying filtering, edge detection, and feature extraction methods
- Working with color spaces and advanced segmentation techniques
Object Detection and Tracking via OpenCV
- Implementing object detection using classical approaches (e.g., Haar cascades, HOG)
- Tracking dynamic objects within video streams
- Incorporating visual feedback loops into robotic system controls
Deep Learning for Enhanced Visual Perception
- Introducing convolutional neural networks (CNNs) and their architecture
- Training and deploying robust object detection models
- Utilizing pre-trained architectures such as YOLO, SSD, and Faster R-CNN
Sensor Fusion and Depth Awareness
- Fusing camera data with LiDAR and ultrasonic sensor inputs
- Performing depth estimation and 3D spatial reconstruction
- Enabling perception capabilities for obstacle avoidance and precise navigation
Vision-Driven Control and Decision Making
- Applying computer vision techniques to robotic manipulation tasks
- Implementing visual servoing and closed-loop control strategies
- Achieving autonomous decision-making driven by visual inputs
Deployment and Optimization of Vision Models
- Deploying models efficiently on embedded systems and edge devices
- Optimizing inference speed for real-time application requirements
- Troubleshooting issues and refining model accuracy
Conclusions and Future Directions
Requirements
- A foundational understanding of core robotics concepts
- Proficiency in Python programming
- Basic familiarity with machine learning principles
Target Audience
- Robotics Engineers
- Computer Vision Practitioners
- Machine Learning Engineers
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.