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Duration 21 hours
Course Outline
Foundations of Computer Vision in Robotics
- Surveying the role of computer vision in robotic applications
- Addressing critical challenges in visual perception and understanding
- Establishing a development environment with OpenCV and Python
Essentials of Image Processing
- Techniques for image representation and manipulation
- Applying filtering, edge detection, and feature extraction methods
- Exploring color spaces and segmentation strategies
Object Detection and Tracking via OpenCV
- Identifying objects using traditional approaches (Haar cascades, HOG)
- Tracking moving entities within video streams
- Embedding visual feedback loops into robotic systems
Deep Learning for Enhanced Visual Perception
- Understanding convolutional neural networks (CNNs)
- Processes for training and deploying object detection models
- Utilizing pre-trained architectures (YOLO, SSD, Faster R-CNN)
Sensor Fusion and Depth Analysis
- Merging camera data with LiDAR and ultrasonic sensor inputs
- Estimating depth and reconstructing 3D environments
- Enhancing obstacle avoidance and navigation through perception
Vision-Driven Control and Decision Logic
- Applying computer vision to precise robotic manipulation
- Implementing visual servoing and closed-loop control mechanisms
- Enabling autonomous decision-making based on visual inputs
Model Deployment and Performance Optimization
- Running vision models on embedded systems and edge devices
- Refining inference speed for real-time operational requirements
- Diagnosing issues and enhancing model accuracy
Conclusion and Future Pathways
Requirements
- A solid grasp of fundamental robotics principles
- Proficiency in Python programming
- Knowledge of core machine learning concepts
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.