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Duration 21 hours
Course Outline
Foundations of Computer Vision in Robotics
- Exploring key applications of computer vision within robotics
- Understanding critical challenges in visual perception and understanding
- Configuring the development environment with OpenCV and Python
Core Principles of Image Processing
- Techniques for image representation and manipulation
- Methods for filtering, edge detection, and feature extraction
- Utilizing color spaces and advanced segmentation techniques
Object Detection and Tracking via OpenCV
- Identifying objects using traditional approaches (Haar cascades, HOG)
- Tracking dynamic objects within video streams
- Incorporating visual feedback into robotic system architectures
Leveraging Deep Learning for Visual Perception
- An introduction to Convolutional Neural Networks (CNNs)
- Processes for training and deploying object detection models
- Implementing pre-trained architectures (YOLO, SSD, Faster R-CNN)
Sensor Fusion and Depth Sensing
- Combining camera data with LiDAR and ultrasonic sensor inputs
- Estimating depth and performing 3D reconstruction
- Enhancing obstacle avoidance and navigation capabilities through perception
Vision-Driven Control and Decision Logic
- Applying computer vision principles to robotic manipulation tasks
- Implementing visual servoing and closed-loop control strategies
- Enabling autonomous decision-making driven by visual data
Deployment and Optimization of Vision Models
- Running models on embedded systems and edge devices
- Enhancing inference performance for real-time operational needs
- Debugging and refining model accuracy
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental robotics concepts
- Proficiency in Python programming
- Foundational knowledge of machine learning principles
Target Audience
- Robotics Engineers
- Computer Vision Specialists
- 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.
