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Duration 21 hours
Course Outline
Introduction to Computer Vision for Robotics
- Examining computer vision applications within robotics
- Addressing core challenges in perception and visual understanding
- Configuring the development environment using OpenCV and Python
Image Processing Fundamentals
- Understanding image representation and manipulation
- Techniques for filtering, edge detection, and feature extraction
- Working with color spaces and segmentation methods
Object Detection and Tracking with OpenCV
- Identifying objects via classical methods such as Haar cascades and HOG
- Tracking dynamic objects within video streams
- Embedding visual feedback loops into robotic systems
Deep Learning for Visual Perception
- Foundations of Convolutional Neural Networks (CNNs)
- Processes for training and deploying object detection models
- Leveraging pre-trained architectures like YOLO, SSD, and Faster R-CNN
Sensor Fusion and Depth Perception
- Merging camera data with LiDAR and ultrasonic sensor inputs
- Methods for depth estimation and 3D reconstruction
- Enabling perception for obstacle avoidance and navigation
Vision-Based Control and Decision Making
- Applying computer vision to robotic manipulation tasks
- Implementing visual servoing and closed-loop control strategies
- Facilitating autonomous decisions driven by visual inputs
Deploying and Optimizing Vision Models
- Running models on embedded systems and edge devices
- Enhancing inference performance for real-time applications
- Refining accuracy through troubleshooting and optimization
Summary and Next Steps
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.
