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Duration 21 hours
Course Outline
Introduction to Computer Vision in Robotics
- An overview of computer vision applications within the robotics domain
- Examining key challenges in perception and visual understanding
- Configuring the development environment with OpenCV and Python
Image Processing Essentials
- Understanding image representation and manipulation
- Techniques for filtering, edge detection, and feature extraction
- Working with color spaces and segmentation methods
Object Detection and Tracking via OpenCV
- Identifying objects using classical approaches (Haar cascades, HOG)
- Tracking moving objects within video streams
- Integrating visual feedback mechanisms into robotic systems
Deep Learning for Visual Perception
- An introduction to convolutional neural networks (CNNs)
- Processes for training and deploying object detection models
- Utilizing pre-trained models (YOLO, SSD, Faster R-CNN)
Sensor Fusion and Depth Perception
- Combining camera data with LiDAR and ultrasonic sensor inputs
- Methods for depth estimation and 3D reconstruction
- Enhancing perception for obstacle avoidance and navigation
Vision-Based Control and Decision-Making
- Applying computer vision principles to robotic manipulation
- Implementing visual servoing and closed-loop control strategies
- Facilitating autonomous decision-making driven by visual input
Deploying and Optimizing Vision Models
- Implementing models on embedded systems and edge devices
- Optimizing inference performance for real-time operational needs
- Techniques for troubleshooting and enhancing model accuracy
Summary and Future Directions
Requirements
- A foundational grasp of basic robotics concepts
- Proficiency in Python programming
- Working knowledge of machine learning fundamentals
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.
