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Duration 21 hours
Course Outline
Foundations of Computer Vision in Robotics
- Survey of computer vision applications within robotics
- Primary challenges in perception and visual interpretation
- Configuring the development environment using OpenCV and Python
Core Principles of Image Processing
- Methods for image representation and manipulation
- Techniques for filtering, edge detection, and feature extraction
- Utilizing color spaces and segmentation methods
Object Detection and Tracking via OpenCV
- Identifying objects through classical approaches (Haar cascades, HOG)
- Monitoring moving objects within video streams
- Incorporating visual feedback mechanisms into robotic systems
Deep Learning Approaches for Visual Perception
- Introduction to convolutional neural networks (CNNs)
- Processes for training and deploying object detection models
- Implementation of pre-trained models (YOLO, SSD, Faster R-CNN)
Sensor Fusion and Depth Analysis
- Combining camera data with LiDAR and ultrasonic sensor inputs
- Methods for depth estimation and 3D reconstruction
- Perception strategies for obstacle avoidance and navigation
Vision-Driven Control and Decision Logic
- Applying computer vision techniques to robotic manipulation
- Visual servoing and closed-loop control strategies
- Autonomous decision-making processes derived from visual inputs
Deployment and Optimization of Vision Models
- Implementing models on embedded systems and edge devices
- Enhancing inference performance for real-time operational needs
- Debugging and refining model accuracy
Conclusion and Path Forward
Requirements
- Foundational knowledge of robotics principles
- Proficiency in Python programming
- Basic familiarity with machine learning concepts
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.
