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Duration 21 hours
Course Outline
Foundations of Computer Vision in Robotics
- Exploring the role of computer vision in robotic applications
- Addressing key challenges in perception and visual interpretation
- Configuring the development environment with OpenCV and Python
Core Principles of Image Processing
- Handling image representation and data 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 such as Haar cascades and HOG
- Tracking moving objects within video streams
- Incorporating visual feedback into robotic control loops
Enhancing Visual Perception with Deep Learning
- Introduction to Convolutional Neural Networks (CNNs)
- Process of training and deploying object detection models
- Utilizing pre-trained architectures like YOLO, SSD, and Faster R-CNN
Sensor Fusion and Depth Awareness
- Combining camera feeds with LiDAR and ultrasonic sensor data
- Methods for depth estimation and 3D scene reconstruction
- Implementing perception for obstacle avoidance and navigation
Vision-Driven Control and Decision-Making
- Applying computer vision to robotic manipulation tasks
- Implementing visual servoing and closed-loop control mechanisms
- Enabling autonomous decisions based on visual inputs
Deployment and Optimization of Vision Models
- Running models on embedded systems and edge devices
- Optimizing inference speed for real-time performance
- 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 professionals
- 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.