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Duration 21 hours
Course Outline
Foundations of Computer Vision in Robotics
- Exploration of computer vision applications within robotics
- Addressing key challenges in perception and visual understanding
- Configuring the development environment with OpenCV and Python
Core Image Processing Concepts
- Image representation and manipulation techniques
- Filtering, edge detection, and feature extraction methods
- Working with color spaces and segmentation approaches
Object Detection and Tracking using OpenCV
- Identifying objects using classical methods such as Haar cascades and HOG
- Tracking moving objects within video streams
- Incorporating visual feedback into robotic system operations
Deep Learning for Visual Perception
- Understanding convolutional neural networks (CNNs)
- Training and deploying object detection models
- Utilizing pre-trained architectures like YOLO, SSD, and Faster R-CNN
Sensor Fusion and Depth Perception
- Combining camera data with LiDAR and ultrasonic sensors
- Performing depth estimation and 3D reconstruction
- Enabling 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 strategies
- Facilitating autonomous decision-making based on visual input
Deployment and Optimization of Vision Models
- Deploying models on embedded systems and edge devices
- Optimizing inference performance for real-time scenarios
- Debugging issues and enhancing model accuracy
Conclusions and Future Directions
Requirements
- A foundational understanding of basic robotics concepts
- Proficiency in Python programming
- A 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.
