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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 comprehension
- Configuring the development environment with OpenCV and Python
Core Image Processing Principles
- Image representation and manipulation techniques
- Filtering, edge detection, and feature extraction methods
- Color spaces and segmentation approaches
Object Detection and Tracking via OpenCV
- Object detection using traditional techniques (Haar cascades, HOG)
- Tracking moving objects within video streams
- Integrating visual feedback into robotic systems
Deep Learning for Visual Perception
- Introduction to convolutional neural networks (CNNs)
- Training and deploying object detection models
- Utilizing pre-trained architectures (YOLO, SSD, Faster R-CNN)
Sensor Fusion and Depth Perception
- Merging camera data with LiDAR and ultrasonic sensor inputs
- Depth estimation and 3D reconstruction
- Perception strategies for obstacle avoidance and navigation
Vision-Driven Control and Decision Logic
- Applying computer vision to robotic manipulation tasks
- Visual servoing and closed-loop control mechanisms
- Autonomous decision-making driven by visual data
Deployment and Optimization of Vision Models
- Deploying models on embedded systems and edge devices
- Enhancing inference performance for real-time scenarios
- Debugging and refining model accuracy
Conclusion and Future Directions
Requirements
- A solid grasp of fundamental robotics concepts
- Proficiency in Python programming
- Knowledge of machine learning basics
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.
