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Struttura del corso
Introduction to Computer Vision for Robotics
- Overview of computer vision applications in robotics
- Key challenges in perception and visual understanding
- Setting up the development environment with OpenCV and Python
Image Processing Fundamentals
- Image representation and manipulation
- Filtering, edge detection, and feature extraction
- Color spaces and segmentation techniques
Object Detection and Tracking with OpenCV
- Detecting objects using classical methods (Haar cascades, HOG)
- Tracking moving objects in video streams
- Integrating visual feedback into robotic systems
Deep Learning for Visual Perception
- Overview of convolutional neural networks (CNNs)
- Training and deploying object detection models
- Applying pre-trained models (YOLO, SSD, Faster R-CNN)
Sensor Fusion and Depth Perception
- Integrating camera data with LiDAR and ultrasonic sensors
- Depth estimation and 3D reconstruction
- Perception for obstacle avoidance and navigation
Vision-Based Control and Decision Making
- Applying computer vision to robotic manipulation
- Visual servoing and closed-loop control
- Autonomous decision-making based on visual input
Deploying and Optimizing Vision Models
- Deploying models on embedded systems and edge devices
- Optimizing inference performance for real-time applications
- Troubleshooting and improving accuracy
Summary and Next Steps
Requisiti
- An understanding of basic robotics concepts
- Experience with Python programming
- Familiarity with machine learning fundamentals
Audience
- Robotics engineers
- Computer vision practitioners
- Machine learning engineers
21 Ore
Recensioni (1)
la sua conoscenza e l'utilizzo dell'IA per Robotics in futuro.
Ryle - PHILIPPINE MILITARY ACADEMY
Corso - Artificial Intelligence (AI) for Robotics
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