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Course Outline
Introduction to Computer Vision in Robotics
- Survey of computer vision applications within the robotics domain.
- Analyzing critical challenges in perception and visual understanding.
- Configuring the development environment using OpenCV and Python.
Image Processing Fundamentals
- Techniques for image representation and manipulation.
- Applying filtering, edge detection, and feature extraction methods.
- Utilizing color spaces and advanced segmentation techniques.
Object Detection and Tracking via OpenCV
- Identifying objects using classical approaches such as Haar cascades and HOG.
- Implementing tracking algorithms for moving objects in video streams.
- Incorporating visual feedback mechanisms into robotic systems.
Deep Learning for Visual Perception
- Introduction to convolutional neural networks (CNNs).
- Strategies for training and deploying object detection models.
- Leveraging pre-trained architectures like YOLO, SSD, and Faster R-CNN.
Sensor Fusion and Depth Perception
- Synthesizing camera data with inputs from LiDAR and ultrasonic sensors.
- Executing depth estimation and 3D reconstruction tasks.
- Enhancing obstacle avoidance and navigation through perceptual data.
Vision-Based Control and Decision Making
- Applying computer vision techniques to robotic manipulation tasks.
- Implementing visual servoing and closed-loop control systems.
- Enabling autonomous decision-making processes driven by visual input.
Deploying and Optimizing Vision Models
- Implementing models on embedded systems and edge computing devices.
- Optimizing inference performance for real-time operational requirements.
- Diagnosing issues and refining model accuracy.
Conclusion and Future Directions
Requirements
- Solid grasp of fundamental robotics principles.
- Proficiency in Python programming.
- Working knowledge of machine learning core concepts.
Target Audience
- Robotics engineers.
- Computer vision practitioners.
- Machine learning engineers.
21 Hours
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.