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Course Outline
Introduction to Computer Vision for Robotics
- Overview of computer vision applications within robotics.
- Key challenges related to perception and visual understanding.
- Setting up the development environment with OpenCV and Python.
Image Processing Fundamentals
- Image representation and manipulation techniques.
- Filtering, edge detection, and feature extraction.
- Color spaces and segmentation techniques.
Object Detection and Tracking with OpenCV
- Object detection 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 techniques for obstacle avoidance and navigation.
Vision-Based Control and Decision Making
- Applying computer vision to robotic manipulation.
- Visual servoing and closed-loop control mechanisms.
- Autonomous decision-making driven by visual input.
Deploying and Optimizing Vision Models
- Deploying models on embedded systems and edge devices.
- Optimizing inference performance for real-time applications.
- Troubleshooting and enhancing model accuracy.
Summary and Next Steps
Requirements
- Understanding of fundamental robotics concepts.
- Experience in Python programming.
- Familiarity with the core principles of machine learning.
Target Audience
- Robotics engineers.
- Computer vision specialists.
- 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.
