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Duration 21 hours
Course Outline
Introduction to Computer Vision for Robotics
- Exploring the role of computer vision in robotic applications
- Addressing key challenges in perception and visual understanding
- Configuring the development environment with OpenCV and Python
Image Processing Fundamentals
- Representing and manipulating digital images
- Applying filtering, edge detection, and feature extraction techniques
- Working with colour spaces and segmentation methods
Object Detection and Tracking with OpenCV
- Identifying objects using classical algorithms (Haar cascades, HOG)
- Tracking moving objects within video streams
- Incorporating visual feedback into robotic system architectures
Deep Learning for Visual Perception
- An overview of convolutional neural networks (CNNs)
- Training and deploying object detection models
- Utilising pre-trained architectures (YOLO, SSD, Faster R-CNN)
Sensor Fusion and Depth Perception
- Combining camera data with LiDAR and ultrasonic sensor inputs
- Estimating depth and performing 3D reconstruction
- Enhancing obstacle avoidance and navigation through perception
Vision-Based Control and Decision Making
- Applying computer vision to enhance robotic manipulation
- Implementing visual servoing and closed-loop control mechanisms
- Enabling autonomous decision-making driven by visual inputs
Deploying and Optimizing Vision Models
- Implementing models on embedded systems and edge devices
- Optimising inference performance for real-time operational requirements
- Diagnosing issues and enhancing model accuracy
Summary and Next Steps
Requirements
- A solid grasp of fundamental robotics concepts
- Proficiency in Python programming
- A working knowledge of machine learning basics
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.
