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 Duration 21 hours

Course Outline

Foundations of Computer Vision in Robotics

  • Exploring the role of computer vision in robotic applications
  • Addressing key challenges in perception and visual interpretation
  • Configuring the development environment with OpenCV and Python

Core Principles of Image Processing

  • Handling image representation and data manipulation
  • Techniques for filtering, edge detection, and feature extraction
  • Working with color spaces and segmentation methods

Object Detection and Tracking via OpenCV

  • Identifying objects using classical approaches such as Haar cascades and HOG
  • Tracking moving objects within video streams
  • Incorporating visual feedback into robotic control loops

Enhancing Visual Perception with Deep Learning

  • Introduction to Convolutional Neural Networks (CNNs)
  • Process of training and deploying object detection models
  • Utilizing pre-trained architectures like YOLO, SSD, and Faster R-CNN

Sensor Fusion and Depth Awareness

  • Combining camera feeds with LiDAR and ultrasonic sensor data
  • Methods for depth estimation and 3D scene reconstruction
  • Implementing perception for obstacle avoidance and navigation

Vision-Driven Control and Decision-Making

  • Applying computer vision to robotic manipulation tasks
  • Implementing visual servoing and closed-loop control mechanisms
  • Enabling autonomous decisions based on visual inputs

Deployment and Optimization of Vision Models

  • Running models on embedded systems and edge devices
  • Optimizing inference speed for real-time performance
  • Diagnosing issues and enhancing model accuracy

Conclusion and Future Pathways

Requirements

  • A solid grasp of fundamental robotics principles
  • Proficiency in Python programming
  • Knowledge of core machine learning concepts

Target Audience

  • Robotics engineers
  • Computer vision professionals
  • Machine learning engineers

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