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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 understanding
  • Establishing a development environment with OpenCV and Python

Core Image Processing Concepts

  • Managing image representation and manipulation
  • Applying filtering, edge detection, and feature extraction
  • Utilizing color spaces and segmentation techniques

Object Detection and Tracking via OpenCV

  • Identifying objects using classical methods (Haar cascades, HOG)
  • Tracking moving objects within video streams
  • Incorporating visual feedback into robotic systems

Deep Learning for Enhanced Visual Perception

  • Understanding convolutional neural networks (CNNs)
  • Training and implementing object detection models
  • Leveraging pre-trained models (YOLO, SSD, Faster R-CNN)

Sensor Fusion and Depth Perception

  • Combining camera data with LiDAR and ultrasonic sensors
  • Performing depth estimation and 3D reconstruction
  • Enabling obstacle avoidance and navigation through perception

Vision-Driven Control and Decision-Making

  • Applying computer vision to robotic manipulation tasks
  • Implementing visual servoing and closed-loop control
  • Facilitating autonomous decision-making based on visual inputs

Deployment and Optimization of Vision Models

  • Implementing models on embedded systems and edge devices
  • Enhancing inference performance for real-time scenarios
  • Debugging and refining accuracy

Concluding Remarks and Future Directions

Requirements

  • A solid grasp of fundamental robotics concepts
  • Proficiency in Python programming
  • A working knowledge of machine learning fundamentals

Target Audience

  • Robotics engineers
  • Computer vision specialists
  • Machine learning engineers

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