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 Duration 21 hours

Course Outline

Foundations of Computer Vision in Robotics

  • Survey of computer vision applications in the robotics domain
  • Primary challenges in perception and visual understanding
  • Configuring the development environment with OpenCV and Python

Core Image Processing Techniques

  • Representing and manipulating image data
  • Filtering, edge detection, and feature extraction methods
  • Color spaces and segmentation strategies

Object Detection and Tracking via OpenCV

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

Deep Learning for Visual Perception

  • Introduction to convolutional neural networks (CNNs)
  • Training and deploying object detection models
  • Leveraging pre-trained architectures (YOLO, SSD, Faster R-CNN)

Sensor Fusion and Depth Perception

  • Merging camera data with LiDAR and ultrasonic sensor inputs
  • Estimating depth and performing 3D reconstruction
  • Perception strategies for obstacle avoidance and navigation

Vision-Driven Control and Decision-Making

  • Applying computer vision to robotic manipulation tasks
  • Visual servoing and closed-loop control mechanisms
  • Autonomous decision-making processes based on visual input

Deployment and Optimization of Vision Models

  • Implementing models on embedded systems and edge devices
  • Enhancing inference performance for real-time applications
  • Diagnosing issues and refining accuracy

Conclusion and Future Directions

Requirements

  • A solid grasp of fundamental robotics concepts
  • Proficiency in Python programming
  • A strong foundation in machine learning fundamentals

Target Audience

  • Robotics engineers
  • Computer vision practitioners
  • Machine learning engineers

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