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 Duration 21 hours

Course Outline

Foundations of Computer Vision in Robotics

  • Surveying the role of computer vision in robotic applications
  • Addressing critical challenges in visual perception and understanding
  • Establishing a development environment with OpenCV and Python

Essentials of Image Processing

  • Techniques for image representation and manipulation
  • Applying filtering, edge detection, and feature extraction methods
  • Exploring color spaces and segmentation strategies

Object Detection and Tracking via OpenCV

  • Identifying objects using traditional approaches (Haar cascades, HOG)
  • Tracking moving entities within video streams
  • Embedding visual feedback loops into robotic systems

Deep Learning for Enhanced Visual Perception

  • Understanding convolutional neural networks (CNNs)
  • Processes for training and deploying object detection models
  • Utilizing pre-trained architectures (YOLO, SSD, Faster R-CNN)

Sensor Fusion and Depth Analysis

  • Merging camera data with LiDAR and ultrasonic sensor inputs
  • Estimating depth and reconstructing 3D environments
  • Enhancing obstacle avoidance and navigation through perception

Vision-Driven Control and Decision Logic

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

Model Deployment and Performance Optimization

  • Running vision models on embedded systems and edge devices
  • Refining inference speed for real-time operational requirements
  • 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 practitioners
  • Machine learning engineers

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