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Course Outline

Introduction to 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

Foundations of Image Processing

  • Image representation and manipulation techniques
  • Filtering, edge detection, and feature extraction methods
  • Color space management and segmentation strategies

Object Detection and Tracking via OpenCV

  • Object detection using classical algorithms (Haar cascades, HOG)
  • Tracking moving objects within video streams
  • Incorporating visual feedback into robotic system control

Deep Learning for Visual Perception

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

Sensor Fusion and Depth Perception

  • Merging camera data with LiDAR and ultrasonic sensor inputs
  • Depth estimation and 3D scene reconstruction
  • Perception techniques for obstacle avoidance and navigation

Vision-Based Control and Decision-Making

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

Deployment and Optimization of Vision Models

  • Deploying models on embedded systems and edge devices
  • Optimizing inference performance for real-time scenarios
  • Troubleshooting and enhancing model accuracy

Summary and Future Directions

Requirements

  • A foundational understanding of basic robotics concepts
  • Proficiency in Python programming
  • A solid grasp of machine learning fundamentals

Target Audience

  • Robotics engineers
  • Computer vision professionals
  • Machine learning engineers
 21 Hours

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