Get in Touch
 Duration 21 hours

Course Outline

Introduction to Computer Vision in Robotics

  • Survey of computer vision applications within robotics
  • Primary challenges in perception and visual interpretation
  • 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 spaces and segmentation strategies

Object Detection and Tracking using OpenCV

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

Deep Learning for Visual Perception

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

Sensor Fusion and Depth Perception

  • Combining camera data with LiDAR and ultrasonic sensors
  • Depth estimation and 3D reconstruction techniques
  • Perception strategies 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 Next Steps

Requirements

  • Foundation in basic robotics concepts
  • Proficiency in Python programming
  • Knowledge of machine learning fundamentals

Target Audience

  • Robotics engineers
  • Computer vision practitioners
  • Machine learning engineers

Number of participants


Price per participant

Testimonials (2)

Upcoming Courses

Related Categories