Welcome to Level 2 of RealSense University! This level builds upon your beginner knowledge to create more sophisticated applications that integrate RealSense with robotics frameworks and computer vision tools.
Learning Objectives
By the end of Level 2, you will be able to:
- Process and visualize 3D point clouds effectively
- Integrate RealSense cameras with ROS2
- Build depth-based computer vision applications
- Develop cross-platform RealSense applications
- Create obstacle detection systems for robotics
Prerequisites
- Completion of Level 1 or equivalent experience
- Basic understanding of robotics concepts
- Familiarity with Linux command line
- Basic knowledge of ROS2 (helpful but not required)
- Understanding of computer vision fundamentals
Modules Overview
Required Hardware & Software
Hardware
- RealSense Camera**: D435, D455, or D457 recommended
- Development Computer: Linux (Ubuntu 20.04+), Windows 10+, or macOS
- Additional Sensors: IMU-enabled camera for advanced features
- Robotics Platform: Optional for ROS2 integration
Software
- RealSense SDK 2.0: Latest version
- ROS2: Humble, Iron, or Jazzy (for ROS2 modules)
- Open3D: 3D data processing
- OpenCV: Computer vision
- Python 3.10+: Development environment
Quick Start Guide
- Complete Level 1 or ensure you have equivalent experience
- Set up ROS2 following Module 2 instructions
- Install additional libraries for point cloud processing
- Complete modules in order for best learning experience
- Build the obstacle detection project to apply your knowledge
Module Details
Module 1: Working with Point Clouds
Master point cloud generation, filtering, and visualization techniques.
Key Topics:
- Point cloud generation and processing
- Filtering and noise reduction
- Visualization with Open3D and RViz
- Aligning depth and color streams
- Point cloud registration and merging
Module 2: Using RealSense in ROS2
Integrate RealSense cameras with the Robot Operating System 2.
Key Topics:
- Hardware setup and connections
- Installing RealSense SDK 2.0
- Testing with realsense-viewer
- Troubleshooting common issues
Module 3: Depth-Based Applications
Build practical applications using depth data for computer vision
Key Topics:
- Obstacle detection and distance alerts
- Background segmentation with depth masks
- Real-time object tracking
- Gesture recognition
- 3D object detection
Module 4: Cross-Platform Development
Develop RealSense applications for different platforms and architectures.
Key Topics:
- Running on NVIDIA Jetson platforms
- Optimizing for Raspberry Pi
- Performance and power optimization
- Cross-compilation techniques
- Platform-specific considerations
Module 5: Mini Project: Obstacle Detection
Build a complete obstacle detection system for autonomous mobile robots.
Key Topics:
- ROS2-based obstacle detection node
- Real-time depth processing
- Integration with robot navigation
- Performance optimization
Assessment & Progress
Each module includes:
- Hands-on exercises with real-world applications
- Code examples you can run and modify
- Integration challenges with robotics frameworks
- Performance optimization techniques
- Troubleshooting guides for common issues
Getting Help
If you encounter issues:
- Check the troubleshooting section
- Review the FAQ
- Join our Discord community
- Search GitHub issues
- Consult ROS2 documentation
Completion Certificate
Upon completing all modules and the mini project, you’ll receive a Level 2 Completion Achievement and be ready to advance to Level 3: Advanced.
Ready to start? Let’s begin with Module 1: Working with Point Clouds!