Welcome to Level 3 of RealSense University! This advanced level focuses on integrating RealSense depth perception with AI, SLAM, and embodied robotics systems.
Learning Objectives
By the end of Level 3, you will be able to:
- Implement Visual SLAM and 3D mapping systems
- Integrate multiple sensors for robust perception
- Build AI-powered perception pipelines
- Develop cloud robotics solutions
- Create autonomous navigation systems
Prerequisites
- Completion of Level 2 or equivalent experience
- Advanced programming skills (Python, C++)
- Understanding of computer vision and AI concepts
- Familiarity with ROS2 and robotics frameworks
- Experience with machine learning libraries
Modules Overview
Required Hardware & Software
Hardware
- RealSense Camera: D455 or D457 (IMU-enabled)
- High-performance Computer: GPU recommended
- Additional Sensors: LiDAR, IMU, wheel encoders
- Robotics Platform: Mobile robot or simulation environment
Software
- ROS2 Humble/Iron: Latest version
- OpenCV 4.5+: Computer vision
- Open3D: 3D processing
- PyTorch/TensorFlow: AI frameworks
- RTAB-Map/ORB-SLAM2: SLAM libraries
- Docker: Containerization
Quick Start Guide
- Complete Level 2 or ensure advanced experience
- Set up development environment with GPU support
- Install SLAM libraries and AI frameworks
- Complete modules in order for best learning experience
- Build the autonomous navigation project
Module Details
Module 1: Visual SLAM & Mapping
Master simultaneous localization and mapping with RealSense cameras.
Key Topics:
- RTAB-Map integration with RealSense
- ORB-SLAM2 for visual SLAM
- Loop closure detection
- Pose estimation and tracking
Module 2: Sensor Fusion
Integrate multiple sensors for robust perception systems.
Key Topics:
- RealSense + LiDAR fusion
- IMU integration and calibration
- Multi-camera setups
- Data synchronization
- Kalman filtering and sensor fusion
Module 3: AI Perception Pipelines
Build AI-powered perception systems using RealSense data.
Key Topics:
- RGB-D object detection
- 3D semantic segmentation
- Real-time inference optimization
- Edge AI deployment
Module 4: Remote and Cloud Robotics
Develop cloud-based robotics solutions with RealSense.
Key Topics:
- ROS2 + Zenoh integration
- Cloud inference services
- Edge-cloud collaboration
- Distributed robotics systems
Module 5: Mini Project: Autonomous Navigation
Build a practical application that measures distances to objects in your environment.
Key Topics:
- SLAM-based navigation
- Dynamic obstacle avoidance
- Path planning integration
- Multi-sensor fusion
- Real-time performance optimization
Getting Help
If you encounter issues:
- Check the troubleshooting section
- Review the FAQ
- Join our Discord community
- Search GitHub issues
- ROS2 documentation
Completion Certificate
Upon completing all modules and the mini project, you’ll receive a Level 3 Completion Achievement and be ready to advance to Level 4: Expert.
Course Content
Module 1: Visual SLAM & Mapping
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Module 2: Sensor Fusion
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Module 3: AI Perception Pipelines
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Module 4: Remote and Cloud Robotics
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Module 5: Mini Project – Autonomous Navigation
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Level 3: Advanced – Final Quiz
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