Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Multi-Sensor Data Fusion
- Importance of data fusion in autonomous navigation
- Challenges of multi-sensor integration
- Applications of data fusion in real-time perception
Sensor Technologies and Data Characteristics
- LiDAR: Point cloud generation and processing
- Camera: Visual data capture and image processing
- RADAR: Object detection and speed estimation
- Inertial Measurement Units (IMUs): Motion tracking
Fundamentals of Data Fusion
- Mathematical foundations: Kalman filters, Bayesian inference
- Data association and alignment techniques
- Dealing with sensor noise and uncertainty
Fusion Algorithms for Autonomous Navigation
- Kalman Filter and Extended Kalman Filter (EKF)
- Particle Filter for nonlinear systems
- Unscented Kalman Filter (UKF) for complex dynamics
- Data association using Nearest Neighbor and Joint Probabilistic Data Association (JPDA)
Practical Sensor Fusion Implementation
- Integrating LiDAR and camera data for object detection
- Fusing RADAR and camera data for velocity estimation
- Combining GPS and IMU data for accurate localization
Real-Time Data Processing and Synchronization
- Time stamping and data synchronization methods
- Latency handling and real-time performance optimization
- Managing data from asynchronous sensors
Advanced Techniques and Challenges
- Deep learning approaches for data fusion
- Multi-modal data integration and feature extraction
- Handling sensor failures and degraded data
Performance Evaluation and Optimization
- Quantitative evaluation metrics for fusion accuracy
- Performance analysis under different environmental conditions
- Improving system robustness and fault tolerance
Case Studies and Real-World Applications
- Fusion techniques in autonomous vehicle prototypes
- Successful deployment of sensor fusion algorithms
- Workshop: Implementing a multi-sensor fusion pipeline
Summary and Next Steps
Requirements
- Experience with Python programming
- Knowledge of basic sensor technologies (e.g., LiDAR, cameras, RADAR)
- Familiarity with ROS and data processing
Audience
- Sensor fusion specialists working on autonomous navigation systems
- AI engineers focused on multi-sensor integration and data processing
- Researchers in the field of autonomous vehicle perception
21 Hours
