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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
Delivery Options
Private Group Training
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- Pre-course call with your trainer
- Customisation of the learning experience to achieve your goals -
- Bespoke outlines
- Practical hands-on exercises containing data / scenarios recognisable to the learners
- Training scheduled on a date of your choice
- Delivered online, onsite/classroom or hybrid by experts sharing real world experience
Private Group Prices RRP from £5700 online delivery, based on a group of 2 delegates, £1800 per additional delegate (excludes any certification / exam costs). We recommend a maximum group size of 12 for most learning events.
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