Multi-Sensor Data Fusion for Autonomous Navigation

• 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

Multi-Sensor Data Fusion for Autonomous Navigation is a specialized course designed to teach specialists how to integrate data from multiple sensors to enhance navigation, perception, and decision-making in autonomous vehicles.

This instructor-led, live training (online or onsite) is aimed at advanced-level sensor fusion specialists and AI engineers who wish to develop multi-sensor fusion algorithms and optimize real-time navigation in autonomous systems.

By the end of this training, participants will be able to:

• Understand the fundamentals and challenges of multi-sensor data fusion.
• Implement sensor fusion algorithms for real-time autonomous navigation.
• Integrate data from LiDAR, cameras, and RADAR for perception enhancement.
• Analyze and evaluate fusion system performance under various conditions.
• Develop practical solutions for sensor noise reduction and data alignment.

Format of the Course

• Interactive lecture and discussion.
• Lots of exercises and practice.
• Hands-on implementation in a live-lab environment.

Course Customization Options

• To request a customized training for this course, please contact us to arrange.

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