Example Concept: Sensor fusion in robotics involves combining data from multiple sensors (e.g., LiDAR, cameras, IMUs) to create a comprehensive understanding of the environment. Challenges include managing different data formats and rates, ensuring time synchronization, and dealing with sensor noise and drift. Techniques such as Kalman filtering, particle filtering, and deep learning-based fusion are commonly used to address these issues and improve the reliability of the navigation system.