Example Concept: Sensor fusion in robotics involves combining data from multiple sensors to create a more accurate and comprehensive understanding of the environment. This process requires addressing challenges like temporal synchronization, spatial alignment, and data consistency. Techniques such as Kalman filters, particle filters, and probabilistic models are commonly used to integrate data from sensors like LIDAR, cameras, and IMUs, enabling robust perception and decision-making in autonomous systems.