Example Concept: Sensor fusion integrates data from various sensors, such as LiDAR, cameras, IMUs, and GPS, to create a comprehensive environmental model. By using algorithms like Kalman filters or particle filters, sensor fusion refines the robot's position and orientation estimates, compensating for the weaknesses of each sensor type. This results in improved localization and mapping, which are crucial for effective path planning and obstacle avoidance in autonomous navigation.