Example Concept: Sensor fusion combines data from multiple sensors (e.g., LiDAR, cameras, ultrasonic sensors) to create a more accurate representation of the robot's environment. By integrating data from different modalities, the robot can better detect and classify obstacles, improving both accuracy and reliability. Techniques such as Kalman filtering or particle filtering are often used to fuse sensor data, reducing noise and increasing the robustness of obstacle detection.