Example Concept: Sensor fusion enhances perception by leveraging complementary sensor strengths. For instance, LIDAR provides precise distance measurements, while cameras offer rich visual details. By fusing these data sources, a robot can construct a comprehensive environmental model, improving depth perception and object recognition. This integration is often implemented using algorithms like Kalman filters, particle filters, or deep learning-based approaches, which help in refining sensor data into actionable insights.