Example Concept: Multi-sensor data integration requires the fusion of information from various sensors such as LiDAR, cameras, and IMUs. Each sensor type provides different data characteristics — for instance, LiDAR offers precise distance measurements, while cameras provide rich visual information. The challenge lies in aligning these data streams in time and space, compensating for sensor noise, and resolving discrepancies to form a unified perception model. Techniques like Kalman filtering, particle filtering, and SLAM (Simultaneous Localization and Mapping) are commonly used to address these challenges by estimating the robot's state and environment in a probabilistic framework.