Example Concept: Sensor fusion combines data from multiple sensors to create a more accurate and reliable representation of the environment. Key factors influencing accuracy include sensor calibration, synchronization, noise characteristics, and the fusion algorithm (e.g., Kalman filter, particle filter). Properly calibrated and synchronized sensors reduce errors, while robust algorithms can effectively manage noise and uncertainty to enhance system performance.