Example Concept: Sensor fusion in robotics involves combining data from multiple sensors to create a coherent understanding of the environment. Key challenges include synchronizing data streams with different update rates, handling noise and uncertainty in sensor measurements, and ensuring low-latency processing to maintain real-time performance. Techniques such as Kalman filtering, Bayesian inference, and machine learning models are often used to address these issues, providing robust estimates of the robot's state and environment.