Example Concept: Sensor fusion in robotics involves integrating data from various sensors to improve localization accuracy. By using techniques such as Kalman filtering or particle filtering, the system can combine GPS data for global positioning, IMU data for orientation and acceleration, and visual data from cameras for environmental context. This multi-sensor approach helps to reduce noise, correct drift, and provide a more robust estimate of the robot's state, especially in complex or dynamic environments.