Example Concept: Sensor fusion in robotics typically involves integrating data from various sensors using algorithms like the Kalman Filter or Particle Filter. These algorithms process inputs from sensors such as GPS for global positioning, IMUs for orientation and acceleration, and LIDAR for environmental mapping. By fusing these data sources, the robot can achieve higher localization accuracy, compensating for the drift of IMUs, the noise in GPS signals, and the limited range of LIDAR.