Sensor fusion is the process of combining data from multiple sensors to produce more accurate, reliable, and comprehensive information than could be obtained from any single sensor alone. By integrating inputs from sensors such as accelerometers, gyroscopes, magnetometers, and sometimes even cameras or GPS, sensor fusion algorithms can filter out noise, reduce uncertainty, and estimate variables like position, orientation, or velocity with higher precision. This technique is widely used in robotics, autonomous vehicles, smartphones, and wearable devices to enhance navigation, motion tracking, and situational awareness. Sensor fusion often leverages advanced algorithms like Kalman filters, complementary filters, or neural networks to intelligently merge and interpret sensor data in real time.
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