Sensor Fusion
Sensor fusion is a technique that combines data from multiple sensors to produce more accurate, reliable, and comprehensive information than could be obtained from any single sensor alone. It involves algorithms and methods to integrate inputs from sources like cameras, LiDAR, radar, IMUs, and GPS, often using probabilistic models such as Kalman filters or Bayesian networks. This is critical in applications like autonomous vehicles, robotics, and IoT systems where robust environmental perception is essential.
Developers should learn sensor fusion when working on systems that require high-precision situational awareness, such as self-driving cars, drones, or industrial automation, where single sensors are prone to noise, errors, or limitations. It enables better decision-making by reducing uncertainty and improving data integrity, making it vital for safety-critical and real-time applications. Knowledge of sensor fusion is also valuable in fields like augmented reality, smart devices, and environmental monitoring.