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, correlate, and process heterogeneous sensor inputs, such as cameras, LiDAR, radar, IMUs, and GPS, to create a unified representation of the environment or system state. This is critical in applications like autonomous vehicles, robotics, and IoT systems where robust perception and decision-making are required.
Developers should learn sensor fusion when building systems that require high-precision environmental awareness or state estimation, such as in autonomous driving, drone navigation, or industrial automation. It is essential for reducing uncertainty, handling sensor failures, and improving overall system reliability by leveraging complementary sensor strengths. Use cases include object tracking, localization, and motion prediction in safety-critical or real-time applications.