sensorfüusiooni

Sensor fusion is the process of combining data from multiple sensors to achieve a more accurate, complete, or reliable understanding of a system or environment than could be obtained from any single sensor alone. This integration can overcome the limitations of individual sensors, such as noise, limited field of view, or susceptibility to specific environmental conditions. By leveraging the strengths of different sensor types, sensor fusion can provide a more robust and comprehensive perception.

The core idea behind sensor fusion is to exploit the complementary information provided by diverse sensing modalities. For instance, a camera might provide visual information, while a lidar sensor offers precise depth measurements, and a radar sensor can detect objects through obscurants like fog or rain. Combining these allows for enhanced object detection, tracking, and scene understanding.

Various techniques exist for implementing sensor fusion, ranging from simple averaging or voting schemes to more complex probabilistic methods like Kalman filters, particle filters, and Bayesian networks. The choice of method often depends on the specific application, the types of sensors involved, and the desired level of accuracy and computational complexity. Applications of sensor fusion are widespread, including autonomous vehicles, robotics, medical imaging, weather forecasting, and industrial automation, where improved situational awareness is critical for decision-making and control.