szenzorfúziója

Szenzorfúziója, often translated as sensor fusion, is the process of combining data from multiple sensors to produce more accurate, complete, or reliable information than could be obtained from any single sensor alone. This integration allows for a richer understanding of the environment or the system being monitored.

The core principle behind sensor fusion is that different sensors typically have complementary strengths and weaknesses. By combining their outputs, the limitations of individual sensors can be mitigated, leading to improved performance. For example, a camera might provide rich visual information but struggle in low light conditions, while an infrared sensor could excel in darkness but offer less detail. Fusing these two sensors could create a system that works effectively in a wide range of lighting conditions.

Various techniques are employed for sensor fusion, ranging from simple averaging to complex probabilistic models like Kalman filters and Bayesian networks. The choice of method depends on the type of sensors involved, the nature of the data they produce, and the specific application.

Sensor fusion finds applications in a wide array of fields. In autonomous vehicles, it is crucial for tasks such as object detection, localization, and navigation, combining data from cameras, lidar, radar, and GPS. In robotics, it enables robots to perceive and interact with their surroundings more effectively. Other applications include medical imaging, industrial automation, and environmental monitoring. The ultimate goal of sensor fusion is to create a more robust and intelligent system by leveraging the collective intelligence of its sensory inputs.