Szenzorfúziós

Szenzorfúzió refers to the process of combining data from multiple sensors to create a more comprehensive and accurate understanding of the environment or an object. This technique is employed in various fields, including robotics, autonomous vehicles, augmented reality, and industrial automation. By integrating information from different sensor types, such as cameras, lidar, radar, and infrared sensors, a system can overcome the limitations of individual sensors and achieve improved performance.

The primary goal of szenzorfúzió is to enhance situational awareness. For instance, a camera might provide rich visual information but struggle in low-light conditions, while infrared sensors excel at detecting heat signatures but offer limited spatial detail. Combining these allows for robust perception across diverse scenarios. This fusion can lead to more precise object detection, tracking, and identification, as well as more reliable navigation and decision-making.

Various methods exist for implementing szenzorfúzió, often categorized into low-level fusion (combining raw sensor data) and high-level fusion (combining processed information from individual sensors). Algorithms like Kalman filters, particle filters, and deep learning models are commonly used to achieve effective data integration. The success of szenzorfúzió depends on factors such as sensor calibration, synchronization, and the appropriate choice of fusion algorithms based on the specific application requirements.