szenzorfúziót

Szenzorfúzió refers to the process of combining information from multiple sensors to create a more comprehensive and accurate understanding of the environment or a system. This technique is employed in various fields, including robotics, autonomous vehicles, artificial intelligence, and medical imaging. The fundamental principle behind szenzorfúzió is that individual sensors often have limitations or blind spots. By integrating data from diverse sources, these weaknesses can be compensated for, leading to improved perception, decision-making, and performance.

Different types of sensors can be fused, such as cameras, lidar, radar, sonar, accelerometers, and gyroscopes. The fusion process can occur at different levels. Raw data from sensors might be combined directly (low-level fusion), or the processed information from each sensor could be integrated (high-level fusion). Intermediate fusion, where features extracted from sensor data are combined, is also common. The specific algorithms and techniques used for szenzorfúzió depend on the application and the characteristics of the sensors involved. Common methods include Bayesian filtering, Kalman filters, and various machine learning approaches. The goal is to reduce uncertainty, enhance robustness, and provide a richer representation of the sensed world than any single sensor could achieve on its own.