What Is Sensor Fusion?
Sensor fusion combines information from multiple senses to form a more accurate image of one's surroundings. It's gaining a complete scenario picture by integrating data from various sensors, such as cameras, microphones, etc. If you were driving a car through a busy city, you might rely on several sensors to help you escape collisions and make good choices. Cameras can determine where other vehicles are, radar can measure how far away an item is, and global positioning systems can pinpoint your exact location. You can get a clearer image of your environment and make more informed choices if you compile all this information. Sensor fusion has many applications, from autonomous vehicles and robots to video games and augmented reality. Integrating information from several devices allows designers to create more accurate data models or collect more information for a specific system. Cameras, LIDAR, radar, and GPS, might all work together to help a self-driving vehicle get where it needs to go. Lane markers and other vehicles could be spotted by the cameras, while LIDAR and infrared could gauge their relative distances. The car can make better choices about how to proceed on the road thanks to integrating all this information. The field of virtual reality is another illustration. A user's head, hands, and feet could all be tracked by sensors for a more interactive and realistic feel. The virtual reality system improves the realism of its depiction of the user's motions by combining data from multiple sensors. To sum up, sensor fusion combines data from multiple sensors to gain a more in-depth knowledge of a situation. It's like your senses—sight, hearing, and smell—are all amplified to provide you with a 360° panorama of your environment. This method helps planners create more robust data models or get more data points for a given system, making it applicable to various disciplines, from robotics to self-driving vehicles to virtual reality.
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