What is meant by sensor fusion in robotics?

Sensor fusion refers to the integration of data from multiple sensors to achieve more accurate and reliable information than what could be obtained from individual sensors alone. By combining the readings from various sensors, such as cameras, lidar, and accelerometers, a robot can create a comprehensive understanding of its environment. This process enhances the robot’s ability to detect objects, assess distances, and interpret complex scenes, contributing to improved decision-making and performance.

The effectiveness of sensor fusion lies in its ability to leverage the strengths of different sensors while compensating for their individual weaknesses. For example, a camera might provide detailed visual information, but it can struggle in low light conditions, whereas other sensors such as lidar can provide accurate distance measurements regardless of lighting. By merging the data from these sensors, the robot can achieve greater situational awareness and reliability.

The other choices do not accurately capture the concept of sensor fusion. Replacing sensors with algorithms doesn’t encompass the idea of integrating different sources of information. Creating redundant sensors focuses on backup and reliability but does not inherently improve data accuracy through integration. Separating sensor functions, while it might enhance individual performance, does not relate to the collaborative approach that defines sensor fusion.