Modern robotic systems – in drones or autonomous vehicles, for example – use a variety of sensors, ranging from cameras and accelerometers to GPS modules. To date, their correct integration has required expert knowledge and time-consuming calibration.
Christian Brommer, Alessandro Fornasier, Jan Steinbrener and Stephan Weiss, all members of the Control of Networked Systems research group at the time the research was conducted, have developed a new method and published it in the renowned journal IEEE Transactions on Robotics (T-RO). This method allows robots to automatically identify the type of a newly added sensor, estimate its position and orientation, and correctly integrate it into the existing navigation system.
According to Christian Brommer, it is no longer necessary to know which sensor is being used with the method being presented. Whether GPS, magnetometer/compass or speedometer, the data can simply be passed on to the algorithm and the sensor model is automatically recognised. However, the researchers still need some movement for recognition, as he goes on to explain: “This can be managed, for example, by holding the device in your hand in a laboratory or, as we demonstrate in the paper, during flight with a quadcopter or while driving a car.”
The need for this method is undeniable: GitHub, a platform for open-source projects, has registered more than 14,000 requests from developers using the keywords ‘sensor model integration.’ “Our work aims to make the integration of sensors into localisation solutions such as filters easier, faster and more robust”, says Christian Brommer.