RI-Small: Motion-induced Extrinsic Calibration of Rigid and Reconfigurable Networks of Sensors

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

Navigation sensors are found not only on robots, but also on passenger vehicles, portable devices, such as cell phones, and on wheel-chairs and white-canes that aid people with disabilities. For several applications, sensor-fusion algorithms are employed to combine measurements from multiple sensors rigidly attached to the same vehicle, or spatially dispersed and mobile. In either case, their relative spatial configuration must be known. This introduces the need for sensor-to-sensor extrinsic calibration. Moreover, in order for sensor measurements to be useful for high-precision navigation, their placement on the host robot must be determined, which brings about the problem of sensor-to-body calibration. To this day, very little is known about how to rigorously perform sensor-to-sensor and sensor-to-body extrinsic calibration. Typically, the transformations between the sensor and/or robot frames of interest are estimated using expensive calibration equipment, or found through approximate manual measurements and use of CAD plots. The objectives of this research effort are to develop rigorous methods for motion-induced sensor-to-sensor and sensor-to-body calibration, and to conduct a detailed theoretical study of their performance. The results of this work will significantly improve the quality and reduce the effort needed for multi-sensor calibration, thus providing valuable tools for designing and implementing navigation systems.