ATOM: A general calibration framework for multi-modal, multi-sensor systems

Abstract

The fusion of data from different sensors often requires that an accurate geometric transformation between the sensors is known. The procedure by which these transformations are estimated is known as sensor calibration. The vast majority of calibration approaches focus on specific pairwise combinations of sensor modalities, unsuitable to calibrate robotic systems containing multiple sensors of varied modalities. This paper presents a novel calibration methodology which is applicable to multi-sensor, multi-modal robotic systems. The approach formulates the calibration as an extended optimization problem, in which the poses of the calibration patterns are also estimated. It makes use of a topological representation of the coordinate frames in the system, in order to recalculate the poses of the sensors throughout the optimization. Sensor poses are retrieved from the combination of geometric transformations which are atomic, in the sense that they are indivisible. As such, we refer to this approach as ATOM — Atomic Transformations Optimization Method. This makes the approach applicable to different calibration problems, such as sensor to sensor, sensor in motion, or sensor to coordinate frame. Additionally, the proposed approach provides advanced functionalities, integrated into ROS, designed to support the several stages of a complete calibration procedure. Results covering several robotic platforms and a large spectrum of calibration problems show that the methodology is in fact general, and achieves calibrations which are as accurate as the ones provided by state of the art methods designed to operate only for specific combinations of pairwise modalities.