A General Approach to Hand–Eye Calibration Through the Optimization of Atomic Transformations

This article proposes a general approach to solve the hand–eye calibration problem. The system is general since it is able to calibrate any number of cameras and, moreover, is able to simultaneously perform the calibration of several instances of the two common hand–eye calibration use cases: eye-on-hand and eye-to-base. The calibration is solved with a nonlinear least squares method, and the reprojection error is used as a metric to guide the optimization procedure. Our approach is seamlessly integrated with the robot operating system framework and allows for the interactive positioning of sensors and labeling of data, facilitating both the data acquisition and labeling and the calibration procedures. Results show that the proposed approach is able to handle any calibration use case with a minimal initial configuration. The approach is compared with several other state-of-the-art hand–eye calibration algorithms. Results show that the proposed approach produces very accurate calibrations when compared to the state of the art.