Robot Hand-Eye Calibration using Structure-from-Motion
This enables self-calibration for robots and unmanned vehicles in remote or unstructured environments, representing an incremental improvement over rig-based methods.
The paper tackles hand-eye calibration for robots by proposing a flexible method that uses structure-from-motion and known robot motions to solve for parameters linearly, eliminating the need for a calibration rig. Experiments validate the method's quality compared to existing techniques, though no specific numerical results are provided.
In this paper we propose a new flexible method for hand-eye calibration. The vast majority of existing hand-eye calibration techniques requires a calibration rig which is used in conjunction with camera pose estimation methods. Instead, we combine structure-from-motion with known robot motions and we show that the solution can be obtained in linear form. The latter solves for both the hand-eye parameters and for the unknown scale factor inherent with structure-from-motion methods. The algebraic analysis that is made possible with such a linear formulation allows to investigate not only the well known case of general screw motions but also such singular motions as pure translations, pure rotations, and planar motions. In essence, the robot-mounted camera looks to an unknown rigid layout, tracks points over an image sequence and estimates the camera-to-robot relationship. Such a self calibration process is relevant for unmanned vehicles, robots working in remote places, and so forth. We conduct a large number of experiments which validate the quality of the method by comparing it with existing ones.