Abstract
Hand-eye calibration is the problem of estimating the spatial transformation between a reference frame, usually the base of a robot arm or its gripper, and the reference frame of one or multiple cameras. Generally, this calibration is solved as a non-linear optimization problem, what instead is rarely done is to exploit the underlying graph structure of the problem itself. Actually, the problem of hand-eye calibration can be seen as an instance of the Simultaneous Localization and Mapping (SLAM) problem. Inspired by this fact, in this work we present a pose-graph approach to the hand-eye calibration problem that extends a recent state-of-the-art solution in two different ways: i) by formulating the solution to eye-on-base setups with one camera; ii) by covering multi-camera robotic setups. The proposed approach has been validated in simulation against standard hand-eye calibration methods. Moreover, a real application is shown. In both scenarios, the proposed approach overcomes all alternative methods. We release with this paper an open-source implementation of our graph-based optimization framework for multi-camera setups.
Abstract (translated)
手眼校准是指测量一个参考框架(通常是机器人手臂的基座或抓手)与一个或多个相机的相对位置变换的问题。一般来说,这个问题是通过非线性优化问题来解决的,而很少使用的是利用问题本身的 underlying graph 结构。实际上,手眼校准的问题可以被视为同时定位和地图绘制(SLAM)问题的一个实例。基于这个事实,在本文中我们提出了一种姿态图方法来解决手眼校准问题,以两种方式扩展了最近先进的解决方案:第一种方式是通过制定一个针对单个相机的基础视角解决方案;第二种方式是通过覆盖多个相机的机器人 setups。该 proposed 方法是在模拟中验证了与标准手眼校准方法的兼容性。此外,还展示了一个实际应用场景。在这两种情况下,该 proposed 方法克服了所有其他方法。本文同时发布了我们为多相机 setups 开发的开放源代码实现的基于 graph 的优化框架。
URL
https://arxiv.org/abs/2303.04747
