Intelligent System Associate Laboratory (LASI), Institute of Electronics and Informatics Engineering of Aveiro (IEETA), University of Aveiro, 3810-193 Aveiro, Portugal.
Department of Mechanical Engineering, University of Aveiro, 3810-193 Aveiro, Portugal.
Sensors (Basel). 2023 Jan 13;23(2):936. doi: 10.3390/s23020936.
Robotic systems are evolving to include a large number of sensors and diverse sensor modalities. In order to operate a system with multiple sensors, the geometric transformations between those sensors must be accurately estimated. The process by which these transformations are estimated is known as sensor calibration. Behind every sensor calibration approach is a formulation and a framework. The formulation is the method by which the transformations are estimated. The framework is the set of operations required to carry out the calibration procedure. This paper proposes a novel calibration framework that gives more flexibility, control and information to the user, enhancing the user interface and the user experience of calibrating a robotic system. The framework consists of several visualization and interaction functionalities useful for a calibration procedure, such as the estimation of the initial pose of the sensors, the data collection and labeling, the data review and correction and the visualization of the estimation of the extrinsic and intrinsic parameters. This framework is supported by the Atomic Transformations Optimization Method formulation, referred to as ATOM. Results show that this framework is applicable to various robotic systems with different configurations, number of sensors and sensor modalities. In addition to this, a survey comparing the frameworks of different calibration approaches shows that ATOM provides a very good user experience.
机器人系统正在发展，包含大量的传感器和各种传感器模式。为了操作具有多个传感器的系统，必须准确估计这些传感器之间的几何变换。估计这些变换的过程称为传感器校准。每个传感器校准方法的背后都有一个公式和一个框架。公式是估计变换的方法。框架是执行校准过程所需的一组操作。本文提出了一种新颖的校准框架，为用户提供了更大的灵活性、控制和信息，增强了机器人系统校准的用户界面和用户体验。该框架包括一些对校准过程有用的可视化和交互功能，例如传感器初始姿态的估计、数据采集和标记、数据审查和修正以及外参数和内参数估计的可视化。该框架由原子变换优化方法公式（称为 ATOM）支持。结果表明，该框架适用于具有不同配置、传感器数量和传感器模式的各种机器人系统。除此之外，比较不同校准方法框架的调查显示，ATOM 提供了非常好的用户体验。
