EKF-based visual self-calibration tool for robots with rotating directional cameras

J. Ribeiro, Rui Serra, N. Nunes, Hugo Silva, J. Almeida
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引用次数: 1

Abstract

Autonomous mobile robots perception systems are complex multi-sensors systems. Information from different sensors, placed in different parts of the platforms, need to be related and fused into some representation of the world or robot state. For that, the knowledge of the relative pose (position and rotation) between sensors frames and the platform frame plays a critical role. The process to determine those is called extrinsic calibration. This paper addresses the development of automatic robot calibration tool for Middle Size League Robots with rotating directional cameras, such as the ISePorto team robots. The proposed solution consists on a robot navigating in a path, while acquiring visual information provided by a known target positioned in a global reference frame. This information is then combined with wheel odometry sensors, robot rotative axis encoders and gyro information within an Extend Kalman filter framework, that estimates all parameters required for the sensors angles and position determination related to the robot body frame. We evaluated our solution, by performing several trials and obtaining similar results to the previous used manual calibration procedure, but with a much less time consuming performance and also without being susceptible to human error.
基于ekf的旋转定向摄像机机器人视觉自标定工具
自主移动机器人感知系统是一个复杂的多传感器系统。来自不同传感器的信息,放置在平台的不同部分,需要相互关联并融合成世界或机器人状态的某种表示。为此,传感器框架与平台框架之间的相对姿态(位置和旋转)的知识起着至关重要的作用。确定这些的过程称为外部校准。本文研究了中型联盟机器人(如ISePorto团队机器人)旋转定向摄像头的自动机器人标定工具的开发。提出的解决方案包括机器人在路径上导航,同时获取由定位在全局参考框架中的已知目标提供的视觉信息。然后将这些信息与车轮里程计传感器、机器人旋转轴编码器和扩展卡尔曼滤波框架内的陀螺仪信息相结合,估计与机器人身体框架相关的传感器角度和位置确定所需的所有参数。我们评估了我们的解决方案,通过执行几次试验,获得了与之前使用的手动校准过程相似的结果,但消耗的时间要少得多,而且也不会受到人为错误的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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