arXiv 2001.015552020-01-06cs.RO

一种通用的轮式移动机器人自主标定框架

A Generalized Framework for Autonomous Calibration of Wheeled Mobile Robots

Indian Institute of Technology Kanpur（印度理工学院坎普尔分校）

Mohan Krishna Nutalapati, Lavish Arora, Anway Bose, Ketan Rajawat, Rajesh M Hegde

备注：: This manuscript has been submitted to 'Elsevier Journal of Robotics and Autonomous Systems' and is under review for possible publication. Based on IROS 2019 conference submission [arXiv:1910.11917]

摘要：

机器人校准允许融合来自多个传感器（例如里程计、相机等）的数据，通过在相应的参考帧之间提供适当的变换关系。对于配备有外感受传感器的轮式机器人，校准需要根据里程计数据学习传感器或机器人的运动模型，并且通常必须在执行诸如同时定位与地图构建（SLAM）的任务之前执行。在这种情况下，目前的趋势是进行里程计和传感器的同时校准，而不使用任何额外的硬件。在现有的同步校准算法的基础上，我们提出了一个通用的校准框架，不仅可以处理机器人在2D任意或未知的运动模型，但也处理离群值在自动化的方式。首先，我们提出了一种算法的交替最小化框架适用于两轮差动驱动的基础上。随后，对于任意但已知的驱动器配置，我们提出了一种利用智能加权方案的迭代重加权最小二乘方法。与现有的工作不同，这些算法不需要人工干预，无缝地处理由于系统和非系统误差而产生的离群值。最后，我们提出了一种新的基于高斯过程的非参数方法来标定轮式机器人与任意或未知的驱动配置。详细的实验证明所提出的算法的准确性，实用性和灵活性。

英文摘要：

Robotic calibration allows for the fusion of data from multiple sensors such as odometers, cameras, etc., by providing appropriate transformational relationships between the corresponding reference frames. For wheeled robots equipped with exteroceptive sensors, calibration entails learning the motion model of the sensor or the robot in terms of the odometric data, and must generally be performed prior to performing tasks such as simultaneous localization and mapping (SLAM). Within this context, the current trend is to carry out simultaneous calibration of odometry and sensor without the use of any additional hardware. Building upon the existing simultaneous calibration algorithms, we put forth a generalized calibration framework that can not only handle robots operating in 2D with arbitrary or unknown motion models but also handle outliers in an automated manner. We first propose an algorithm based on the alternating minimization framework applicable to two-wheel differential drive. Subsequently, for arbitrary but known drive configurations we put forth an iteratively re-weighted least squares methodology leveraging an intelligent weighing scheme. Different from the existing works, these proposed algorithms require no manual intervention and seamlessly handle outliers that arise due to both systematic and non-systematic errors. Finally, we put forward a novel Gaussian Process-based non-parametric approach for calibrating wheeled robots with arbitrary or unknown drive configurations. Detailed experiments are performed to demonstrate the accuracy, usefulness, and flexibility of the proposed algorithms.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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英文摘要：

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