Indoor Wheeled Robot Positioning Algorithm Based on Extended Kalman Filter

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS) Pub Date : 2019-10-01 DOI:10.1109/IUCC/DSCI/SmartCNS.2019.00115

Xiangbin Shi, Jingyuan Tan, Deyuan Zhang

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引用次数: 1

Abstract

The indoor wheeled robot is widely used in research, industrial manufacturing, and service industries. For the positioning process of indoor wheeled mobile robots, the data from a single sensor is not reliable and accurate. The traditional solution to this problem is to use the extended Kalman filter (EKF) method, which suffers from linearization error and accumulation error. To tackle these problems, we propose Linear transformation error elimination extended Kalman filter(TEKF) to fuse multiple sensors. Firstly, the data of the sensors of the odometer, Inertial measurement unit(IMU) and lidar are collected and preprocessed, and a complementary filtering method is proposed to obtain the angular velocity. Secondly, the second-order Taylor series expansion is performed on the state and the observation equation, which overcomes the linearization error and improves the accuracy of data fusion. Finally, the backtracking processing method is adopted to eliminate the accumulated error and enhance the environmental adaptability. The experimental results of the real indoor wheeled robot shows that TEKF can effectively improve the accuracy of data fusion and ensure that the indoor wheeled robot can be more accurately positioned.

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基于扩展卡尔曼滤波的室内轮式机器人定位算法

室内轮式机器人广泛应用于科研、工业制造和服务业。对于室内轮式移动机器人的定位过程，单个传感器的数据不可靠、不准确。传统的解决方法是使用扩展卡尔曼滤波(EKF)方法，该方法存在线性化误差和累积误差。为了解决这些问题，我们提出了线性变换误差消除扩展卡尔曼滤波器(TEKF)来融合多个传感器。首先，对里程计、惯性测量单元(IMU)和激光雷达传感器的数据进行采集和预处理，提出一种互补滤波方法来获取角速度;其次，对状态方程和观测方程进行二阶泰勒级数展开，克服了线性化误差，提高了数据融合的精度;最后，采用回溯处理方法消除了累积误差，增强了环境适应性。真实室内轮式机器人的实验结果表明，TEKF可以有效提高数据融合的精度，保证室内轮式机器人能够更准确地定位。

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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来源期刊

2019 IEEE International Conferences on Ubiquitous Computing & Communications (IUCC) and Data Science and Computational Intelligence (DSCI) and Smart Computing, Networking and Services (SmartCNS)

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