Localization of a Mobile Autonomous Robot Using Extended Kalman Filter

2013 Third International Conference on Advances in Computing and Communications Pub Date : 2013-08-29 DOI:10.1109/ICACC.2013.59

V. Sangale, Abhishek Shendre

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引用次数: 3

Abstract

This paper demonstrates an effective method for combining measurements from a gyroscope and rotary wheel encoders (odometry) in mobile robot localization. Sensor fusion of this kind is done using an Extended Kalman filter obtained from the values of above sensors for a mobile autonomous robot. Many such methods implement a statistical model that describes the behaviour of the gyroscope and the odometry component. However, because these systems are based on models, they cannot anticipate the unpredictable and potentially "catastrophic" effects of irregularities and frictional changes occasionally encountered on the floor. We present experimental evidence that non-systematic odometry error sources impact the robot's motion. Therefore a new approach has been developed based on a study of the physical interaction between ground and the robot. This approach has been implemented by developing an embedded system with ARM 7 based LPC2148 micro-controller. Experimental results show that the proposed method effectively reduces the localization error while yielding feasible parameter estimation.

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基于扩展卡尔曼滤波的移动自主机器人定位

本文提出了一种将陀螺仪测量与转轮编码器测量相结合的移动机器人定位方法。这种类型的传感器融合是利用由上述传感器的值得到的扩展卡尔曼滤波器来实现的。许多这样的方法实现了一个统计模型，该模型描述了陀螺仪和里程计组件的行为。然而，由于这些系统是基于模型的，因此它们无法预测地板上偶尔遇到的不规则和摩擦变化的不可预测和潜在的“灾难性”影响。我们提出的实验证据表明，非系统里程误差源影响机器人的运动。因此，在研究地面与机器人物理相互作用的基础上，开发了一种新的方法。通过开发一个基于ARM 7的LPC2148微控制器的嵌入式系统，实现了该方法。实验结果表明，该方法在给出可行参数估计的同时，有效地减小了定位误差。

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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2013 Third International Conference on Advances in Computing and Communications

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