Ground-truth localization using a sequential-update extended Kalman filter

IEEE International Conference on Electro Information Technology : [proceedings]. IEEE International Conference on Electro Information Technology Pub Date : 2014-06-05 DOI:10.1109/EIT.2014.6871747

Eyad Zeino, M. Paulik, N. M. Krishnan, C. Luo, J. Overholt, G. Hudas, Thomas Udvare

引用次数: 1

Abstract

A ground-truth environment for mobile robot localization is developed. The sequential update extended Kalman filter is employed to fuse data from a Sick Nav200 laser positioning system and multiple onboard sensors to provide highly accurate robot pose estimation. Results are suitable for validation of mapping, localization and kino-dynamic modeling. Experimental work presented covers odometry calibration and system validation.

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基于序列更新扩展卡尔曼滤波的真地定位

开发了一种用于移动机器人定位的真实环境。序列更新扩展卡尔曼滤波器用于融合来自Sick Nav200激光定位系统和多个机载传感器的数据，以提供高精度的机器人姿态估计。结果适用于制图、定位和kino-dynamic建模的验证。实验工作包括里程计校准和系统验证。

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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