基于传感器融合的无人导航线路检测

C. Chun, SeungBeum Suh, Chi-won Roh, Yeonsik Kang, Sungchul Kang, Jung-yup Lee, Chang-Soo Han
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引用次数: 6

摘要

提出了一种基于传感器融合的移动机器人无人导航线路可靠检测算法。为了检测机器人与线之间的距离和角度,我们使用了视觉传感器系统和激光测距仪(LRF)。每个传感器系统运行自己的扩展卡尔曼滤波(EKF)来估计线的距离和方向。视觉系统使用众所周知的边缘检测算法处理被捕获的图像,LRF使用测量反射激光束的强度来检测线。然而,根据道路和环境光线的情况,每个传感器会给我们错误的线路测量,有时甚至完全无法检测到它。为了解决这种不确定性,我们开发了一种简单且易于实现的传感器融合算法,该算法使用每个EKF输出的加权和,它可以比每个测量/估计系统更可靠地估计线的距离和方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Sensor fusion-based line detection for unmanned navigation
We propose an algorithm of reliable detection of line for unmanned navigation of mobile robots using sensor fusion. To detect the distance and the angle between the robot and the line, we use a vision sensor system and a laser range finder (LRF). Each sensor system runs its own extended Kalman filter (EKF) to estimate the distance and orientation of the line. The vision system processes images being captured using well-known edge detection algorithms, and the LRF detects the line using the measurement of the intensity of the laser beam reflected. However, depending on the condition of the road and ambient light, each sensor gives us wrong measurement of the line or sometimes completely fails to detect it. To resolve such uncertainty, we develop a simple and easy-to-implement sensor fusion algorithm that uses weighted sum of the output of each EKF, and it gives us more reliable estimate of the distance and orientation of the line than each measurement/estimator system.
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