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Sensor fusion-based line detection for unmanned navigation

机译:无人导航的传感器融合线路检测

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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.
机译:我们提出了一种使用传感器融合的无人驾驶机器人无人导航线路的可靠检测算法。为了检测机器人和线之间的距离和角度,我们使用视觉传感器系统和激光测距仪(LRF)。每个传感器系统运行其自己的扩展卡尔曼滤波器(EKF)以估计线路的距离和方向。视觉系统处理使用众所周知的边缘检测算法捕获的图像,并且LRF使用反射的激光束强度的测量来检测线路。然而,根据道路和环境光的条件,每个传感器都给我们提供错误的线路,或者有时完全无法检测到它。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的输出的加权和,它可以比每种测量/估计系统更可靠地估计线的距离和方向。

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