Road and Intersection Detection Using Convolutional Neural Network

Ryuki Higuchi, Y. Fujimoto
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引用次数: 2

Abstract

It is an important task to detect the direction in which the autonomous robot can move. The robot can autonomously move to its destination following the information on how many times it detects the intersections and turns. In this paper, we use a convolutional neural network (CNN) to simultaneously recognize both the direction along the road and the intersection. The CNN detects the directions through the map image built using the scan data from a two-dimensional laser range finder (2D LRF). We show that the robot is able to make an autonomous movement along the road until it detects the intersection where it should turn.
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基于卷积神经网络的道路和交叉口检测
检测自主机器人的运动方向是一项重要的任务。机器人可以根据检测到路口和转弯的次数自动移动到目的地。在本文中,我们使用卷积神经网络(CNN)来同时识别道路方向和十字路口。CNN通过使用二维激光测距仪(2D LRF)的扫描数据构建的地图图像来检测方向。我们展示了机器人能够沿着道路自主移动,直到它检测到应该转弯的十字路口。
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