Development of Robot Navigation Method Based on Single Camera Vision Using Deep Learning

被引：0

作者：

Khalilullah, K. M. Ibrahim ^{[1
]}

Ota, Shunsuke ^{[2
]}

Yasuda, Toshiyuki ^{[2
]}

Jindai, Mitsuru ^{[2
]}

机构：

[1] Univ Toyama, Grad Sch Sci & Engn Educ, Toyama, Japan

[2] Univ Toyama, Grad Sch Sci & Engn, Toyama, Japan

来源：

2017 56TH ANNUAL CONFERENCE OF THE SOCIETY OF INSTRUMENT AND CONTROL ENGINEERS OF JAPAN (SICE) | 2017年

关键词：

Deep Belief Neural Network; illuminant-invariant; image mask; discriminant features; image patch; ALGORITHMS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents a complete vision guided real-time approach to robot navigation in urban narrow roads based on drivable road area detection using deep learning. In this approach, an illuminant-invariant road database is created from captured images. This database is used to train the Deep Belief Neural Network (DBNN) for road detection. During navigation, a camera takes a snapshot of the road and then the captured image is converted into an illuminantinvariant image. After that, DBNN takes this image as an input. It extracts the road features layer-by-layer for detection. The experimental wheelchair robot follows detected road boundary for navigation. The performance of the developed algorithm is demonstrated by the experiments. In addition, we encompass the large areas of autonomous robot navigation in a single camera.

引用

页码：939 / 942

页数：4

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