[1]杨磊[],蔡纪源[],任衍允[],等. 一种基于深度信息的障碍物检测方法[J].计算机技术与发展,2015,25(08):43-47.
　YANG Lei[],CAI Ji-yuan[],REN Yan-yun[],et al. A Method of Obstacle Detection Based on Depth Information[J].,2015,25(08):43-47.
点击复制

 一种基于深度信息的障碍物检测方法()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年08期
页码:
43-47
栏目:
智能、算法、系统工程
出版日期:
2015-08-10

文章信息/Info

Title:
 A Method of Obstacle Detection Based on Depth Information
文章编号:
1673-629X（2015)08-0043-05
作者:
 杨磊[1] ; 蔡纪源[1];  任衍允[1];  李俄德[2]
 1.上海大学 机电工程与自动化学院 上海市电站自动化技术重点实验室；2.大唐陕西府谷能源化工有限责任公司
Author(s):
 YANG Lei[1] ; CAI Ji-yuan[1] ; REN Yan-yun[1];  LI E-de[2]
关键词:
 ; 深度数据; 视差图; 最小二乘; 最大类间方差法; 障碍物检测
Keywords:
 ; depth data; disparity map; least squares; Otsu; obstacle detection
分类号:
TP301
文献标志码:
A
摘要:
 为增强室内移动机器人障碍物检测和道路提取能力，文中提出了一种基于深度信息的障碍检测方法。首先对深度数据进行滤波处理，填补缺失的数据；然后将深度图转换为视差图，对视差图进行水平和竖直方向投影直方图统计获得U-V视差图；由V视差图得到初步道路信息，进一步用最小二乘法拟合出完整道路平面。对U-V视差图进行两次最大类间方差法( Otsu法)分割，提取出障碍物主要信息，并根据视差关系得到障碍物在世界坐标系中的位置。实验结果表明，使用Kinect可以有效地对地面障碍物进行检测并提取出道路信息，可为室内移动机器人提供良好的导航信息。
Abstract:
 To enhance obstacle detection and road extraction ability of the indoor mobile robot,present an obstacle detection method based on depth information. Firstly,process the depth data by filtering and fill up the missing data. Then transform depth map to disparity map and calculate U-V disparity map by horizontal and vertical direction projection histogram statistics. Based on preliminary road information got by V disparity map,can fit a complete road plane by using the least square method. With twice segmentation of the U-V disparity map by Otsu’ s method,extract the main information of obstacles and obtain the location of obstacle in the world coordinate according to disparity relationship. Experiments show that Kinect can effectively improve the ability of obstacle detection and road information extrac-tion for indoor mobile robot,and provide good navigation information.

相似文献/References:

[1]黄露丹,严利民.基于Kinect深度数据的人物检测[J].计算机技术与发展,2013,(04):119.
　HUANG Lu-dan,YAN Li-min.Human Detection Based on Depth Data Acquired by Kinect[J].,2013,(08):119.
[2]张志宏,吴庆波,邵立松,等.基于飞腾平台TOE协议栈的设计与实现[J].计算机技术与发展,2014,24(07):1.
　ZHANG Zhi-hong,WU Qing-bo,SHAO Li-song,et al. Design and Implementation of TCP/IP Offload Engine Protocol Stack Based on FT Platform[J].,2014,24(08):1.
[3]梁文快,李毅. 改进的基因表达算法对航班优化排序问题研究[J].计算机技术与发展,2014,24(07):5.
　LIANG Wen-kuai,LI Yi. Research on Optimization of Flight Scheduling Problem Based on Improved Gene Expression Algorithm[J].,2014,24(08):5.
[4]黄静,王枫,谢志新,等. EAST文档管理系统的设计与实现[J].计算机技术与发展,2014,24(07):13.
　HUANG Jing,WANG Feng,XIE Zhi-xin,et al. Design and Implementation of EAST Document Management System[J].,2014,24(08):13.
[5]侯善江[],张代远[][][]. 基于样条权函数神经网络P2P流量识别方法[J].计算机技术与发展,2014,24(07):21.
　HOU Shan-jiang[],ZHANG Dai-yuan[][][]. P2P Traffic Identification Based on Spline Weight Function Neural Network[J].,2014,24(08):21.
[6]李璨,耿国华,李康,等. 一种基于三维模型的文物碎片线图生成方法[J].计算机技术与发展,2014,24(07):25.
　LI Can,GENG Guo-hua,LI Kang,et al. A Method of Obtaining Cultural Debris’ s Line Chart Based on Three-dimensional Model[J].,2014,24(08):25.
[7]翁鹤,皮德常. 混沌RBF神经网络异常检测算法[J].计算机技术与发展,2014,24(07):29.
　WENG He,PI De-chang. Chaotic RBF Neural Network Anomaly Detection Algorithm[J].,2014,24(08):29.
[8]刘茜[],荆晓远[],李文倩[],等. 基于流形学习的正交稀疏保留投影[J].计算机技术与发展,2014,24(07):34.
　LIU Qian[],JING Xiao-yuan[,LI Wen-qian[],et al. Orthogonal Sparsity Preserving Projections Based on Manifold Learning[J].,2014,24(08):34.
[9]尚福华,李想,巩淼. 基于模糊框架-产生式知识表示及推理研究[J].计算机技术与发展,2014,24(07):38.
　SHANG Fu-hua,LI Xiang,GONG Miao. Research on Knowledge Representation and Inference Based on Fuzzy Framework-production[J].,2014,24(08):38.
[10]叶偲,李良福,肖樟树. 一种去除运动目标重影的图像镶嵌方法研究[J].计算机技术与发展,2014,24(07):43.
　YE Si,LI Liang-fu,XIAO Zhang-shu. Research of an Image Mosaic Method for Removing Ghost of Moving Targets[J].,2014,24(08):43.
[11]武乾坤,胡冰,兰浩. 基于Android+Kinect的失能老人自助系统设计与实现[J].计算机技术与发展,2017,27(04):135.
　WU Qian-kun,HU Bing,LAN Hao. Design and Realization of an Android+Kinect-based Self-help System for Disabled Seniors[J].,2017,27(08):135.

更新日期/Last Update: 2015-09-11