基于投影学习的移动机器人导航地标识别

R. Luo, H. Potlapalli
{"title":"基于投影学习的移动机器人导航地标识别","authors":"R. Luo, H. Potlapalli","doi":"10.1109/ICNN.1994.374649","DOIUrl":null,"url":null,"abstract":"Mobile robots rely on traffic signs for navigation in outdoor environments. The recognition of these signs using vision is a unique problem. The important aspects of this problem are that the object parameters such as scale and orientation are constantly changing with the motion of the camera. Also, new signs may appear at some time. In this case feature extraction algorithms are unable to meet the constraints of flexibility. Neural networks can be easily programmed for this task. A new learning strategy for self-organizing neural networks is presented. By iteratively subtracting the projection of the winning neuron onto the null space of the input vector, the neuron is progressively made more representative of the input. The convergence properties of the new neural network model are studied. Comparison results with standard Kohonen learning are presented. The performance of the network with respect to training and recognition of traffic signs is studied.<<ETX>>","PeriodicalId":209128,"journal":{"name":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1994-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"17","resultStr":"{\"title\":\"Landmark recognition using projection learning for mobile robot navigation\",\"authors\":\"R. Luo, H. Potlapalli\",\"doi\":\"10.1109/ICNN.1994.374649\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Mobile robots rely on traffic signs for navigation in outdoor environments. The recognition of these signs using vision is a unique problem. The important aspects of this problem are that the object parameters such as scale and orientation are constantly changing with the motion of the camera. Also, new signs may appear at some time. In this case feature extraction algorithms are unable to meet the constraints of flexibility. Neural networks can be easily programmed for this task. A new learning strategy for self-organizing neural networks is presented. By iteratively subtracting the projection of the winning neuron onto the null space of the input vector, the neuron is progressively made more representative of the input. The convergence properties of the new neural network model are studied. Comparison results with standard Kohonen learning are presented. The performance of the network with respect to training and recognition of traffic signs is studied.<<ETX>>\",\"PeriodicalId\":209128,\"journal\":{\"name\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"volume\":\"38 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1994-06-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"17\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICNN.1994.374649\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 1994 IEEE International Conference on Neural Networks (ICNN'94)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICNN.1994.374649","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 17

摘要

移动机器人依靠交通标志在室外环境中导航。用视觉识别这些标志是一个独特的问题。这个问题的重要方面是,物体的参数,如规模和方向是不断变化的相机的运动。此外,新的迹象可能会在某个时候出现。在这种情况下,特征提取算法无法满足灵活性的约束。神经网络可以很容易地编程来完成这项任务。提出了一种新的自组织神经网络学习策略。通过迭代地减去获胜神经元在输入向量零空间上的投影,神经元逐渐变得更能代表输入。研究了该神经网络模型的收敛性。给出了与标准Kohonen学习的比较结果。研究了该网络在交通标志的训练和识别方面的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Landmark recognition using projection learning for mobile robot navigation
Mobile robots rely on traffic signs for navigation in outdoor environments. The recognition of these signs using vision is a unique problem. The important aspects of this problem are that the object parameters such as scale and orientation are constantly changing with the motion of the camera. Also, new signs may appear at some time. In this case feature extraction algorithms are unable to meet the constraints of flexibility. Neural networks can be easily programmed for this task. A new learning strategy for self-organizing neural networks is presented. By iteratively subtracting the projection of the winning neuron onto the null space of the input vector, the neuron is progressively made more representative of the input. The convergence properties of the new neural network model are studied. Comparison results with standard Kohonen learning are presented. The performance of the network with respect to training and recognition of traffic signs is studied.<>
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信