View-based localization in outdoor environments based on support vector learning
View-based localization in outdoor environments based on support vector learning
复制标题
基于支持向量学习的室外环境中基于视图的定位
DOI：
10.1109/iros.2005.1545445
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发表时间：
2005
期刊：
影响因子：
--
通讯作者：
Y. Shirai
中科院分区：
文献类型：
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作者：
Hideo Morita;M. Hild;J. Miura;Y. Shirai
本文描述了一种在户外环境中使用支持向量机的基于视图的定位方法。我们一直在开发一种两阶段的基于视觉的导航方法。在训练阶段，机器人获取图像序列沿着所需的路线，并自动学习的路线视觉。在随后的自主导航阶段中，机器人通过基于输入图像和学习的路线表示之间的比较定位自身来移动。我们以前的定位方法使用的对象识别方法，这是强大的天气和季节的变化，但它有许多参数和阈值手动调整。因此，本文应用支持向量机（SVM）算法，这个对象识别问题。支持向量机也适用于判别位置的基础上的识别结果。这种两阶段的基于SVM的定位方法表现出令人满意的性能为真实的室外图像数据，而无需任何手动调整的参数和阈值。
This paper describes a view-based localization method using support vector machines in outdoor environments. We have been developing a two-phase vision-based navigation method. In the training phase, the robot acquires image sequences along the desired route and automatically learns the route visually. In the subsequent autonomous navigation phase, the robot moves by localizing itself based on the comparison between input images and the learned route representation. Our previous localization method uses an object recognition method which is robust to changes of weather and the seasons; however it has many parameters and threshold values to be manually adjusted. This paper, therefore, applies a support vector machine (SVM) algorithm to this object recognition problem. SVM is also applied to discriminating locations based on the recognition results. This two-stage SVM-based localization approach exhibits a satisfactory performance for real outdoor image data without any manual adjustment of parameters and threshold values.
DOI：
10.1109/iros.2003.1249323
发表时间：
2003-10
期刊：
Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453)
影响因子：
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作者：
Hiroaki Katsura;J. Miura;M. Hild;Y. Shirai
通讯作者：
Hiroaki Katsura;J. Miura;M. Hild;Y. Shirai