Invariant signatures for omnidirectional visual place recognition and robot localization in unknown environments

被引：0
|
作者：
Marie, Romain [1 ]
Labbani-Igbida, Ouiddad [1 ]
Mouaddib, El Mustapha [1 ]
机构：
[1] Univ Picardie Jules Verne, Modelisat Informat & Syst Lab, Amiens, France
来源：
2012 21ST INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR 2012) | 2012年
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper introduces a novel approach to place representation for robot localization and mapping. It uses classical invariance theory while proposing an adaptive kernel to omnidirectional images and exploiting only the main significant visual information in the images. The approach is validated in real world robot exploration and localization and compared to color histograms.
引用
收藏
页码：2537 / 2540
页数：4
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