基于场景识别的移动机器人定位方法研究

Scene Recognition for Mobile Robot Localization

提出了一种基于场景识别的移动机器人定位方法.对CCD采集的工作环境的系列场景图像,用多通道Gabor滤波器提取场景图像的全局纹理特征,然后通过SVM分类器来识别场景图像,实现机器人的逻辑定位.在移动机器人CASIA I上对该算法进行了实验.实验结果表明,该定位方法可达到 91. 11%的定位准确率,对光照、对比度等因素有较强的鲁棒性,并且满足机器人实时定位的要求.

This paper proposes a scene recognition approach for mobile robot localization. The multi-channel Gabor filters are used to extract the global texture features of the scene images which are associated with the corresponding locations, and then these texture features are fed back to support the vector machine classifier to determine the logical location of the robot. The algorithm has been tested on the autonomous mobile robot CASIA-I designed and developed by us. The experiment results indicate that the algorithm can reach up to a correct localization rate of 91.11%, is robust to the various illumination and contrast, and satisfies the real-time localization demand of the mobile robot.