Combining Features for Adaptive Terrain Classification Based on ART Neural Network

摘要：

Terrain classification focuses on determining a safe region for robot to traverse while labeling obstacles. This paper study terrain classification based on scene imageries of the natural environment and attempts to combine color, texture and geometry moment features to train an ARTMAP neural network. After learning the relationship between the combined features and the traversability of terrains, the neural network can be used to assess the front terrain. In this paper, we used the combined features to carve up the environment and enable the classification more lighting independent, season adaptable and more efficient in the whole. Thanks to the adaptability of the ARTMAP neural network classifier and the strategy of features combination, the results show that the presented method can classify the terrain more accurately across different scenarios with fairly high classification efficiency.

关键词： terrain classification combined features neural network classifier

作者: XU Zhangjian SONG Meng LI Shulun SUN Fengchi

作者单位: College of Software, Nankai University, Tianjin 300071, China

会议类型: 国际会议

会议名称: The 31st Chinese Control Conference(第三十一届中国控制会议)

会议地点: 合肥

会议语种:英文

页码: 4808-4813

在线出版日期: 2012-07-01（万方平台首次上网日期，不代表论文的发表时间）