摘要
近年来,室外自主移动机器人在野外环境下的有着十分重要的应用,比如在野外救援和月球探测等方面。而室外复杂环境下的地形识别研究是面向移动机器人环境感知和识别的一个重要挑战。针对在室外复杂环境下的光照干扰和遮挡等因素,论文提出了一种基于金字塔化SIFT特征(SIFT Spatial Pyramid Matching,SSPM)与最小二乘相关支持向量机(Least Squares Twin Support Vector Machines,LSTSVM)相结合的地形识别方法。相较于传统的词袋式特征表示,加入了局部和空间信息特征,增强了特征对图像的表现能力,进一步提高了识别率,大大减小了训练时间。再利LSTSVM在组合得到的新特征集上学习,最后在得到的分类器上验证算法的可靠性。
In recent years,autonomous mobile outdoor robots has a very important application in the field environment,such as rescuing and lunar exploration,etc. Terrain recognition research in complex outdoor environment is a big challenge in the field of mobile robot's environment awareness and identification. Considering the factors in the complex outdoor environment,such as light. ing disturbance and block,this paper puts forward a kind of terrain recognition method based on Pyramid SIFT features(SIFT Spa. tial Pyramid Matching,SSPM)associated with the Least Squares Support Vector machine(Least Squares Twin Support Vector, LSTSVM). Compared with the traditional word bag features,the method joins in the space information and local features,which en. hances the characteristics of image expression,improves the accuracy and reduces training time. In the end,the LSTSVM is used to study in the new features and analyze the algorithm reliability on the classifier.
作者
薛琮琳
郭剑辉
马玲玲
XUE Conglin;GUO Jianhui;MA Lingling(School of Computer Science and Engineering,Nanjing University of Science and Engineering,Nanjing 210094)
出处
《计算机与数字工程》
2019年第5期1217-1222,共6页
Computer & Digital Engineering
基金
国家自然科学基金项目(编号:61603190)资助
