A Discriminative Algorithm for Indoor Place Recognition Based on Clustering of Features and Images  

作　　者：Ke Wang Xue-Xiong Long Rui-Feng Li Li-Jun Zhao 

机构地区：[1]State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China

出　　处：《International Journal of Automation and computing》2017年第4期407-419,共13页国际自动化与计算杂志（英文版）

基　　金：supported by National Natural Science Foundation of China(Nos.61305103 and 61473103);Natural Science Foundation Heilongjiang province(No.QC2014C072);Postdoctoral Science Foundation of Heilongjiang(No.LBH-Z14108);SelfPlanned Task of State Key Laboratory of Robotics and System(HIT)(No.SKLRS201609B)

摘　　要：In order to solve the problem of indoor place recognition for indoor service robot, a novel algorithm, clustering of features and images （CFI）, is proposed in this work. Different from traditional indoor place recognition methods which are based on kernels or bag of features, with large margin classifier, CFI proposed in this work is based on feature matching, image similarity and clustering of features and images. It establishes independent local feature clusters by feature cloud registration to represent each room, and defines image distance to describe the similarity between images or feature clusters, which determines the label of query images. Besides, it improves recognition speed by image scaling, with state inertia and hidden Markov model constraining the transition of the state to kill unreasonable wrong recognitions and achieves remarkable precision and speed. A series of experiments are conducted to test the algorithm based on standard databases, and it achieves recognition rate up to 97% and speed is over 30 fps, which is much superior to traditional methods. Its impressive precision and speed demonstrate the great discriminative power in the face of complicated environment.In order to solve the problem of indoor place recognition for indoor service robot, a novel algorithm, clustering of features and images （CFI）, is proposed in this work. Different from traditional indoor place recognition methods which are based on kernels or bag of features, with large margin classifier, CFI proposed in this work is based on feature matching, image similarity and clustering of features and images. It establishes independent local feature clusters by feature cloud registration to represent each room, and defines image distance to describe the similarity between images or feature clusters, which determines the label of query images. Besides, it improves recognition speed by image scaling, with state inertia and hidden Markov model constraining the transition of the state to kill unreasonable wrong recognitions and achieves remarkable precision and speed. A series of experiments are conducted to test the algorithm based on standard databases, and it achieves recognition rate up to 97% and speed is over 30 fps, which is much superior to traditional methods. Its impressive precision and speed demonstrate the great discriminative power in the face of complicated environment.

关 键 词：Indoor place recognition locally and globally independent clustering of features and images （CFI） state inertia hidden Markov model. 

分 类 号：TP242[自动化与计算机技术—检测技术与自动化装置] TP391.41[自动化与计算机技术—控制科学与工程]