期刊名称：International Journal of Advanced Robotic Systems
印刷版ISSN：1729-8806
电子版ISSN：1729-8814
出版年度：2005
卷号：2
期号：4
DOI：10.5772/5770
出版社：SAGE Publications
摘要：Mobile robot applications that involve exploration and inspection of dynamic environments benefit, and often even are dependant on reliable novelty detection algorithms. In this paper we compare and discuss the performance and functionality of two different on-line novelty detection algorithms, one based on incremental Principal Component Analysis and the other on a Grow-When-Required artificial neural network. A series of experiments using visual input obtained by a mobile robot interacting with laboratory and real-world environments demonstrate and measure advantages and disadvantages of each approach.
关键词：real-time computer vision ; on-line novelty detection ; mobile robotics
