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：2009
卷号：6
期号：4
DOI：10.5772/7253
出版社：SAGE Publications
摘要：Autonomous robots can initiate their mission plans only after gathering sufficient information about the environment. Therefore reliable perception information plays a major role in the overall success of an autonomous robot. The Hidden Markov Model based post-perception filtering module proposed in this paper aims to identify and remove spurious perception information in a given perception sequence using the generic meta-pose definition. This method allows representing uncertainty in more terms compared to the common physical representations. Our experiments with the four legged AIBO robot indicated that the proposed module improved perception and localization performance significantly.
