Gaussian random field-based log odds occupancy mapping

Abstract

This paper focuses on mapping problem with known robot pose in static environments and proposes a Gaussian random field-based log odds occupancy mapping (GRF-LOOM). In this method, occupancy probability is regarded as an unknown parameter and the dependence between parameters are considered. Given measurements and the dependence, the parameters of not only observed space but also unobserved space can be predicted. The occupancy probabilities in log odds form are regarded as a GRF. This mapping task can be solved by the wellknown prediction equation in Gaussian processes, which involves an inverse problem. Instead of the prediction equation, a new recursive algorithm is also proposed to avoid the inverse problem. Finally, the proposed method is evaluated in simulations. Index Terms-Binary Bayes filter, Gaussian random field, Log odds occupancy mapping.