Gaussian random field-based log odds occupancy mapping

• Autor(a) principal: Li, Hongjun
• Data de Publicação: 2018
• Outros Autores: Barão, Miguel, Rato, Luís
• Tipo de documento: Artigo de conferência
• Idioma: por
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10174/24962; https://doi.org/10.1109/AQTR.2018.8402727
• topic: Gaussian Processes; Robot mapping

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 well-known 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.