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This paper presents a framework for information sharing and fusion in cooperative tasks involving humans and robots. In this context, all information regarding the state of interest is recursively fused and maintained by each agent in a form of belief. For a robot agent, its belief is commonly and practically represented as a probability density function (pdf), formed by traditional sensor fusion...
A terrain mapping model is proposed using a generalized Markov random field (MRF) representation. Unlike previous work, the proposed MRF can fully represent uncertainties due to sensor pose and measurement errors, as well as data association errors in a single model. Additionally, neither homoscedasticity nor a predefined shape of the likelihood distribution is assumed. The flexibility of an MRF model...
This paper proposes a Markov Random Field (MRF) representation for sensor and terrain information fusion in a 2.5D map. Unlike in the previous works, the proposed MRF formally models the sensor pose and measurement uncertainties, thus allowing the measurements to be appropriately fused with terrain information. Additionally, the MRF's graphical modelbased representation allows for an easy modification...
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