Distributed data fusion (DDF) is the process whereby a group of agents sense their local
environment, communicate with other agents, and collectively try to infer knowledge about a …

The paper introduces an algorithm for estimation of dynamic mixture models. A new feature
of the proposed algorithm is the ability to consider a dynamic form not only for component …

This paper presents a method for Bayesian multi-robot peer-to-peer data fusion where any
pair of autonomous robots hold non-identical, but overlapping parts of a global joint …

This paper proposes a Markov Random Field (MRF) representation for sensor and terrain
information fusion in a 2.5 D map. Unlike in the previous works, the proposed MRF formally …

In Bayesian peer-to-peer decentralized data fusion, the underlying distributions held locally
by autonomous agents are frequently assumed to be over the same set of variables …

In many robotics problems, there is a significant gain in collaborative information sharing
between multiple robots, for exploration, search and rescue, tracking multiple targets, or …

A method for generating a three dimensional grid of terrain includes receiving data
representing a three-dimensional point cloud and generating a plurality of slices of the data …

A terrain mapping model is proposed using a generalized Markov random field (MRF)
representation. Unlike previous work, the proposed MRF can fully represent uncertainties …

Recent research has shown that robots can model their world with Multi-Level (ML) maps,
which utilize patches in a two-dimensional grid space to represent various environment …

This work examines the problem of using finite Gaussian mixtures (GMs) in Bayesian
message passing algorithms for decentralized data fusion (DDF). It is shown that both exact …