Unknown correlations (UCs) generally exist in a wide spectrum of practical multi-source
information fusion problems, and thereby, their corresponding fusion problems have …

Bernoulli filters are a class of exact Bayesian filters for non-linear/non-Gaussian recursive
estimation of dynamic systems, recently emerged from the random set theoretical framework …

For many applications in Urban Search and Rescue (USAR) scenarios robots need to learn
a map of unknown environments. We present a system for fast online learning of occupancy …

The employment of unmanned aerial vehicles (UAVs) has greatly facilitated the lives of
humans. Due to the mass manufacturing of consumer unmanned aerial vehicles and the …

We are concerned with improving the range resolution of stereo vision for entry, descent,
and landing (EDL) missions to Mars and other planetary bodies. The goal is to create …

For multisensor system with unknown cross-covariances among local estimation errors, the
batch covariance intersection (BCI) fusion estimation algorithm requires the optimization of a …

This paper presents an consistent decentralized data fusion approach for robust multi-robot
SLAM in dangerous, unknown environments. The DDF-SAM 2.0 approach extends our …

Pose SLAM is the variant of simultaneous localization and map building (SLAM) is the
variant of SLAM, in which only the robot trajectory is estimated and where landmarks are …

Multisensor fusion and consensus filtering are two fascinating subjects in the research of
sensor networks. In this survey, we will cover both classic results and recent advances …

The paradigm of multisensor data fusion has been evolved from a centralized architecture to
a decentralized or distributed architecture along with the advancement in sensor and …