We review the use of factor graphs for the modeling and solving of large-scale inference
problems in robotics. Factor graphs are a family of probabilistic graphical models, other …

For robots to navigate and interact more richly with the world around them, they will likely
require a deeper understanding of the world in which they operate. In robotics and related …

Solving the SLAM (simultaneous localization and mapping) problem is one way to enable a
robot to explore, map, and navigate in a previously unknown environment. Smoothing …

This article provides a comprehensive introduction into the simultaneous localization and
mapping problem, better known in its abbreviated form as SLAM. SLAM addresses the …

Rao–Blackwellized particle filters have become a popular tool to solve the simultaneous
localization and mapping problem. This technique applies a particle filter in which each …

The lack of publicly accessible datasets with a reliable ground truth has prevented in the
past a fair and coherent comparison of different methods proposed in the mobile robot …

The maps that are built by standard feature-based simultaneous localization and mapping
(SLAM) methods cannot be directly used to compute paths for navigation, unless enriched …

Large indoor cluttered environment representation is still a challenging task when non-
uniform triangle cell-based or quadrangle cell-based decomposition is used to build the …

Mapping the surroundings is a challenging task required for mobile robot. Fusion of
heterogeneous sensors measurement has gradually been the main focus of robot …

Simultaneous Localization and Mapping (SLAM) has been a long-standing research
problem in robotics. It describes the problem of a robot mapping an unknown environment …