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 …

Maps are a key component in image-based camera localization and visual SLAM systems:
they are used to establish geometric constraints between images, correct drift in relative …

We present a novel data structure, the Bayes tree, that provides an algorithmic foundation
enabling a better understanding of existing graphical model inference algorithms and their …

Many popular problems in robotics and computer vision including various types of
simultaneous localization and map** (SLAM) or bundle adjustment (BA) can be phrased …

Recently, Rao-Blackwellized particle filters (RBPF) have been introduced as an effective
means to solve the simultaneous localization and map** problem. This approach uses a …

In this paper, we present incremental smoothing and map** (iSAM), which is a novel
approach to the simultaneous localization and map** problem that is based on fast …

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

Pose graphs have become a popular representation for solving the simultaneous
localization and map** (SLAM) problem. A pose graph is a set of robot poses connected …

This article presents GraphSLAM, a unifying algorithm for the offline SLAM problem.
GraphSLAM is closely related to a recent sequence of research papers on applying …

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