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 …

In this paper, we propose a survey of the Simultaneous Localization And Mapping (SLAM)
field when considering the recent evolution of autonomous driving. The growing interest …

Autonomous vehicles are expected to play a key role in the future of urban transportation
systems, as they offer potential for additional safety, increased productivity, greater …

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

This dissertation is concerned with three-dimensional (3D) sensing and 3D scan
representation. Three-dimensional records are important tools in several, quite diverse …

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

Topological maps model the environment as a graph, where nodes are distinctive places of
the environment and edges indicate topological relationships between them. They represent …

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 …

Current state of the art solutions of the SLAM problem are based on efficient sparse
optimization techniques and represent the problem as probabilistic constraint graphs. For …

Many robotic applications work with visual reference maps, which usually consist of sets of
more or less organized images. In these applications, there is a compromise between the …