SLAM is an abbreviation for simultaneous localization and mapping, which is a technique for
estimating sensor motion and reconstructing structure in an unknown environment …

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 …

Humans are able to form a complex mental model of the environment they move in. This
mental model captures geometric and semantic aspects of the scene, describes the …

Where am I? This is one of the most critical questions that any intelligent system should
answer to decide whether it navigates to a previously visited area. This problem has long …

Real-time, high-quality, 3D scanning of large-scale scenes is key to mixed reality and robotic
applications. However, scalability brings challenges of drift in pose estimation, introducing …

We present the first dense SLAM system capable of reconstructing non-rigidly deforming
scenes in real-time, by fusing together RGBD scans captured from commodity sensors. Our …

We present an approach to indoor scene reconstruction from RGB-D video. The key idea is
to combine geometric registration of scene fragments with robust global optimization based …

We present a new simultaneous localization and mapping (SLAM) system capable of
producing high-quality globally consistent surface reconstructions over hundreds of meters …

In this paper we introduce Co-Fusion, a dense SLAM system that takes a live stream of RGB-
D images as input and segments the scene into different objects (using either motion or …

Accurately localizing in and mapping an environment are essential building blocks of most
autonomous systems. In this paper, we present a novel approach for LiDAR odometry and …