In this letter a low-drift monocular SLAM method is proposed targeting indoor scenarios,
where monocular SLAM often fails due to the lack of textured surfaces. Our approach …

In this paper, a robust RGB-D SLAM system is proposed to utilize the structural information
in indoor scenes, allowing for accurate tracking and efficient dense map** on a CPU. Prior …

This work proposes a RGB-D SLAM system specifically designed for structured
environments and aimed at improved tracking and map** accuracy by relying on …

We present a low-drift visual odometry algorithm that separately estimates rotational and
translational motion from lines, planes, and points found in RGB-D images. Previous …

With the advancement of consumer-grade RGB-D cameras, obtaining depth information for
indoor 3D spaces has become increasingly accessible. This paper systematically reviews …

We present a novel end-to-end visual odometry architecture with guided feature selection
based on deep convolutional recurrent neural networks. Different from current monocular …

Objects and structures within man-made environments typically exhibit a high degree of
organization in the form of orthogonal and parallel planes. Traditional approaches utilize …

We introduce UprightNet, a learning-based approach for estimating 2DoF camera
orientation from a single RGB image of an indoor scene. Unlike recent methods that …

Detecting 3D objects from a single RGB image is intrinsically ambiguous, thus requiring
appropriate prior knowledge and intermediate representations as constraints to reduce the …

A wide range of man-made environments can be abstracted as the Atlanta world. It consists
of a set of Atlanta frames with a common vertical (gravitational) axis and multiple horizontal …