Simultaneous localization and mapping (SLAM) is a widely researched topic in the field of
robotics, augmented/virtual reality and more dominantly in self-driving cars. SLAM is a …

This paper presents ORB-SLAM, a feature-based monocular simultaneous localization and
mapping (SLAM) system that operates in real time, in small and large indoor and outdoor …

This paper studies visual odometry (VO) from the perspective of deep learning. After
tremendous efforts in the robotics and computer vision communities over the past few …

Visual ego-motion estimation, or briefly visual odometry (VO), is one of the key building
blocks of modern SLAM systems. In the last decade, impressive results have been …

Reliable feature correspondence between frames is a critical step in visual odometry (VO)
and visual simultaneous localization and mapping (V-SLAM) algorithms. In comparison with …

Scale drift is a crucial challenge for monocular autonomous driving to emulate the
performance of stereo. This paper presents a real-time monocular SFM system that corrects …

Image feature-based ego-motion estimation has been dominating the development of visual
odometry (VO) visual simultaneously localisation and mapping (V-SLAM) and structure-from …

We present a heterogeneous landmark-based visual navigation approach for a monocular
mobile robot. We utilize heterogeneous visual features, such as points, line segments, lines …

Though a large body of computer vision research has investigated developing generic
semantic representations, efforts towards developing a similar representation for 3D has …

We propose a dense indirect visual odometry method taking as input externally estimated
optical flow fields instead of hand-crafted feature correspondences. We define our problem …