A Novel Feedback Mechanism-Based Stereo Visual-Inertial SLAM

The case for visual-inertial SLAM over visual SLAM

Real-time Stereo Visual SLAM

Simultaneous localisation and mapping (SLAM) has been the focus of intensive research in the last decade due to the potential benefits it offers to the field of autonomous mobile robotics. SLAM is concerned with the ability of an autonomous vehicle to navigate through an unexplored environment and incrementally construct a map of the environment and localise itself within this map. This thesis ...

Keyframe-Based Visual-Inertial SLAM using Nonlinear Optimization

The fusion of visual and inertial cues has become popular in robotics due to the complementary nature of the two sensing modalities. While most fusion strategies to date rely on filtering schemes, the visual robotics community has recently turned to non-linear optimization approaches for tasks such as visual Simultaneous Localization And Mapping (SLAM), following the discovery that this comes w...

Asynchronous Adaptive Conditioning for Visual-Inertial SLAM

This paper is concerned with real-time monocular visual inertial simultaneous localization and mapping (VI-SLAM). In particular a tightly coupled nonlinear-optimization based solution that can match the global optimal result in real time is proposed. The methodology is motivated by the requirement to produce a scale-correct visual map, in an optimization framework that is able to incorporate re...

Large Scale Dense Visual Inertial SLAM

In this paper we present a novel large scale SLAM system that combines dense stereo vision with inertial tracking. The system divides space into a grid and efficiently allocates GPU memory only when there is surface information within a grid cell. A rolling grid approach allows the system to work for large scale outdoor SLAM. A dense visual inertial dense tracking pipeline incrementally localiz...