Large scale dense visual inertial SLAM

Abstract

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 localizes stereo cameras against the scene. The proposed system is tested with both a simulated data set and several real-life data in different lighting (illumination changes), motion (slow and fast), and weather (snow, sunny) conditions. Compared to structured light-RGBD systems the proposed system works indoors and outdoors and over large scales beyond single rooms or desktop scenes. Crucially, the system is able to leverage inertial measurements for robust tracking when visual measurements do not suffice. Results demonstrate effective operation with simulated and real data, and both indoors and outdoors under varying lighting conditions.