Towards Globally Consistent Visual-Inertial Collaborative SLAM

Abstract

Motivated by the need for globally consistent tracking and mapping before autonomous robot navigation becomes realistically feasible, this paper presents a novel back-end to monocular-inertial odometry. As some of the most challenging platforms for vision-based perception, we evaluate the performance of our system using Unmanned Aerial Vehicles (UAVs). Our experimental validation demonstrates that the proposed approach achieves drift correction and metric scale estimation from a single UAV on benchmarking datasets. Furthermore, the generality of our approach is demonstrated to achieve globally consistent maps built in a collaborative manner from two UAVs, each equipped with a monocular-inertial sensor suite, showing the possible gains opened by collaboration amongst robots to perform SLAMVideo.