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 backend 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 (UAV s). 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 SLAM. Video - https://youtu.be/wbX36HBu2Eg.