A Loosely-Coupled Approach for Metric Scale Estimation in Monocular Vision-Inertial Systems
This work addresses metric scale estimation for flying robots using monocular vision-inertial systems, presenting an incremental improvement with flexible, loosely-coupled fusion.
The paper tackles the scale ambiguity problem in monocular vision systems by fusing inertial measurements with monocular odometry to estimate metric distances and increase pose estimation rates, achieving results through experiments with ORB-SLAM and Euler integration on UAV data.
In monocular vision systems, lack of knowledge about metric distances caused by the inherent scale ambiguity can be a strong limitation for some applications. We offer a method for fusing inertial measurements with monocular odometry or tracking to estimate metric distances in inertial-monocular systems and to increase the rate of pose estimates. As we performed the fusion in a loosely-coupled manner, each input block can be easily replaced with one's preference, which makes our method quite flexible. We experimented our method using the ORB-SLAM algorithm for the monocular tracking input and Euler forward integration to process the inertial measurements. We chose sets of data recorded on UAVs to design a suitable system for flying robots.