Real-time visual-inertial localization for aerial and ground robots

Abstract

Localization is essential for robots to operate autonomously, especially for extended periods of time, when estimator drift tends to destroy alignment to any global map. Though there has been extensive work in vision-based localization in recent years, including several systems that show real-time performance, none have been demonstrated running entirely on-board in closed loop on robotic platforms. We propose a fast, real-time localization system that keeps the existing local visual-inertial odometry frame consistent for controllers and collision avoidance, while correcting drift and alignment to a global coordinate frame. We demonstrate our localization system entirely on-board an aerial and ground robot, showing a collaboration experiment where both robots are able to localize against the same map accurately enough to allow the multicopter to land on top of the ground robot. We also perform extensive evaluations for the proposed closed-loop system on ground-truth datasets from MAV flight in an industrial setting.