Robust visual-inertial localization with weak GPS priors for repetitive UAV flights

Abstract

Agile robots, such as small Unmanned Aerial Vehicles (UAVs) can have a great impact on the automation of tasks, such as industrial inspection and maintenance or crop monitoring and fertilization in agriculture. Their deploy-ability, however, relies on the UAV's ability to self-localize with precision and exhibit robustness to common sources of uncertainty in real missions. Here, we propose a new system using the UAV's onboard visual-inertial sensor suite to first build a Reference Map of the UAV's workspace during a piloted reconnaissance flight. In subsequent flights over this area, the proposed framework combines keyframe-based visual-inertial odometry with novel geometric image-based localization, to provide a real-time estimate of the UAV's pose with respect to the Reference Map paving the way towards completely automating repeated navigation in this workspace. The stability of the system is ensured by decoupling the local visual-inertial odometry from the global registration to the Reference Map, while GPS feeds are used as a weak prior for suggesting loop closures. The proposed framework is shown to outperform GPS localization significantly and diminishes drift effects via global image-based alignment for consistently robust performance.