IROS Conference 2022 Conference Paper
A 2D Georeferenced Map Aided Visual-Inertial System for Precise UAV Localization
- Jun Mao
- Lilian Zhang
- Xiaofeng He
- Hao Qu
- Xiaoping Hu
Precise geolocalization is crucial for unmanned aerial vehicles (UAVs). However, most current deployed UAVs rely on the global navigation satellite systems (GNSS) for geolocalization. In this paper, we propose to use a lightweight visual-inertial system with a 2D georeferenced map to obtain accurate geodetic positions for UAVs. The proposed system firstly integrates a micro inertial measurement unit (MIMU) and a monocular camera to build a visual-inertial odometry (VIO) to consecutively estimate the UAV's motion states and reconstruct the 3D position of the observed visual features in the local world frame. To obtain the geolocation, the visual features tracked by the odometry are further registered to the 2D georeferenced map. While most conventional methods perform image-level aerial image registration, we propose to align the reconstructed 3D points with the map, and then use the registered 3D points to relocalize the vehicle in the geodetic frame, which helps to improve the geolocalization accuracy. Finally, a pose graph is deployed to fuse the geolocation from the point registration and the local navigation result from the visual-inertial odometry (VIO) to obtain smooth and drift-free geolocalization results. We have validated the proposed method by installing the sensors to a UAV body rigidly and have conducted two real-world flights in different environments with unknown initials. The results show that the proposed method can achieve less than 4m position error in flight at about 100m high and less than 9m position error in flight at about 300m high.