Most of the navigation systems used in today’s aircraft rely on Global Navigation Satellite Systems (GNSS). However, GNSS is not fully reliable. For example, it can be jammed by attacks on the space or ground segments of the system or denied at inaccessible areas. Hence to ensure successful navigation it is of great importance to continuously be able to establish the aircraft’s location without having to rely on external reference systems. Localization is one of many sub-problems in navigation and will be the focus of this thesis. This brings us to the field of visual odometry (VO), which involves determining position and orientation with the help of images from one or more camera sensors. But to date, most VO systems have primarily been established on ground vehicles and low flying multi-rotor systems. This thesis seeks to extend VO to new applications by exploring it in a fairly new context; a fixed-wing piloted combat aircraft, for vision-only pose estimation in applications of extremely large scene depth. A major part of this research work is the data gathering, where the data is collected using the flight simulator X-Plane 11. Three different flight routes are flown; a straight line, a curve and a loop, for two types of visual conditions; in clear weather with daylight and during sunset. The method used in this work is ORB-SLAM3, an open-source library for visual simultaneous localization and mapping (SLAM). It has shown excellent results in previous works and has become a benchmark method often used in the field of visual pose estimation. ORB-SLAM3 tracks the straight line of 78 km very well at an altitude over 2700 m. The absolute trajectory error (ATE) is 0.072% of the total distance traveled in daylight and 0.11% during sunset. These results are of the same magnitude as ORB-SLAM3 on the EuRoC MAV dataset. For the curved trajectory of 79 km ATE is 2.0% and 1.2% of total distance traveled in daylight and sunset respectively. The longest flight route of 258 km shows the challenges of visual pose ...
Localization of Combat Aircraft at High Altitude using Visual Odometry
2022-01-01
Theses
Electronic Resource
English
Selective visual odometry for accurate AUV localization
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