Abstract
As unmanned aerial vehicles (UAVs) become widely used in various civil applications, many civil aerodromes are being transformed into a hybrid environment for both manned and unmanned aircraft. In order to make these hybrid aerodromes operate safely and efficiently, the autonomous taxiing system of UAVs that adapts to the dynamic environment has now become increasingly important, particularly under poor visibility conditions. In this paper, we develop a probabilistic self-learning approach for the situation awareness of UAVs' autonomous taxiing. First, the probabilistic representation for a dynamic navigation map and camera images are developed at the pixel level to capture the taxiway markings and the other objects of interest (e.g., logistic vehicles and other aircraft). Then, we develop a self-learning approach so that the navigation map can be maintained online by continuously map-updating with the obtained camera observations via Bayesian learning. An indoor experiment was undertaken to evaluate the developed self-learning method for improved situation awareness. It shows that the developed approach is capable of improving the robustness of obstacle detection via updating the navigation map dynamically.
Highlights
A FTER several decades of research and development, unmanned aerial vehicles (UAVs) are widely used in civil applications
We focus on two important issues of the self-learning process: (a) how knowledge is accumulated over time; and (b) how to deal with a dynamic environment with obstacles
With respect to obstacle detection and the navigation map updating, we considered two approaches: the approach without the self-learning where the obstacle detection was based on the saliency threshold only, and the approach with the self-learning enhanced obstacle detection where the probability distribution incorporated the prior knowledge
Summary
A FTER several decades of research and development, unmanned aerial vehicles (UAVs) are widely used in civil applications (e.g. monitoring gas pipelines [1] and surveillance of electrical power infrastructures [2]). By combining the two lines of research on vehicle lane detection and vehicle localization, [22] has made the first attempt to use both the map information and camera images for a taxiway centerline extraction in an aerodrome scenario. We propose a self-learning probabilistic approach for maintaining and updating a dynamic navigation map for autonomous taxiing that includes an aerodrome map and an obstacle map. The obtained posterior distribution of the dynamic navigation map at the current time step is further treated as prior knowledge at the time step for processing the frame of image In this way, the navigation map can be dynamically maintained and updated to adapt to the dynamic environment of an aerodrome.
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