Evaluation of Keypoint Descriptors for Flight Simulator Cockpit Elements: WrightBroS Database

Abstract

The goal of the WrightBroS project is to design a system supporting the training of pilots in a flight simulator. The desired software should work on smart glasses supplementing the visual information with augmented reality data, displaying, for instance, additional training information or descriptions of visible devices in real time. Therefore, the rapid recognition of observed objects and their exact positioning is crucial for successful deployment. The keypoint descriptor approach is a natural framework that is used for this purpose. For this to be applied, the thorough examination of specific keypoint location methods and types of keypoint descriptors is required first, as these are essential factors that affect the overall accuracy of the approach. In the presented research, we prepared a dedicated database presenting 27 various devices of flight simulator. Then, we used it to compare existing state-of-the-art techniques and verify their applicability. We investigated the time necessary for the computation of a keypoint position, the time needed for the preparation of a descriptor, and the classification accuracy of the considered approaches. In total, we compared the outcomes of 12 keypoint location methods and 10 keypoint descriptors. The best scores recorded for our database were almost 96% for a combination of the ORB method for keypoint localization followed by the BRISK approach as a descriptor.

Robust Estimation and Optimized Transmission of 3D Feature Points for Computer Vision on Mobile Communication Network

“…Finally, the keypoint descriptor computes a descriptor vector for each keypoint in order to ensure that the descriptor is highly distinctive and partially invariant to the remaining variations, such as illumination, 3D viewpoint, etc. The final step is performed on the image closest in scale to the keypoint’s scale [ 46 , 47 , 48 ].…”