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A Cockpit Panel Detection Visual System with Panel Localization and Button State Recognition

Due to the diversity of aircraft cockpit panels and the complexity of buttons, panel detection tasks are often complex and repetitive, and require a lot of manpower and time, but the application of robotics will make panel detection tasks easier. The robot accurately operating buttons is very important to panel detection, which requires to accurately locate the panel and recognize the state of button. However, the classic point cloud rough registration method takes a long runtime and cannot provide a reasonable initial pose for Iterative Closest Point (ICP) algorithm. Therefore, we propose a cockpit panel detection visual system. In terms of panel localization, YOLOv5 is applied for panel identification and segmentation, and a pose initialization method based on anchor vector is proposed, and a multi-scale ICP registration scheme is applied for pose refinement. In the aspect of button state recognition, the method combining HOG and SVM is used. We use three metrics to evaluate the panel localization method. Finally, we locate the panel and recognize button state to operate the button with dual-arm robot, and verify the robustness and accuracy of the panel detection visual system.