Conventional reconnaissance camera systems have been flown on manned aircraft, where the weight, size, and power requirements are not stringent. However, today, these parameters are important for unmanned aerial vehicles (UAVs). This article provides a solution to the design of airborne large aperture infrared optical systems, based on a monocentric lens that can meet the strict criteria of aerial reconnaissance UAVs for a wide field of view (FOV) and lightness of airborne electro-optical pod cameras. A monocentric lens has a curved image plane, consisting of an array of microsensors, which can provide an image with 368 megapixels over a 100° FOV. We obtained the initial structure of a five-glass (5GS) asymmetric monocentric lens with an air gap, using ray-tracing and global optimization algorithms. According to the design results, the ground sampling distance (GSD) of the system is 0.33 m at 3000 m altitude. The full-field modulation transfer function (MTF) value of the system is more than 0.4 at a Nyquist frequency of 70 lp/mm. We present a primary thermal control method, and the image quality was steady throughout the operating temperature range. This compactness and simple structure fulfill the needs of uncrewed airborne lenses. This work may facilitate the practical application of monocentric lens in UAVs.

Supervoxels have a widespread application of instance segmentation on account of the merit of providing a highly approximate representation with fewer data. However, low accuracy, mainly caused by point cloud adhesion in the localization of industrial robots, is a crucial issue. An improved bottom-up clustering method based on supervoxels was proposed for better accuracy. Firstly, point cloud data were preprocessed to eliminate the noise points and background. Then, improved supervoxel over-segmentation with moving least squares (MLS) surface fitting was employed to segment the point clouds of workpieces into supervoxel clusters. Every supervoxel cluster can be refined by MLS surface fitting, which reduces the occurrence that over-segmentation divides the point clouds of two objects into a patch. Additionally, an adaptive merging algorithm based on fusion features and convexity judgment was proposed to accomplish the clustering of the individual workpiece. An experimental platform was set up to verify the proposed method. The experimental results showed that the recognition accuracy and the recognition rate in three different kinds of workpieces were all over 0.980 and 0.935, respectively. Combined with the sample consensus initial alignment (SAC-IA) coarse registration and iterative closest point (ICP) fine registration, the coarse-to-fine strategy was adopted to obtain the location of the segmented workpieces in the experiments. The experimental results demonstrate that the proposed clustering algorithm can accomplish the localization of industrial robots with higher accuracy and lower registration time.