Localization is the most important basic information for unmanned aerial vehicles (UAV) during their missions. Currently, most UAVs use GNSS to calculate their own position. However, when faced with complex electromagnetic interference situations or multipath effects within cities, GNSS signals can be interfered with, resulting in reduced positioning accuracy or even complete unavailability. To avoid this situation, this paper proposes an autonomous UAV localization method for low-altitude urban scenarios based on POI and store signage text matching (LPS) in UAV images. The text information of the store signage is first extracted from the UAV images and then matched with the name of the POI data. Finally, the scene location of the UAV images is determined using multiple POIs jointly. Multiple corner points of the store signage in a single image are used as control points to the UAV position. As verified by real flight data, our method can achieve stable UAV autonomous localization with a positioning error of around 13 m without knowing the exact initial position of the UAV at take-off. The positioning effect is better than that of ORB-SLAM2 in long-distance flight, and the positioning error is not affected by text recognition accuracy and does not accumulate with flight time and distance. Combined with an inertial navigation system, it may be able to maintain high-accuracy positioning for UAVs for a long time and can be used as an alternative to GNSS in ultra-low-altitude urban environments.

In urban scenes, buildings are usually dense and exhibit similar shapes. Thus, existing autonomous unmanned aerial vehicle (UAV) localization schemes based on map matching, especially the semantic shape matching (SSM) method, cannot capture the uniqueness of buildings and may result in matching failure. To solve this problem, we propose a new method to locate UAVs via shape and spatial relationship matching (SSRM) of buildings in urban scenes as an alternative to UAV localization via image matching. SSRM first extracts individual buildings from UAV images using the SOLOv2 instance segmentation algorithm. Then, these individual buildings are subsequently matched with vector e-map data (stored in .shp format) based on their shape and spatial relationship to determine their actual latitude and longitude. Control points are generated according to the matched buildings, and finally, the UAV position is determined. SSRM can efficiently realize high-precision UAV localization in urban scenes. Under the verification of actual data, SSRM achieves localization errors of 7.38 m and 11.92 m in downtown and suburb areas, respectively, with better localization performance than the radiation-variation insensitive feature transform (RIFT), channel features of the oriented gradient (CFOG), and SSM algorithms. Moreover, the SSRM algorithm exhibits a smaller localization error in areas with higher building density.

Road network extraction plays a critical role in data updating, urban development, and decision support. To improve the efficiency of labeling road datasets and addressing the problems of traditional methods of manually extracting road networks from high-resolution images, such as their slow speed and heavy workload, this paper proposes a semi-automatic method of road network extraction from high-resolution remote-sensing images. The proposed method needs only a few points to extract a single road in the image. After the roads are extracted one by one, the road network is generated according to the width of each road and the spatial relationships among the roads. For this purpose, we use regional growth, morphology, vector tracking, vector simplification, endpoint modification, road connections, and intersection connections to generate road networks. Experiments on four images with different terrains and different resolutions show that this method has high extraction accuracy under different image conditions. The comparisons with the semi-automatic GVF-snake method based on regional growth also showed its advantages and potentiality. The proposed method is a novel form of semi-automatic road network extraction, and it significantly increases the efficiency of road network extraction.

Cholesteric liquid crystal microcapsules (CLCMs) are used to improve the stability of liquid crystals while ensuring their stimulus response performance and versatility, with representative applications such as sensing, anticounterfeiting, and smart fabrics. However, the reflectivity and angular anisotropy decrease because of the anchoring effect of the polymer shell matrix, and the influence of particle size on this has not been thoroughly studied. In this study, the effect of synthesis technology on microcapsule particle size was investigated using a complex coalescence method, and the effect of particle size on the reflectivity and angular anisotropy of CLCMs was investigated in detail. A particle size of approximately 66 µm with polyvinyl alcohol (PVA, 1:1) exhibited a relative reflectivity of 16.6% and a bandwidth of 20 nm, as well as a narrow particle size distribution of 22 µm. The thermosetting of microcapsules coated with PVA was adjusted and systematically investigated by controlling the mass ratio. The optimized mass ratio of microcapsules (66 µm) to PVA was 2:1, increasing the relative reflectivity from 16.6% (1:1) to 32.0% (2:1) because of both the higher CLCM content and the matching between the birefringence of the gelatin–arabic shell system and PVA. Furthermore, color based on Bragg reflections was observed in the CLCM-coated ortho-axis and blue-shifted off-axis, and this change was correlated with the CLCM particle size. Such materials are promising for anticounterfeiting and color-based applications with bright colors and angular anisotropy in reflection.

Absolute visual localization based on onboard cameras and georeferenced data is the dominant solution for unmanned aerial vehicles (UAVs) when the global navigation satellite system is not available. However, current image-based matching methods are easily affected by significant feature differences between UAV images and georeferenced images, and map-based matching methods are affected by image rotation and resolution differences. To address the above-mentioned challenges, this article proposes a rotation and scale invariance map matching method (RSIM) to realize UAV visual localization in urban scenarios. Specifically, RSIM first extracts building contour information from UAV images. Then, based on the shape and spatial relationship features of the buildings, a scene matching method is designed to match the UAV image and the vector e-map. Finally, the centers of the matched building individuals are used as control points for UAV position solving. Extensive experiments conducted on the LoFTR and DKM datasets demonstrate that the proposed RSIM algorithm achieves superior localization performance compared to existing deep learning-based image matching methods. Moreover, our RSIM effectively enables UAV localization in urban scenarios even when the orientation and resolution of UAV images are unknown, achieving performance comparable to the SSRM map matching method, which relies on prior knowledge of these parameters.

This paper presents a new method using multi-objective optimization algorithm to automatically find the best solution from a topology library of analog circuits. Firstly this method abstracts the Pareto-front of each topology in the library by SPICE simulation. Then, the Pareto-front of the topology library is abstracted from the Pareto-fronts of topologies in the library followed by the theorem we proved. The best solution which is defined as the nearest point to specification on the Pareto-front of the topology library is then calculated by the equations derived from collinearity theorem. After the local searching using Nelder-Mead method maps the calculated best solution back to design variable space, the non-dominated best solution is obtained.