Weixi Wang

Understanding the spatial differences and evolutionary characteristics of urban economy and exploring the impact of industrial agglomeration and industrial proximity on urban economic convergence are the bases for scientifically formulating policies for coordinated regional economic development. This study used QGIS 3.10.10 software and the Theil index to analyze the spatial distribution characteristics and regional disparities of urban economy. Then, a spatial econometric model was constructed to analyze the convergence and influencing factors of Guangdong’s urban economy. The results indicate that from 2006 to 2020, Guangdong’s urban economy grew rapidly and the degree of economic agglomeration gradually weakened, but its economic pattern always maintained the “Core-Edge” structural feature. The interval disparities between the Pearl River Delta Urban Agglomeration (PRD) and the edge area have always been greater than the intra-regional disparities, so they are main source of disparities in Guangdong. In Guangdong’s urban economy, σ-convergence and β-convergence coexist. The conditional β-convergence rate is 0.96~1.53%, and the half-life cycle is 45.4~72.36 years. Compared to the PRD, the economic disparities in the edge area are smaller but the convergence speed is faster and the half-life cycle is shorter. Both industrial agglomeration and industrial proximity have a significant impact on the economic convergence of Guangdong’s cities. Among them, industrial agglomeration has a positive impact, while industrial proximity has a negative impact. There is spatial heterogeneity in the impact of industries on economic development. Industrial agglomeration has a positive impact on the overall economic development of Guangdong, but it is not significant within the regions. Industrial proximity has significant negative externalities in the PRD region, and its impact is not significant in the edge area.

RGB-D dense mapping systems are widely used for indoor navigation and augmented reality. However, the drift in egomotion estimation systems accumulates as the systems expand over space and time. In this study, we develop a vertex-to-edge weighted closed-form algorithm to reduce camera drift for dense RGB-D indoor simultaneous localization and mapping (SLAM). First, due to the high frame rate of common RGB-D sensors, in our approach we employ a robust key-frame selection strategy. We then consider the typical depth noise properties of the sensor and exploit both visual and geometric information to determine the camera motion of the key frames. To enforce global consistency, we employ a combination of techniques to enhance the efficiency and accuracy of loop closure detection and then factor graph optimization is used to mitigate trajectory drift; specifically, the graph edges are weighted by residual errors. To demonstrate its accuracy and robustness, our SLAM system is tested on seven sets of sequences from publicly available benchmark datasets collected with Kinect v1 and on three sets of sequences taken with a handheld structure sensor. In a direct comparison, our approach yields significantly less trajectory error than several other state-of-the-art methods.

Lampposts, traffic lights, traffic signs, utility poles and so forth are important road furniture in urban areas. The fast and accurate localization and extraction of this type of furniture is urgent for the construction and updating of infrastructure databases in cities. This paper proposes a pipeline for mobile laser scanning data processing to locate and extract road poles. The proposed method is based on the vertical continuity with isolation feature of the pole part and the overall roughness feature of the attachment part of road poles. The isolation feature of the pole part is analysed by constructing two concentric cylinders from bottom to top and there should be no or a limited number of, points between these two cylinders. After splitting up the pole part and the attachment part of a road pole, the roughness of the candidate attachment points is computed and the attachment is obtained by performing region growing method based on roughness values. By applying the proposed pipeline to different situations in two datasets, the proposed method proves to be efficient not only in simple scenes but also in cluttered scenes.