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Docking technology plays a crucial role in enabling long-duration operations of autonomous underwater vehicles (AUVs). Visual positioning solutions alone are susceptible to abnormal drift values due to the challenging underwater optical imaging environment. When an AUV approaches the docking station, the absolute positioning method fails if the AUV captures an insufficient number of tracers. This study aims to to provide a more stable absolute position visual positioning method for underwater terminal visual docking.

This paper presents a six-degree-of-freedom positioning method for AUV terminal visual docking, which uses lights and triangle codes. The authors use an extended Kalman filter to fuse the visual calculation results with inertial measurement unit data. Moreover, this paper proposes a triangle code recognition and positioning algorithm.

The authors conducted a simulation experiment to compare the underwater positioning performance of triangle codes, AprilTag and Aruco. The results demonstrate that the implemented triangular code reduces the running time by over 70% compared to the other two codes, and also exhibits a longer recognition distance in turbid environments. Subsequent experiments were carried out in Qingjiang Lake, Hubei Province, China, which further confirmed the effectiveness of the proposed positioning algorithm.

This fusion approach effectively mitigates abnormal drift errors stemming from visual positioning and cumulative errors resulting from inertial navigation. The authors also propose a triangle code recognition and positioning algorithm as a supplementary approach to overcome the limitations of tracer light positioning beacons.

Industrial land renewal is a significant constituent of urban environment and sustainable development. Most implementation in planning of renewal of industrial land has been mainly conducted at the site level of industrial zones or parks and the larger scale of township planning deserve further attention in China. To fill this gap, this paper aims to investigate the implementation of industrial land renewal for a whole urbanized area under the township master planning.

This study introduces a progressive approach to identify and evaluate the renewal of industrial land in township master planning to move toward a more practical understanding of industrial transition. The authors chose a typical industrialized town, Lijia in Changzhou City, under the development model of “Southern Jiangsu” to explain the measurement and assessment framework to identify and evaluate the renewable industrial land. Synthesizing the idea of sustainable development, the authors investigated the renewable industrial land with an econometric model including multiple-indexes of economic, social and ecological aspects, field observations and depth interviews.

The analysis demonstrated the spatial heterogeneity and complex generous structure of industrial land renewal in developing countries. It pointed out the major responsibility of enterprises as main industrial land users and indispensable responsibility of government and society. Following the idea of organic concentration and avoiding one-size-fits-all kind of deal, the master planning of Lijia emphasized the connection of industrial land and the combination of market force, social force and government regulation.

With original data and discussion, the authors provide more scientific renewal strategies for planners in sustainable development.

The purpose of this paper is to improve the robustness of the traditional Bhattacharyya metric for the effect of histogram quantization in the histogram-based visual tracking. However, the traditional Bhattacharyya metric neglects the correlation of crossing-bin and is not robust for the effect of histogram quantization.

In this paper, the authors propose a visual tracking method via crossing-bin histogram Bhattacharyya similarity in the particle filter.

A crossing-bin matrix is introduced into the traditional Bhattacharyya similarity for measuring the reference histogram and the candidate histogram, and the basic tasks of measure such as maximum similarity of self and the triangle inequality are proven. The authors use the proposed measure in the particle filter visual tracking framework and address a model update strategy based on the crossing-bin histogram Bhattacharyya similarity to improve the robustness of visual tracking.

In the experiments using the famous challenging benchmark sequences, precision of the proposed method increases by 12.8 per cent comparing the traditional Bhattacharyya similarity and the cost time decreases by 38 times comparing the incremental Bhattacharyya similarity. The experimental results show that the proposed method can track the object robustly and rapidly under illumination change and occlusion.