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Partial alignment for 3 D point sets is a challenging problem for laser calibration and robot calibration due to the unbalance of data sets, especially when the overlap of data sets is low. Geometric features can promote the accuracy of alignment. However, the corresponding feature extraction methods are time consuming. The purpose of this paper is to find a framework for partial alignment by an adaptive trimmed strategy.

First, the authors propose an adaptive trimmed strategy based on point feature histograms (PFH) coding. Second, they obtain an initial transformation based on this partition, which improves the accuracy of the normal direction weighted trimmed iterative closest point (ICP) method. Third, they conduct a series of GPU parallel implementations for time efficiency.

The initial partition based on PFH feature improves the accuracy of the partial registration significantly. Moreover, the parallel GPU algorithms accelerate the alignment process.

This study is applicable to rigid transformation so far. It could be extended to non-rigid transformation.

In practice, point set alignment for calibration is a technique widely used in the fields of aircraft assembly, industry examination, simultaneous localization and mapping and surgery navigation.

Point set calibration is a building block in the field of intelligent manufacturing.

The contributions are as follows: first, the authors introduce a novel coarse alignment as an initial calibration by PFH descriptor similarity, which can be viewed as a coarse trimmed process by partitioning the data to the almost overlap part and the rest part; second, they reduce the computation time by GPU parallel coding during the acquisition of feature descriptor; finally, they use the weighted trimmed ICP method to refine the transformation.

Organisations encourage green creativity among their employees to mitigate pollution and achieve sustainable growth. Green inclusive leadership practices have a key role in influencing employees’ green attitudes and environmental efficiency. Thus, the purpose of this study is to investigate how green inclusive leadership influences employees’ green creativity. It also aims to analyse the intermediating mechanism of green human capital and employee voice between the relationship of green inclusive leadership and green creativity.

Data was collected through an in-person administered questionnaire-based survey from 312 employees of the manufacturing industry of Pakistan. SPSS PROCESS macro was used for hypothesis testing in the present study.

The findings depict that the perception of green inclusive leadership positively influences employees’ green creativity. Moreover, the findings demonstrate that green human capital and employee voice play substantial intervening roles among the associations investigated.

This research study is novel because it is one of the scarce research studies to examine green inclusive leadership and employees’ green creativity with the underlying mechanism of green human capital and employee voice in an eastern context.