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The principal objective of this study is to identify and recommend auspicious research directions within the field of family business research, with a specific focus on the wealth creator. In conjunction with these research trajectories, the paper also aims to elucidate the potential implications of cultivating these lines of inquiry on the existing family business literature.

This perspective paper adopts a comprehensive approach to examine the multifaceted role of the wealth creator in the context of family businesses. It reviews the last decades of research that resulted in the identification of this role within family business and proposes future research avenues to further address their characterization and importance.

Investigating the wealth creator's attributes can provide insights into their role, the importance of timely identification and its preparatory elements. Furthermore, this exploration can shed light on the dynamics of inter-family relationships within family businesses and enrich the literature on power transition and continuity in family enterprises. Additionally, the maturation of the wealth creator concept may significantly impact the management of wealth portfolios, facilitating smoother wealth transfer, strategic portfolio management and the preservation of multi-generational wealth.

This research offers a diverse understanding of the role of the wealth creator in family businesses. The findings also enhance the comprehension of family business dynamics, enriching the literature on succession. Lastly, the offered research avenues contribute to addressing the challenges of sustaining family wealth and ensuring the continuity of family businesses across generations.

The purpose of this paper is to present a scan matching simultaneous localization and mapping (SLAM) algorithm based on particle filter to generate the grid map online. It mainly focuses on reducing the memory consumption and alleviating the loop closure problem.

The proposed method alleviates the loop closure problem by improving the accuracy of the robot’s pose. First, two improvements were applied to enhance the accuracy of the hill climbing scan matching. Second, a particle filter was used to maintain the diversity of the robot’s pose and then to supply potential seeds to the hill climbing scan matching to ensure that the best match point was the global optimum. The proposed method reduces the memory consumption by maintaining only a single grid map.

Simulation and experimental results have proved that this method can build a consistent map of a complex environment. Meanwhile, it reduced the memory consumption and alleviates the loop closure problem.

In this paper, a new SLAM algorithm has been proposed. It can reduce the memory consumption and alleviate the loop closure problem without lowering the accuracy of the generated grid map.