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The study explored the relationship between buyer–seller interactions and customer satisfaction in the small apparel fashion enterprises in the emerging markets. The moderating role of COVID-19 protocols implementations on buyer–seller interactions and customer satisfaction was further examined.

Buyer–seller interactions affecting customer satisfaction were divided into three constructs, namely, interactions relating to the overall customers shopping experience, smooth payment process and in-store interactions, and the COVID-19 protocols implementations were used as a moderator. A convenient sampling strategy was adopted to survey 450 customers of apparel fashion enterprises within the four regions in Ghana, of which 397 were validly used for the analysis. Existing questionnaires were adapted to collect data from the respondents. The data collected was therefore analysed using SPSS and SmartPLS programme to ascertain the nature of the relationships among the variables.

The study found that, in-store interactions, shopping experience and smooth payment processes directly influence customer satisfaction. However, the implementation of COVID-19 protocols failed to moderate the relationship between buyer–seller interactions and customer satisfaction.

The limitations of the study involve its context-specific, focusing on the small apparel and fashion market. Also, future researchers can re-examine the model in other geographical jurisdictions, focusing on small apparel owners’ competencies and other variables that position buyer–seller interactions as precursors of customer satisfaction in the small apparel fashion industry. The theoretical and managerial relevance of the findings are also discussed.

The paper extends the domain of buyer–seller interactions and customer satisfaction phenomena within the apparel fashion industry. Its examination of the impact of COVID-19 protocols’ implementation on customer satisfaction provides an insight into managers regarding how the applications can affect customers in a typical shopping environment.

Autonomous mobile robots (AMRs) play a crucial role in industrial and service fields. The paper aims to build a LiDAR-based simultaneous localization and mapping (SLAM) system used by AMRs to overcome challenges in dynamic and changing environments.

This research introduces SLAM-RAMU, a lifelong SLAM system that addresses these challenges by providing precise and consistent relocalization and autonomous map updating (RAMU). During the mapping process, local odometry is obtained using iterative error state Kalman filtering, while back-end loop detection and global pose graph optimization are used for accurate trajectory correction. In addition, a fast point cloud segmentation module is incorporated to robustly distinguish between floor, walls and roof in the environment. The segmented point clouds are then used to generate a 2.5D grid map, with particular emphasis on floor detection to filter the prior map and eliminate dynamic artifacts. In the positioning process, an initial pose alignment method is designed, which combines 2D branch-and-bound search with 3D iterative closest point registration. This method ensures high accuracy even in scenes with similar characteristics. Subsequently, scan-to-map registration is performed using the segmented point cloud on the prior map. The system also includes a map updating module that takes into account historical point cloud segmentation results. It selectively incorporates or excludes new point cloud data to ensure consistent reflection of the real environment in the map.

The performance of the SLAM-RAMU system was evaluated in real-world environments and compared against state-of-the-art (SOTA) methods. The results demonstrate that SLAM-RAMU achieves higher mapping quality and relocalization accuracy and exhibits robustness against dynamic obstacles and environmental changes.

Compared to other SOTA methods in simulation and real environments, SLAM-RAMU showed higher mapping quality, faster initial aligning speed and higher repeated localization accuracy.