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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.

This study aims to better understand tourists’ revisit intention from their perspectives of past and expected experience and to investigate whether the formation of revisit intention differs between tourists from different nationalities.

The sample consisted of 250 Indonesian tourists who have visited Australia and 275 Australian tourists who have visited Indonesia. The data were analysed by conducting structural equation modelling and multi-group analysis with group comparisons.

Indonesian tourists’ intention to revisit Australia was influenced by past feeling experience and expected experience, while Australian tourists’ intention to revisit Indonesia was influenced by past feeling and relating experiences and expected experience. Both samples differ significantly in terms of relations between experiences and revisit intention. The relationship between past thinking experience and revisit intention was positive for Indonesians but negative for Australians. The relationship between past relating and expected experience was positive for Australians but negative for Indonesians. In addition, the influence of expected experience on revisit intention was stronger for Australian than Indonesian tourists.

To the best of the authors’ knowledge, this study, for the first time, enriches tourism literature in understanding revisit intention by investigating revisit intention in relation to both past and expected experiences, along with examining nationality differences in the revisit intention formation.

The COVID-19 pandemic has posed many challenges in almost all sectors around the globe. Because of the pandemic, government entities responsible for managing health-care resources face challenges in managing and distributing their limited and valuable health resources. In addition, severe outbreaks may occur in a small or large geographical area. Therefore, county-level preparation is crucial for officials and organizations who manage such disease outbreaks. However, most COVID-19-related research projects have focused on either state- or country-level. Only a few studies have considered county-level preparations, such as identifying high-risk counties of a particular state to fight against the COVID-19 pandemic. Therefore, the purpose of this research is to prioritize counties in a state based on their COVID-19-related risks to manage the COVID outbreak effectively.

In this research, the authors use a systematic hybrid approach that uses a clustering technique to group counties that share similar COVID conditions and use a multi-criteria decision-making approach – the analytic hierarchy process – to rank clusters with respect to the severity of the pandemic. The clustering was performed using two methods, k-means and fuzzy c-means, but only one of them was used at a time during the experiment.

The results of this study indicate that the proposed approach can effectively identify and rank the most vulnerable counties in a particular state. Hence, state health resources managing entities can identify counties in desperate need of more attention before they allocate their resources and better prepare those counties before another surge.

To the best of the authors’ knowledge, this study is the first to use both an unsupervised learning approach and the analytic hierarchy process to identify and rank state counties in accordance with the severity of COVID-19.