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The purposes of this paper are to solve the low-efficiency problem caused by large search space in global localization and develop an efficient global localization method requiring only one 2D-LiDAR scan to match against the prior map for indoor mobile robots.

This paper solves the global localization problem using phase correlation as the underlying registration method. To obtain accurate rotation parameter, this paper exhaustively pre-rotates the prior map by a certain angle stride in advance. Then the input scan is matched against the pre-rotated maps one by one using phase correlation to determine translation parameters, and this paper constructs an orientation histogram by the correlation coefficients. The map rotation angle and corresponding translation parameters of the maximal peak value in the orientation histogram constitute the global pose. This paper applies a divide-and-conquer method to reduce the time consumption of single phase correlation and determines promising angle ranges where the maximal peak value may appear based on the periodicity of 90º in the orientation histogram with the signal-to-noise ratio (SNR) to reduce execution times of phase correlation.

Both simulated and real experimental results reveal that the proposed method achieves a high enough success rate and efficient (processing time in a second) global localization.

The proposed method constructs an orientation histogram to improve the global localization success rate and applies a divide-and-conquer method with SNR to improve efficiency, which will benefit the indoor mobile robots equipped with 2D-LiDAR.

This paper aims to explore the effectiveness of customer inoculation strategies in the context of AI service failures in the hospitality and tourism industries. Furthermore, it examines how these strategies can enhance customer complaint behavior and satisfaction with service recovery, thereby improving the overall service experience.

Four distinct studies were conducted: Study 1 investigated the influence of customer inoculation on complaint behavior post-AI service failure. Study 2 assessed the impact of service remedies on customer satisfaction. Study 3 explored the implications of initial purchase and usage intentions. Finally, Study 4 validated the findings using a large-scale online survey.

The results indicated that customer inoculation significantly increases customer complaint behavior and satisfaction with service remedies following AI service failures. They also showed that this relationship is mediated by psychological distance. Furthermore, customer inoculation positively affects initial purchase and usage intentions, demonstrating effectiveness at various customer engagement stages.

This study enriches the literature on AI hospitality service failure and recovery by introducing the novel concept of customer inoculation. Additionally, it significantly contributes to the inoculation theory literature, which covers diverse fields. Practically, this study proposes an efficient and low-cost strategy for marketers.

This study introduces the concept of customer inoculation in the context of AI service failures, a novel approach in the hospitality and tourism literature. It provides empirical evidence of the efficacy of the strategy, bridging a crucial gap in understanding customer behavior in the face of technological disruptions.