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Wheeled robots have been widely used in People’s Daily life. Accurate positioning is the premise of autonomous navigation. In this paper, an optimization-based visual-inertial-wheel odometer tightly coupled system is proposed, which solves the problem of failure of visual inertia initialization due to unobservable scale.The aim of this paper is to achieve robust localization of visually challenging scenes.

During system initialization, the wheel odometer measurement and visual-inertial odometry (VIO) fusion are initialized using maximum a posteriori (MAP). Aiming at the visual challenge scene, a fusion method of wheel odometer and inertial measurement unit (IMU) measurement is proposed, which can still be robust initialization in the scene without visual features. To solve the problem of low track accuracy caused by cumulative errors of VIO, the local and global positioning accuracy is improved by integrating wheel odometer data. The system is validated on a public data set.

The results show that our system performs well in visual challenge scenarios, can achieve robust initialization with high efficiency and improves the state estimation accuracy of wheeled robots.

To realize robust initialization of wheeled robot, wheel odometer measurement and vision-inertia fusion are initialized using MAP. Aiming at the visual challenge scene, a fusion method of wheel odometer and IMU measurement is proposed. To improve the accuracy of state estimation of wheeled robot, wheel encoder measurement and plane constraint information are added to local and global BA, so as to achieve refined scale estimation.

To address the issues of low localization and mapping accuracy, as well as map ghosting and drift, in indoor degraded environments using light detection and ranging-simultaneous localization and mapping (LiDAR SLAM), a real-time localization and mapping system integrating filtering and graph optimization theory is proposed. By incorporating filtering algorithms, the system effectively reduces localization errors and environmental noise. In addition, leveraging graph optimization theory, it optimizes the poses and positions throughout the SLAM process, further enhancing map accuracy and consistency. The purpose of this study resolves common problems such as map ghosting and drift, thereby achieving more precise real-time localization and mapping results.

The system consists of three main components: point cloud data preprocessing, tightly coupled inertial odometry based on filtering and backend pose graph optimization. First, point cloud data preprocessing uses the random sample consensus algorithm to segment the ground and extract ground model parameters, which are then used to construct ground constraint factors in backend optimization. Second, the frontend tightly coupled inertial odometry uses iterative error-state Kalman filtering, where the LiDAR odometry serves as observations and the inertial measurement unit preintegration results as predictions. By constructing a joint function, filtering fusion yields a more accurate LiDAR-inertial odometry. Finally, the backend incorporates graph optimization theory, introducing loop closure factors, ground constraint factors and odometry factors from frame-to-frame matching as constraints. This forms a factor graph that optimizes the map’s poses. The loop closure factor uses an improved scan-text-based loop closure detection algorithm for position recognition, reducing the rate of environmental misidentification.

A SLAM system integrating filtering and graph optimization technique has been proposed, demonstrating improvements of 35.3%, 37.6% and 40.8% in localization and mapping accuracy compared to ALOAM, lightweight and ground optimized lidar odometry and mapping and LiDAR inertial odometry via smoothing and mapping, respectively. The system exhibits enhanced robustness in challenging environments.

This study introduces a frontend laser-inertial odometry tightly coupled filtering method and a backend graph optimization method improved by loop closure detection. This approach demonstrates superior robustness in indoor localization and mapping accuracy.

This paper aims to address the safety concerns of path-planning algorithms in dynamic obstacle warehouse environments. It proposes a method that uses improved artificial potential fields (IAPF) as expert knowledge for an improved deep deterministic policy gradient (IDDPG) and designs a hierarchical strategy for robots through obstacle detection methods.

The IAPF algorithm is used as the expert experience of reinforcement learning (RL) to reduce the useless exploration in the early stage of RL training. A strategy-switching mechanism is introduced during training to adapt to various scenarios and overcome challenges related to sparse rewards. Sensor inputs, including light detection and ranging data, are integrated to detect obstacles around waypoints, guiding the robot toward the target point.

Simulation experiments demonstrate that the integrated use of IDDPG and the IAPF method significantly enhances the safety and training efficiency of path planning for mobile robots.

This method enhances safety by applying safety domain judgment rules to improve APF’s security and designing an obstacle detection method for better danger anticipation. It also boosts training efficiency through using IAPF as expert experience for DDPG and the classification storage and sampling design for the RL experience pool. Additionally, adjustments to the actor network’s update frequency expedite convergence.

This study aims to extract knowledge of ancient Chinese scientific and technological documents bibliographic summaries (STDBS) and provide the knowledge graph (KG) comprehensively and systematically. By presenting the relationship among content, discipline, and author, this study focuses on providing services for knowledge discovery of ancient Chinese scientific and technological documents.

This study compiles ancient Chinese STDBS and designs a knowledge mining and graph visualization framework. The authors define the summaries' entities, attributes, and relationships for knowledge representation, use deep learning techniques such as BERT-BiLSTM-CRF models and rules for knowledge extraction, unify the representation of entities for knowledge fusion, and use Neo4j and other visualization techniques for KG construction and application. This study presents the generation, distribution, and evolution of ancient Chinese agricultural scientific and technological knowledge in visualization graphs.

The knowledge mining and graph visualization framework is feasible and effective. The BERT-BiLSTM-CRF model has domain adaptability and accuracy. The knowledge generation of ancient Chinese agricultural scientific and technological documents has distinctive time features. The knowledge distribution is uneven and concentrated, mainly concentrated on C1-Planting and cultivation, C2-Silkworm, and C3-Mulberry and water conservancy. The knowledge evolution is apparent, and differentiation and integration coexist.

This study is the first to visually present the knowledge connotation and association of ancient Chinese STDBS. It solves the problems of the lack of in-depth knowledge mining and connotation visualization of ancient Chinese STDBS.

Taking the support of social capital theory and conservation of resource theory, the present study explores the mediating role of rumination and moderating role of mindfulness in the relationship of workplace ostracism (WO) and workplace withdrawal (WW).

The data were collected in two waves from 467 employees working in hotels located in Delhi NCR region of India. The hypothesised relationships were investigated by macro-PROCESS (Hayes, 2013).

The results found a mediating impact of rumination on WO and WW relationship. It further supported the moderating effect of mindfulness in weakening the association between WO and WW via rumination.

This study identified mindfulness as an essential mechanism by which WO may be regulated to control employee's tendency to ruminate. Rumination may initially be prevented in organisations by regulating the primary effect of WO on employees' decisions for WW.

By linking the research model with the social capital theory, the study has contributed to the existing body of knowledge. The study is the first of its kind in India to examine the impact of hypothesised associations on the hotel industry. The findings of the study would help the industry in understanding the role of mindfulness in reducing aberrant behaviours at workplace.

The purpose of this study is to examine how the interplay of stressor (e.g. fear of missing out, FoMO) and strains (e.g. perceived social overload, communication overload, information overload and system feature overload) in social networking sites (SNS) use can contribute to users’ SNS fatigue from a configurational view.

Data were collected among 363 SNS users in China via an online survey, and fuzzy-set qualitative comparative analysis (fsQCA) was applied in this study to scrutinize the different combinations of FoMO and overload that contribute to the same outcome of SNS fatigue.

Six combinations of casual conditions were identified to underlie SNS fatigue. The results showed that FoMO, perceived information overload and system feature overload are the core conditions that contribute to SNS fatigue when combined with other types of overloads.

The current work supplements the research findings on SNS fatigue by identifying the configurations contributing to SNS fatigue from the joint effects of stressor (FoMO) and strain (perceived social overload, communication overload, information overload and system feature overload) and by providing explanations for SNS fatigue from the configurational perspective.

This study aims to deepen our understanding of how chatbots’ empathy influences humans–AI relationship in frontline service encounters. The authors investigate the underlying mechanisms, including perceived anthropomorphism, perceived intelligence and psychological empowerment, while also considering variations between different stages of the customer journey (before and after purchase).

Data collection was conducted through an online survey distributed among 301 customers who had experience using AI-based service chatbot in frontline service encounters in China. The hypotheses were examined through structural equation modeling and multi-group analysis.

The findings of this study revealed the positive impacts of emotional and cognitive empathy on humans–AI relationship through perceived anthropomorphism, perceived intelligence and psychological empowerment. Furthermore, this study verified the moderating effect of the customer journey stages, such that the impacts of anthropomorphism and intelligence on humans–AI relationship displayed more strength during the pre- and post-purchase phases, respectively.

This research offers practical implications for companies: recognize and enhance empathy dimensions in AI-based service chatbot to empower human–AI relationships; boost customer empowerment in human–AI interactions; and tailor anthropomorphic features in the pre-purchase stage and improve problem-solving capability in the post-purchase stage to enrich user experiences.

This study extends relationship marketing theory and human–AI interaction frameworks by investigating the underlying mechanisms of the effect of two-dimensional empathy on human–AI relationship. This study also enriches service design theories by revealing the moderating effect of customer journey stages.

The paper aims to estimate the house rental values for the purpose of customizing mass appraisals in Bahir Dar City, Ethiopia. It seeks to identify the critical factors affecting the rental values of residential properties and customize a mass appraisal model for such properties. The study focuses on identifying attributes that significantly affect house rental values.

The paper adopted a survey research design, utilizing a survey questionnaire, expert group discussion and document analysis. The data were analyzed using thematic, descriptive and inferential statistical analysis, including correlation and hedonic regression analysis.

Among the variables included in the model, the number of rooms, availability of schools, land value grading, type of nearest road, housing typology, built-up area, plot area, walling material, traveling cost and fencing materials were the most significant factors for predicting the annual rental value of residential properties in the city.

The findings of this study will provide valuable insights to tax assessors, property owners and local government authorities, including municipalities, concerning the key determinants of the rental values of residential properties. Besides, these findings will serve as a useful tool for valuers and researchers in the field of property value modeling.

This study represents the first attempt to develop a framework for mass appraisal of residential properties using annual rental values in the Ethiopian context.

This study aims to investigate how chatbots’ warmth and competence affect customer behavioural expectation (i.e. purchase, recommendation) through perceived humanness and perceived persuasiveness. Moreover, prior knowledge of chatbot is considered the boundary condition of the effects of chatbots’ warmth and competence.

A lab-in-field experiment with 213 participants and a scenario-based experiment of 186 participants were used to test the model using partial least squares structural equation modelling via SmartPLS 4.

Chatbot warmth positively affects customer behavioural expectation through perceived humanness while chatbot competence positively affects customer behavioural expectation through perceived persuasiveness. Prior knowledge of chatbot positively moderates the effect of chatbot warmth on perceived humanness.

This study provides nuanced insights into the effects of chatbots’ warmth and competence on customer behavioural expectation. Future studies could extend the model by exploring additional boundary conditions of the effects of chatbots’ warmth and competence in different generations.

This study offers insightful suggestions for marketing managers on how to impress and convert online customers through designing verbal scripts in customer−chatbot conversations that encourage the customers to anthropomorphise the chatbots.

This study probes into the effects of chatbots’ warmth and competence on customer behavioural expectation by proposing and examining a novel research model that incorporates perceived humanness and perceived persuasiveness as the explanatory mechanisms and prior knowledge of chatbot as the boundary condition.

This study aims to examine the present condition of blockchain technology (BT) applications in auditing by analyzing journal publications on the topic to acquire a better understanding of the field.

This study makes use of the Bibliometric Analysis method and gathered 725 papers from the Web of Science and Scopus databases in the management and accounting, business, financial, economic and social science, as well as decision sciences fields from 2017 to 2021 using the R-Package Bibliometrix Analysis “biblioshiny”.

The findings revealed that blockchain research in terms of auditing has already increased and started to spark a quick rise in popularity, but is still in its initial phases with important quality though less in quantity. Moreover, the Journal of Emerging Technologies in Accounting is the most prolific journal with 2019 as the highest publication year, with the United States and China as the most cited countries in this field. Furthermore, in this field, there are much research topics involving blockchain, audit and smart contracts; and there is less involving data analytics, governance, hyperledger, distributed ledger and financial reporting. Additionally, Sheldon (2019) and Smith and Castonguay (2020) are the most productive authors in the field in terms of the H-index.

This study has certain limitations such as the fact that it only looked at 105 papers in the domains of finance, business, economics, accounting, management as well as multidisciplinary science. Moreover, the research’s data and dates have an impact on the results dependability. As this is an original topic, fresh studies are anticipated to remain to shine a spotlight on and suggest answers to blockchain’s implications on auditing. Additionally, the period of time was limited to only the last five years, from 2017 to 2021. As a result, extensive study into the topic is required since there is currently a research deficit in the blockchain field in the setting of auditing. So, new research is required to offer new frameworks and understandings for describing the blockchain function in auditing, including processes, techniques, security, as well as timeliness. Investigations in unique circumstances and research employing innovative research methodologies for discovering the new issue would be valuable in acquiring a higher grasp of the complexities faced.

This research contributed to the field by assessing the present state of the art of research on the usage and use of BT in finding research gaps, the audit profession and, most importantly, recommending a future direction for researchers in the subject.