Search

Reliability of visual tracking and mapping is a challenging problem in robotics research, and it limits the promotion of vision-based mobile robot applications to a great extent. In this paper, we propose to improve the reliability of visual exploration in terms of its fault tolerance. Three modules are involved in our visual exploration system: visual localization and mapping, active controller and termination condition. High maintainability of mapping is obtained by the submap-based visual mapping module, persistent driving is achieved by a semantic segmentation based active controller, and robustness of re-localization is guaranteed by a novel completeness evaluation method in the termination condition. All the modules are integrated tightly for maintaining mapping and improving visual tracking. The system is verified with simulations and real world experiments, and all the solutions to fault tolerance are verified to overcome the failure conditions of visual tracking and mapping.

Spatio-temporal relations among movement events extracted from temporally varying trajectory data can provide useful information about the evolution of individual or collective movers, as well as their interactions with their spatial and temporal contexts. However, the pure statistical tools commonly used by analysts pose many difficulties, due to the large number of attributes embedded in multiscale and multi-semantic trajectory data. The need for models that operate at multiple scales to search for relations at different locations within time and space, as well as intuitively interpret what these relations mean, also presents challenges. Since analysts do not know where or when these relevant spatio-temporal relations might emerge, these models must compute statistical summaries of multiple attributes at different granularities. In this paper, we propose a multi-view approach to visualize the spatio-temporal relations among movement events. We describe a method for visualizing movement events and spatio-temporal relations that uses multiple displays. A visual interface is presented, and the user can interactively select or filter spatial and temporal extents to guide the knowledge discovery process. We also demonstrate how this approach can help analysts to derive and explain the spatiotemporal relations of movement events from taxi trajectory data. [ABSTRACT FROM AUTHOR]

Ionospheric Scintillations are rapid variations on the phase and/or amplitude of a radio signal as it passes through ionospheric plasma irregularities. The ionosphere is a specific layer of the Earth's atmosphere located approximately between 50 km and 1000 km above the Earth's surface. As Global Navigation Satellite Systems (GNSS) – such as GPS, Galileo, BDS and GLONASS – use radio signals, these variations degrade their positioning service quality. Due to its location, Brazil is one of the places most affected by scintillation in the world. For that reason, ionosphere monitoring stations have been deployed over Brazilian territory since 2011 through cooperative projects between several institutions in Europe and Brazil. Such monitoring stations compose a network that generates a large amount of monitoring data everyday. GNSS receivers deployed at these stations – named Ionospheric Scintillation Monitor Receivers (ISMR) – provide scintillation indices and related signal metrics for available satellites dedicated to satellite-based navigation and positioning services. With this monitoring infrastructure, more than ten million observation values are generated and stored every day. Extracting the relevant information from this huge amount of data was a hard process and required the expertise of computer and geoscience scientists. This paper describes the concepts, design and aspects related to the implementation of the software that has been supporting research on ISMR data – the so-called ISMR Query Tool. Usability and other aspects are also presented via examples of application. This web based software has been designed and developed aiming to ensure insights over the huge amount of ISMR data that is fetched every day on an integrated platform. The software applies and adapts time series mining and information visualization techniques to extend the possibilities of exploring and analyzing ISMR data. The software is available to the scientific community through the World Wide Web, therefore constituting an analysis infrastructure that complements the monitoring one, providing support for researching ionospheric scintillation in the GNSS context. Interested researchers can access the functionalities without cost at http://is-cigala-calibra.fct.unesp.br/ , under online request to the Space Geodesy Study Group from UNESP – Univ Estadual Paulista at Presidente Prudente. [ABSTRACT FROM AUTHOR]

The association rule mining is one of the most popular data mining techniques, however, the users often experience difficulties in interpreting and exploiting the association rules extracted from large transaction data with high dimensionality. The primary reasons for such difficulties are two-folds. Firstly, too many association rules can be produced by the conventional association rule mining algorithms, and secondly, some association rules can be partly overlapped. This problem can be addressed if the user can select the relevant items to be used in association rule mining, however, there are often quite complex relations among the items in large transaction data. In this context, this paper aims to propose a novel visual exploration tool, structured association map (SAM), which enables the users to find the group of the relevant items in a visual way. The appearance of SAM is similar with the well-known cluster heat map, however, the items in SAM are sorted in more intelligent way so that the users can easily find the interesting area formed by a set of associated items, which are likely to constitute interesting many-to-many association rules. Moreover, this paper introduces an index called S2C, designed to evaluate the quality of SAM, and explains the SAM based association analysis procedure in a comprehensive manner. For illustration, this procedure is applied to a mass health examination result data set, and the experiment results demonstrate that SAM with high S2C value helps to reduce the complexities of association analysis significantly and it enables to focus on the specific region of the search space of association rule mining while avoiding the irrelevant association rules. [ABSTRACT FROM AUTHOR]

With the development of strategic oil and gas assets, massive spatiotemporal oil and gas data have been accumulated. Application systems that assist in the storage and management of the voluminous and complex oil and gas datasets are in high demand. The voluminous and various data should be leveraged and turned into information for business decision-making and operation assistance. In this paper, we propose a set of visual analytic methods that specialize in oil and gas data; and, we develop a web-based oil and gas data management, visualization and analytical system, called Oil and Gas Visual Exploration System (OGVES). With OGVES, complex and multi-sourced oil and gas data can be stored, searched, filtered, and represented. As a web-based system, the OGVES provides more accessibility, convenience and efficiency than traditional desktop systems. Spatial scales and temporal primitives contained in oil and gas data are discussed. Different visualization methods are then presented to explore and represent spatiotemporal features of the oil and gas data. Various case studies demonstrate the usability of the system. [ABSTRACT FROM AUTHOR]

Recent advances in web development, including the introduction of HTML5, have opened a door for visualization researchers and developers to quickly access larger audiences worldwide. Open source libraries for the creation of interactive visualizations are becoming more specialized but also modular, which makes them easy to incorporate in domain-specific applications. In this context, the authors developed STRAD (Spatio-Temporal-Radar) Wheel, a web-based library that focuses on the visualization and interactive query of temporal data in a compact view with multiple temporal granularities. This article includes two application examples in urban planning to help illustrate the proposed visualization's use in practice. [ABSTRACT FROM AUTHOR]

The borehole television approach is an effective way of detecting mining-induced fractures in overburden strata as it can visualize fractures to facilitate a quantitative analysis of size, quantity, length, and other features. In this article, the borehole television approach is applied on panel 20105 of the Wangjialing Coal Mine in China to investigate the overburden movement and spatiotemporal evolution law of mining-induced fractures from the coal seam to ground surface. The results revealed that the overburden strata experienced the phases of roof caving, generation of fracture, bed separation, dislocations, fracture propagation, surface subsidence, and closing of fractures. The process can be divided into the initiation stage, the active stage, and the degradation stage along the mining direction. For exploited working faces, the caved zone height is 2.9-4.11 times the mining height, and the height of the fractured zone is 19.35-22.19 times the mining height. The height range of the three parts in the fractured zone is 24-26, 40-45, and 30-35 m. Significant fractures were observed in the bending zone. Step subsidence and cracks, which indicate severe damages, were observed on the ground surface above the goaf. [ABSTRACT FROM AUTHOR]

Eye movements serve to accumulate information from the visual world, contributing to the formation of coherent memory representations that support cognition and behavior. The hippocampus and the oculomotor network are well connected anatomically through an extensive set of polysynaptic pathways. However, the extent to which visual sampling behavior is related to functional responses in the hippocampus during encoding has not been studied directly in human neuroimaging. In the current study, participants engaged in a face processing task while brain responses were recorded with fMRI and eye movements were monitored simultaneously. The number of gaze fixations that a participant made on a given trial was correlated significantly with hippocampal activation such that more fixations were associated with stronger hippocampal activation. Similar results were also found in the fusiform face area, a face-selective perceptual processing region. Notably, the number of fixations was associated with stronger hippocampal activation when the presented faces were novel, but not when the faces were repeated. Increases in fixations during viewing of novel faces also led to larger repetition-related suppression in the hippocampus, indicating that this fixation-hippocampal relationship may reflect the ongoing development of lasting representations. Together, these results provide novel empirical support for the idea that visual exploration and hippocampal binding processes are inherently linked. [ABSTRACT FROM AUTHOR]

Cities are inherently dynamic. Interesting patterns of behavior typically manifest at several key areas of a city over multiple temporal resolutions. Studying these patterns can greatly help a variety of experts ranging from city planners and architects to human behavioral experts. Recent technological innovations have enabled the collection of enormous amounts of data that can help in these studies. However, techniques using these data sets typically focus on understanding the data in the context of the city, thus failing to capture the dynamic aspects of the city. The goal of this work is to instead understand the city in the context of multiple urban data sets. To do so, we define the concept of an “urban pulse” which captures the spatio-temporal activity in a city across multiple temporal resolutions. The prominent pulses in a city are obtained using the topology of the data sets, and are characterized as a set of beats. The beats are then used to analyze and compare different pulses. We also design a visual exploration framework that allows users to explore the pulses within and across multiple cities under different conditions. Finally, we present three case studies carried out by experts from two different domains that demonstrate the utility of our framework. [ABSTRACT FROM PUBLISHER]

This paper presents a novel approach to Linked Data exploration that uses Encyclopedic Knowledge Patterns (EKPs) as relevance criteria for selecting, organising, and visualising knowledge. EKP are discovered by mining the linking structure of Wikipedia and evaluated by means of a user-based study, which shows that they are cognitively sound as models for building entity summarisations. We implemented a tool named Aemoo that supports EKP-driven knowledge exploration and integrates data coming from heterogeneous resources, namely static and dynamic knowledge as well as text and Linked Data. Aemoo is evaluated by means of controlled, task-driven user experiments in order to assess its usability, and ability to provide relevant and serendipitous information as compared to two existing tools: Google and RelFinder. [ABSTRACT FROM AUTHOR]

• Data-driven method to explore visual social exploration • Divergent patterns of social scene exploration in the 22q11DS population compared typical peers • Abnormal pattern of social scene exploration from childhood to adulthood in 22q11DS • Abnormal pattern of social scene exploration associated with negative symptoms, anxiety level and face recognition in the 22q11DS population Current explanatory models of negative symptoms in schizophrenia have suggested the role of social cognition in symptom formation and maintenance. This study examined a core aspect of social cognition, namely social perception, and its association with clinical manifestations in 22q11.2 deletion syndrome (22q11DS), a genetic model of schizophrenia. We used an eye-tracking device to analyze developmental trajectories of complex and dynamic social scenes exploration in 58 participants with 22q11DS compared to 79 typically developing controls. Participants with 22q11DS showed divergent patterns of social scene exploration compared to healthy individuals from childhood to adulthood. We evidenced a more scattered gaze pattern and a lower number of shared gaze foci compared to healthy controls. Associations with negative symptoms, anxiety level, and face recognition were observed. Findings reveal abnormal visual exploration of complex social information from childhood to adulthood in 22q11DS. Atypical gaze patterns appear related to clinical manifestations in this syndrome. [ABSTRACT FROM AUTHOR]

Latent vectors extracted by machine learning (ML) are widely used in data exploration ( e.g ., t-SNE) but suffer from a lack of interpretability. While previous studies employed disentangled representation learning (DRL) to enable more interpretable exploration, they often overlooked the potential mismatches between the concepts of humans and the semantic dimensions learned by DRL. To address this issue, we propose Drava, a visual analytics system that supports users in 1) relating the concepts of humans with the semantic dimensions of DRL and identifying mismatches, 2) providing feedback to minimize the mismatches, and 3) obtaining data insights from concept-driven exploration. Drava provides a set of visualizations and interactions based on visual piles to help users understand and refine concepts and conduct concept-driven exploration. Meanwhile, Drava employs a concept adaptor model to fine-tune the semantic dimensions of DRL based on user refinement. The usefulness of Drava is demonstrated through application scenarios and experimental validation.

The rapid development of information and communication technologies has caused a proliferation of rich and voluminous movement data sources, which thereby necessitates further research on the analysis, modelling and visualisation of moving objects. The human performance analysis in the context of sports, based on sports-oriented movement data using geographical approaches, is an exciting new field. Yet, relatively little attention has been devoted to this topic within the GIScience domain. Therefore, this paper aims to present a research effort in this fresh field based on the movement data obtained from an entire football match. The research focuses on the exploration of match performance, an important issue in the domain of sports analytics, by utilising the Continuous Triangular Model (CTM). In general, the performance of players and teams is explored from a visualisation perspective according to the CTM diagrams of various motion attributes so that potential suggestions for performance improvements and/or tactics arrangements can be provided. More specifically, the motion attributes comprise several basic motion attributes and one composite motion attribute. The basic motion attributes include one general motion attribute (speed) that is valid for almost all kinds of moving objects, and two specific motion attributes (ball possession and territorial advantage) being particularly meaningful in football. The composite motion attribute (dominance index) is the combination of the three basic motion attributes. Among the CTM diagrams, some are generated by employing corresponding map algebra operators so as to discover extra information. The results demonstrate that the CTM approach is useful in exploring match performance and discovering important information. [ABSTRACT FROM AUTHOR]

Empirical evaluation methods for visualizations have traditionally focused on assessing the outcome of the visual analytic process as opposed to characterizing how that process unfolds. There are only a handful of methods that can be used to systematically study how people use visualizations, making it difficult for researchers to capture and characterize the subtlety of cognitive and interaction behaviors users exhibit during visual analysis. To validate and improve visualization design, it is important for researchers to be able to assess and understand how users interact with visualization systems under realistic scenarios. This article presents a methodology for modeling and evaluating the behavior of users in exploratory visual analysis. We model visual exploration using a Markov chain process comprising transitions between mental, interaction, and computational states. These states and the transitions between them can be deduced from a variety of sources, including verbal transcripts, videos and audio recordings, and log files. This model enables the evaluator to characterize the cognitive and computational processes that are essential to insight acquisition in exploratory visual analysis and reconstruct the dynamics of interaction between the user and the visualization system. We illustrate this model with two exemplar user studies, and demonstrate the qualitative and quantitative analytical tools it affords. [ABSTRACT FROM AUTHOR]

Visual exploration is an omnipresent activity in everyday life, and might represent an important determinant of visual attention deficits in patients with Alzheimer’s Disease (AD). The present study aimed at investigating visual search performance in AD patients, in particular target detection in the far periphery, in daily living scenes. Eighteen AD patients and twenty healthy controls participated in the study. They were asked to freely explore a hemispherical screen, covering ± 90°, and to respond to targets presented at 10°, 30°, and 50° eccentricity, while their eye movements were recorded. Compared to healthy controls, AD patients recognized less targets appearing in the center. No difference was found in target detection in the periphery. This pattern was confirmed by the fixation distribution analysis. These results show a neglect for the central part of the visual field for AD patients and provide new insights by mean of a search task involving a larger field of view.

Homonymous Visual Field Defects (HVFD) are common following stroke and can be highly debilitating for visual perception and higher level cognitive functions such as exploring visual scene or reading a text. Rehabilitation using oculomotor compensatory methods with automatic training over a short duration (~15 days) have been shown as efficient as longer voluntary training methods (>1 month). Here, we propose to evaluate and compare the effect of an original HVFD rehabilitation method based on a single 15 min voluntary anti-saccades task (AS) toward the blind hemifield, with automatic sensorimotor adaptation to increase AS amplitude. In order to distinguish between adaptation and training effect, fourteen left- or right-HVFD patients were exposed, one month apart, to three training, two isolated AS task (Delayed-shift & No-shift paradigm) and one combined with AS adaptation (Adaptation paradigm). A quality of life questionnaire (NEI-VFQ 25) and functional measurements (reading speed, visual exploration time in pop-out and serial tasks) as well as oculomotor measurements were assessed before and after each training. We could not demonstrate significant adaptation at the group level, but we identified a group of 9 adapted patients. While AS training itself proved to demonstrate significant functional improvements in the overall patient group , we could also demonstrate in the sub-group of adapted patients and specifically following the adaptation training, an increase of saccade amplitude during the reading task (left-HVFD patients) and the Serial exploration task, and improvement of the visual quality of life. We conclude that short-lasting AS training combined with adaptation could be implemented in rehabilitation methods of cognitive dysfunctions following HVFD. Indeed, both voluntary and automatic processes have shown interesting effects on the control of visually guided saccades in different cognitive tasks.

The diffusion of virtual reality applications dedicated to aging urges us to appraise its acceptance by target populations, especially the oldest olds. We investigated whether immersive virtual biking, and specifically a visuomotor manipulation aimed at improving visual exploration (augmented gaze), was well accepted by elders living in assisted residences. Twenty participants (mean age 89.8 years, five males) performed three 9 min virtual biking sessions pedalling on a cycle ergometer while wearing a Head-Mounted Display which immersed them inside a 360-degree pre-recorded biking video. In the second and third sessions, the relationship between horizontal head rotation and contingent visual shift was experimentally manipulated (augmented gaze), the visual shift being twice (gain = 2.0) or thrice (gain = 3.0) the amount of head rotation. User experience, motion sickness and visual exploration were measured. We found (i) very high user experience ratings, regardless of the gain; (ii) no effect of gain on motion sickness; and (iii) increased visual exploration (slope = +46%) and decreased head rotation (slope = -18%) with augmented gaze. The improvement in visual exploration capacity, coupled with the lack of intolerance signs, suggests that augmented gaze can be a valuable tool to improve the "visual usability" of certain virtual reality applications for elders, including the oldest olds.

Visual exploration is an omnipresent activity in everyday life, and might represent an important determinant of visual attention deficits in patients with Alzheimer’s Disease (AD). The present study aimed at investigating visual search performance in AD patients, in particular target detection in the far periphery, in daily living scenes. Eighteen AD patients and 20 healthy controls participated in the study. They were asked to freely explore a hemispherical screen, covering ±90°, and to respond to targets presented at 10°, 30°, and 50° eccentricity, while their eye movements were recorded. Compared to healthy controls, AD patients recognized less targets appearing in the center. No difference was found in target detection in the periphery. This pattern was confirmed by the fixation distribution analysis. These results show a neglect for the central part of the visual field for AD patients and provide new insights by mean of a search task involving a larger field of view. [ABSTRACT FROM AUTHOR]

Individuals with autism spectrum disorder (ASD) often display enhanced attention to detail and exhibit restricted behavior. However, due to a lack of comprehensive eye-movement modeling techniques, it is currently unknown whether these behavioral effects are also evident during scene viewing (i.e., detailed visual inspection and restricted visual exploration). Free-viewing eye-tracking data from observation of everyday photographic scenes were recorded during 2 experiments involving high-functioning adolescents with ASD and matched typically developing (TD) controls (Experiment 1, ASD n = 14; TD n = 22; Experiment 2, ASD n = 16; TD n = 23). Data from both experiments were combined and analyzed using 5 novel methods of eye-tracking, time-course analysis, enabling detailed characterization of viewing strategies. Participants' verbal descriptions of scenes were also assessed. Scenes either contained a centrally positioned person whose face was in full view or contained no centrally positioned face. For both types of scene, ASD participants displayed significantly less exploration of new areas over time compared with their TD peers. Analyses of scan-path length and recursion suggested a greater tendency to explore areas close to the current fixation in the ASD group, termed visual persistence. Differences were not accounted for by fixation rate. Significantly more areas within the scenes were also missing from the verbal descriptions in the ASD group. Differences were observed for both scene types, suggesting a domain-general difference rather than a specific impairment related to face processing. The observed characteristic viewing patterns may explain relative superior processing of local level information in individuals with ASD. [ABSTRACT FROM AUTHOR]

In recent years, frequent occurrences of significant air pollution events in China have routinely caused panic and are a major topic of discussion by the public and air pollution experts in government and academia. Therefore, this study proposed an efficient visualization method to represent directly, quickly, and clearly the spatio-temporal information contained in air pollution data. Data quality check and cleansing during a preliminary visual analysis is presented in tabular form, heat matrix, or line chart, upon which hypotheses can be deduced. Further visualizations were designed to verify the hypotheses and obtain useful findings. This method was tested and validated in a year-long case study of the air quality index (AQI of PM2.5) in Beijing, China. We found that PM2.5, PM10, and NO2 may be emitted by the same sources, and strong winds may accelerate the spread of pollutants. The average concentration of PM2.5 in Beijing was greater than the AQI value of 50 over the six-year study period. Furthermore, arable lands exhibited considerably higher concentrations of air pollutants than vegetation-covered areas. The findings of this study showed that our visualization method is intuitive and reliable through data quality checking and information sharing with multi-perspective air pollution graphs. This method allows the data to be easily understood by the public and inspire or aid further studies in other fields. [ABSTRACT FROM AUTHOR]

Homonymous Visual Field Defects (HVFD) are common following stroke and can be highly debilitating for visual perception and higher level cognitive functions such as exploring visual scene or reading a text. Rehabilitation using oculomotor compensatory methods with automatic training over a short duration (~15 days) have been shown as efficient as longer voluntary training methods (>1 month). Here, we propose to evaluate and compare the effect of an original HVFD rehabilitation method based on a single 15 min voluntary anti-saccades task (AS) toward the blind hemifield, with automatic sensorimotor adaptation to increase AS amplitude. In order to distinguish between adaptation and training effect, 14 left- or right-HVFD patients were exposed, 1 month apart, to three trainings, two isolated AS task (Delayed-shift and No-shift paradigm), and one combined with AS adaptation (Adaptation paradigm). A quality of life questionnaire (NEI-VFQ 25) and functional measurements (reading speed, visual exploration time in pop-out and serial tasks) as well as oculomotor measurements were assessed before and after each training. We could not demonstrate significant adaptation at the group level, but we identified a group of nine adapted patients. While AS training itself proved to demonstrate significant functional improvements in the overall patient group, we could also demonstrate in the sub-group of adapted patients and specifically following the adaptation training, an increase of saccade amplitude during the reading task (left-HVFD patients) and the Serial exploration task, and improvement of the visual quality of life. We conclude that short-lasting AS training combined with adaptation could be implemented in rehabilitation methods of cognitive dysfunctions following HVFD. Indeed, both voluntary and automatic processes have shown interesting effects on the control of visually guided saccades in different cognitive tasks. [ABSTRACT FROM AUTHOR]

Public transportation schedules are designed by agencies to optimize service quality under multiple constraints. However, real service usually deviates from the plan. Therefore, transportation analysts need to identify, compare and explain both eventual and systemic performance issues that must be addressed so that better timetables can be created. The purely statistical tools commonly used by analysts pose many difficulties due to the large number of attributes at trip- and station-level for planned and real service. Also challenging is the need for models at multiple scales to search for patterns at different times and stations, since analysts do not know exactly where or when relevant patterns might emerge and need to compute statistical summaries for multiple attributes at different granularities. To aid in this analysis, we worked in close collaboration with a transportation expert to design TR-EX, a visual exploration tool developed to identify, inspect and compare spatio-temporal patterns for planned and real transportation service. TR-EX combines two new visual encodings inspired by Marey's Train Schedule: Trips Explorer for trip-level analysis of frequency, deviation and speed; and Stops Explorer for station-level study of delay, wait time, reliability and performance deficiencies such as bunching. To tackle overplotting and to provide a robust representation for a large numbers of trips and stops at multiple scales, the system supports variable kernel bandwidths to achieve the level of detail required by users for different tasks. We justify our design decisions based on specific analysis needs of transportation analysts. We provide anecdotal evidence of the efficacy of TR-EX through a series of case studies that explore NYC subway service, which illustrate how TR-EX can be used to confirm hypotheses and derive new insights through visual exploration. [ABSTRACT FROM AUTHOR]

With the rapid urbanisation and development of three-dimensional (3D) space use, space objects in residential houses are of increasing concern. Illustrating these spatial entities within a clustered and multi-layered environment is confronted with the long-standing cognitive challenges of visual occlusion, visual clutter and visual navigation. Direct illustration as cross sections and cutaways provide valuable instruction for removing occlusion while preserving global contexts to give positioning cues. However, cross-sectioning or cutting away of the popular boundary-described models suffers from computational robustness and efficiency problems, while separated boundary geometry with surface properties prevents the efficient image-based direct illustrations from implementing a visually complete, semantically consistent practice easily. This article proposes a semantic volume texture (SVT) model for direct illustration. This true-3D raster model integrates spatial pattern embedding as well, thus avoiding the costly amendments to keep semantic consistent and visually complete during illustrative cutting and reconstructing operations. The proposed model is extended to the practical base of CityGML schema, the preparation of SVT is presented and applications imitating cross sections and cutaways are demonstrated. Experiments show that SVT-based direct illustrations are effective and efficient, making the proposed model suitable for explorative visualisations in the layered micro-scale environments. [ABSTRACT FROM AUTHOR]

The choropleth map is a device used for the display of socioeconomic data associated with an areal partition of geographic space. Cartographers emphasize the need to standardize any raw count data by an area-based total before displaying the data in a choropleth map. The standardization process converts the raw data from an absolute measure into a relative measure. However, there is recognition that the standardizing process does not enable the map reader to distinguish between low–low and high–high numerator/denominator differences. This research uses concentration-based classification schemes using Lorenz curves to address some of these issues. A test data set of nonwhite birth rate by county in North Carolina is used to demonstrate how this approach differs from traditional mean–variance-based systems such as the Jenks’ optimal classification scheme. [ABSTRACT FROM AUTHOR]

The capacity to extract information and comprehend an unseen environment is critical for mobile robots to navigate. Few surveys has mentioned the combinatorial-non-optimality problem of the traditional visual navigation methods. As computer vision technology has improved in recent years, visual navigation approaches have escalated drastically, particularly after the appearance of the CVPR Embodied AI workshop. However, few studies take these important changes into account. This survey fills this research gap by collecting, analyzing, and summarizing more than 100 recent papers. The majority of them are published within 5 years and are cited over 80 times, which provide more credible results. Based on our thorough comparison, this survey categorizes all visual navigation methods into two styles: geometry style and embodied AI style. This survey examines these two styles from the perspective of input–output. In addition, this survey attempts to provide mathematical formulations for each style. This paper provides a case study to illustrate the methodological paradigm with greatest potential. This methodological paradigm using photo-realistic simulation in the Embodied AI style, which could solve the combinatorial-non-optimality problem. Thereafter, this survey discusses several issues including pros–cons analysis, problem formulation, common framework, task generalization, dynamic environment consideration, sim-to-real, and inspiring approaches, which are all based on the scholars who have cited the method. In the last part, challenges and future trends are summarized. This survey would assist researchers who work on AI-empowered visual navigation systems. [ABSTRACT FROM AUTHOR]