Human Navigation Research Articles

A Model for Optimal Human Navigation with Stochastic Effects

We present a method for optimal path planning of human walking paths in mountainous terrain using a control theoretic formulation and a Hamilton--Jacobi--Bellman equation. Previous models for human...

Философия как антропология навигации человека. Самоопределение философа. Часть 2 (18 июня 2019 г.)

Философия как антропология навигации человека. Самоопределение философа. Часть 2 (18 июня 2019 г.)

Regression Models for Cylindrical Data in Psychology

Cylindrical data are multivariate data which consist of a directional, in this paper circular, and a linear component. Examples of cylindrical data in psychology include human navigation (direction and distance of movement), eye-tracking research (direction and length of saccades) and data from an interpersonal circumplex (location and intensity on the IPC). In this paper we adapt four models for cylindrical data to include a regression of the circular and linear component onto a set of covariates. Subsequently, we illustrate how to fit these models and interpret their results on a dataset on the interpersonal behavior of teachers.

Prediction of Driver Behaviour in Different Driving Path by using Electric Buggy Car

Each human has the capability to make decisions and respond to situations completely on its own based on their intelligence level and experience. During driving, ability makes the driver alert and know what they need to do in a certain situation. This paper aims to investigate human behaviour while driving the electric vehicle at the desired path. The electric buggy car is used and set up with equipment and sensor as an Electric Vehicle (EV). Several sensors used to collect data and certain criteria subjects are selected with the purpose to study their driving pattern. The speed, steering wheel angle, heading, and position of the buggy car is collected throughout the human navigation experiments. The behaviour of the human while driving in the straight path, turn left and turn right will be collected at the end of experiments.

INTEGRATING A LOW-COST MEMS IMU INTO A LASER-BASED SLAM FOR INDOOR MOBILE MAPPING

Abstract. Indoor mapping techniques are highly important in many applications, such as human navigation and indoor modelling. As satellite positioning systems do not work in indoor applications, several alternative navigational sensors and methods have been used to provide accurate indoor positioning for mapping purposes, such as inertial measurement units (IMUs) and simultaneous localisation and mapping algorithms (SLAM). In this paper, we investigate the benefits that the integration of a low-cost microelectromechanical system (MEMS) IMU can bring to a feature-based SLAM algorithm. Specifically, we utilize IMU data to predict the pose of our backpack indoor mobile mapping system to improve the SLAM algorithm. The experimental results show that using the proposed IMU integration method leads into a more robust data association between the measured points and the model planes. Notably, the number of points that are assigned to the model planes is increased, and the root mean square error (RMSE) of the residuals, i.e. distances between these measured points and the model planes, is decreased significantly from 1.8 cm to 1.3 cm.

Topographical Disorientation: Clinical and Theoretical Significance of Long-Lasting Improvements Following Imagery-Based Training.

Neuropsychological studies on acquired topographical disorientation have provided useful insights into the contribution of different brain regions to human navigation. However, little is known about the possibility to restore navigational skills after brain damage. Here we describe the case of No Longer Lost (NLL), a 49-year-old man who complained of severe topographical disorientation following traumatic brain injury. Extensive neuropsychological evaluation at baseline revealed selective episodic memory deficits and topographical disorientation. NLL underwent 8-week imagery-based treatment (IBT) inspired by current cognitive models of human spatial navigation. After IBT, NLL improved topographical skills and episodic memory. From a clinical point of view, the present study describes a model-based intervention for topographical disorientation. From a theoretical point of view, it provides new insights into the cognitive models of human spatial navigation and straightforward evidence about common phylogenetic roots of brain mechanisms devoted to spatial navigation and memory.

Urban Architecture: A Cognitive Neuroscience Perspective

The rapid urbanization of the past century has led to an accelerating demand for urban design that caters for city-dwellers in both physical and psychological domains. The field of architecture has begun to cultivate more analytic approaches to city design, in order to enable quantification and hypothesis-testing of design principles. In parallel, the cognitive science of human navigation has been developing rapidly, fuelled by neuroscientific findings from rodent research. The time seems ripe to bring these disciplines together. This paper reviews some of the most salient neuroscientific discoveries of recent decades and shows how these discoveries, and the design principles that emerge from them, can add important constraints on architectural design. By taking these cognitive constraints into account it is argued that urban spaces – particularly large, complex ones such as transport termini and convention centres – can be made more navigable and able to provide a better experience for users.

The Influence of Spatial Familiarity on Landmark Salience Sensibility Based on Eye Tracking

Abstract. Landmarks play an important role in navigation especially when people in the familiar and unfamiliar environment. These landmarks are usually used for expressing their spatial knowledge. This prior knowledge is related to the spatial familiarity that the spatial knowledge is acquired by individuals as a function of their experience in the environment (Gale et al., 1990). Individuals prefers to take into highly account their familiar route within the environment. Furthermore, the route descriptions include the differed types of landmarks in familiar/unfamiliar routes (Lovelace et al., 1999). The user’s level of spatial familiarity for people is a key factor of how they navigate in the environment (Savage et al., 2012). In human navigation or wayfinding, the dependency on familiar landmarks is the highest priority (Golledge, 2003).Landmarks are more distinguishably salient and prominent than the another spatial features (Sorrows and Hirtle, 1999). Therefore, the landmark salience refers to how easily this prominent spatial feature can be regarded as a landmark (Raubal and Winter, 2002). The landmark salience mainly divided into visual, structural (important location), and cognitive (semantic). Visual salience refers to the visual characteristics of spatial features such as color contrast with surroundings. Some visual characteristics include facade areas, shapes, colors and other properties. Semantic salience refers to the spatial knowledge related features such as a cultural importance of building. Structural salience refers to the spatial features play an important role in the structure of the spatial environment.The level of spatial knowledge of the individual also an important factor in landmark selection (Nuhn and Timpf, 2018). Hamburger and Röser (2014) investigated landmark semantics and showed that famous buildings were better recognized than unfamiliar ones. Quesnot and Roche (2015) found that the familiar individuals prefer the semantic landmarks in environment while the unfamiliar individuals prefer the landmark with the prominent visual and structural. In contrast, people unfamiliar with an environment prefer landmarks with outstanding visual and structural characteristics.The existing literature studies the method that landmark indicators to be considered are not comprehensive while can’t reflect on the landmark characteristics. The reason is that the method of calculation and extraction for landmarks can’t conform the individual preference needs of navigation. Compared with the traditional subjective evaluation method, the eye-tracking method provide objectivity and reliability. For example, a mobile eye tracking system was used by Kiefer et al. (2014) to investigate the process of self-localization. They discovered that test persons directed their attention longer on landmarks on the map and aligned them with objects in the surroundings during successful localization of their own position. Schwarzkopf et al. (2013) used fixed and mobile eye tracking devices to study that participates selected the navigation landmarks in the virtual environment and the real-world of the airport. In general, there are an only limited number of works related to the influence of spatial familiarity on landmark by using eye tracking method.At present, many navigation systems are providing users with route directions under different travel modes. These navigation systems have the "distance-to-turn" method in route directions, however the landmark knowledge has not been conformed to user's habits and spatial cognitive in wayfinding. In order to solve these problems, this paper aims to study the characteristics of the user’s spatial familiarity and landmark salience evaluation method in navigation. The following questions are addressed in this study: 1) What’s the relationship between the spatial familiarity with the landmark selection in navigation for individuals? 2) What is the quantitative evaluation model to connect between the spatial familiarity with the landmark salience by using the eye-tracking data?In our experiments, we will use eye tracking to explore the influence of spatial familiarity on landmark salience. The experimental path is part of the campus area of Nanjing Normal University. The subjects are divided into novice students and senior students from Nanjing Normal University as unfamiliar and familiar groups respectively. The procedures are divided into two parts, namely, the subjects should walk the experimental path within the fixation time and then they are asked to wear VR eye tracking module a Glass DK II special for HTC view (China; https://www.7invensun.com/) to do three types of tasks about landmark picture with the characteristics of visual, semantic and structural. With this research, we expect to establish a landmark salience evaluation method through eye movement data and analyse the landmark selection rules of groups with different spatial familiarity. It is helpful to design the pedestrian navigation system based on landmarks, improve the pertinence and reliability of navigation services and reduce the cognitive load for users.

Understanding landmark types in human navigation from a database perspective

Abstract. Delivering location based information to support human navigation in unfamiliar environments has been a long standing research topic in Location-based Service (LBS). Enriching route and direction communication with landmark information not only increases the efficiency of navigation applications but also facilitates spatial knowledge learning among users (Lovelace et al. 1999, Wunderlich & Gramann 2018). Landmarks are salient geographic entities anchored at fixed positions in an environment. In wayfinding and navigation, landmarks serve for multiple purposes as and organizing concept that help people build up structured spatial knowledge representation and as navigational tools to support navigational decision-making (Sorrows & Hirtle 1999). In previous work, landmarks are often conceptualized as reference points. However, in Geodatabase landmarks are not always represented as point features. Moreover, non-point features are also important references in route instructions, especially for pedestrian navigation. The signs, though often considered as point elements not only provide something about “here”, but also directional information (Gibson 2009). In this work, we attempt to provide a categorization of landmark for route communications from a database perspective. The proposed view is intended to be extended to a landmark model to generate various route communication, such as verbal instructions or symbolic visualizations.

Visual-Inertial Odometer-Based Global High Precision Indoor Human Navigation in a University Library

Abstract. AR (Augmented Reality) could be realized as a basic and high-level function on latest smartphones with a reasonable price. AR enables users to experience consistent three-dimensional (3D) spaces co-existing with 3D real and virtual objects with sensing real 3D environments and reconstructing them in the virtual world through a camera. The accuracy of sensing real 3D environments using an AR function, that is, visual-inertial odometer, of a smartphone is extremely higher than one of a GPS receiver on it, and can be less than one centimeter. However, current common AR applications generally focus on “small” real 3D spaces, not large real 3D spaces. In other words, most of the current AR applications are not designed for uses based on a geographic coordinate system.We proposed a global extension of the visual-inertial odometer with an image recognition function of geo-referenced image markers installed in real 3D spaces. Examples of geo-referenced image markers can be generated from analog guide boards existing in the real world. We tested this framework of a global extension of the visual-inertial odometer embedded in a smartphone on the first floor in the central library of Akita University. The geo-referenced image markers such as floor map boards and book categories sign boards were registered in a database of 3D geo-referenced real-world scene images. Our prototype system developed on a smartphone, that is, iPhone XS, Apple Inc., could first recognized a floor map board (Fig. 1), and could determine the 3D precise distance and direction of the smartphone from the central position of the floor map board in a local 3D coordinate space with the origin point as the central positon of the board. Then, the system could convert the relative precise position and the relative direction of the smartphone’s camera in a local coordinate space into a global precise location and orientation of it. A subject was walking the first floor in the building of the library with a world tracking function of the smartphone. The experimental result shows that the error of tracking a real 3D space of a global coordinate system was accumulated, but not bad. The accumulated error was only about 30 centimeters after the subject’s walking about 30 meters (Fig. 2). We are now planning to improve our prototype system in the accuracy of indoor navigation with calibrating the location and orientation of a smartphone based sequential recognitions of multiple referenced scene image markers which have already existed for a general user services of the library before developing this proposed new services. As the conclusion, the experiment’s result of testing our prototype system was impressive, we are now preparing a more practical high-precision LBS which enables a user to be navigated to the exact location of a book of a user’s interest in a bookshelf on a floor with AR and floor map interfaces.

A meta-analysis of sex differences in human navigation skills.

There are inconsistent reports regarding behavioral sex differences in the human navigation literature. This meta-analysis quantifies the overall magnitude of sex differences in large-scale navigation skills in a variety of paradigms and populations, and examines potential moderators, using 694 effect sizes from 266 studies and a multilevel analytic approach. Overall, male participants outperform female participants, with a small to medium effect size (d = 0.34 to 0.38). The type of task, the type of dependent variable and the testing environment significantly contribute to variability in effect sizes, although there are only a few situations in which differences are either nonexistent or very large. Pointing and recall tasks (and the deviation scores associated with them) show larger sex differences than distance estimation tasks or learning to criterion. Studies with children younger than 13 years showed much smaller effect sizes (d = .15) than older age groups. We discuss the implications of these findings for understanding sex differences in human spatial navigation and identify avenues for future navigation research.

Indoor INS UWB-based human localization with missing data utilizing predictive UFIR filtering

A combined algorithm for the loosely fused ultra wide band &#x0028 UWB &#x0029 and inertial navigation system &#x0028 INS &#x0029 -based measurements is designed under the indoor human navigation conditions with missing data. The scheme proposed fuses the INS- and UWB-derived positions via a data fusion filter. Since the UWB signal is prone to drift in indoor environments and its outage highly affects the integrated scheme reliability, we also consider the missing data problem in UWB measurements. To overcome this problem, the loosely-coupled INS &#x002F UWB-integrated scheme is augmented with a prediction option based on the predictive unbiased finite impulse response &#x0028 UFIR &#x0029 fusion filter. We show experimentally that, the standard UFIR fusion filter has higher robustness than the Kalman filter. It is also shown that the predictive UFIR fusion filter is able to produce an acceptable navigation accuracy under temporary missing UWB-data.

High-resolution, real-time, and nonfluoroscopic 3-dimensional cardiac imaging and catheter navigation in humans using a novel dielectric-based system

Catheter navigation and 3-dimensional (3D) cardiac mapping are essential components of minimally invasive electrophysiological procedures. The purpose of this study was to develop a novel 3D mapping system (KODEX - EPD, EPD Solutions, Best, The Netherlands) that measures changing electric field gradients induced on intracardiac electrodes to enable catheter localization and real-time 3D cardiac mapping. We first validated the accuracy of the system's measurement and localization capabilities by comparing known and KODEX - EPD-measured distances and locations at 12 anatomical landmarks in both the atria and ventricles of 4 swine. Next, invivo images of 3D porcine cardiac anatomy generated by KODEX - EPD and widely used CARTO 3 system (Biosense Webster, Inc., Diamond Bar, CA) were compared with gold standard computed tomography images acquired from the same animals. Finally, 3D maps of atrial anatomy were created for 22 patients with paroxysmal atrial fibrillation (Dielectric Unravelling of Radiofrequency ABLation Effectiveness trial). First, the mean error between known and measured distances was 1.08 ± 0.11 mm (P < .01) and the overall standard deviation between known and measured locations in 12 areas of the porcine heart was 0.35 mm (P < .01). Second, an expert comparison of 3D image quality revealed that KODEX - EPD is noninferior to CARTO 3. Third, the system enabled 3D imaging of atrial anatomy in humans, provided real-time images of atrioventricular valves, and detected important anatomical variations in a subset of patients. The KODEX - EPD system is a novel 3D mapping system that accurately detects catheter location and can generate high-resolution images without the need for preacquired imaging, specialty catheters, or a point-by-point mapping procedure.

An indoor navigation model and its network extraction

We propose a navigation model for indoor environments that combines a 3D geometric modeling of buildings with connection properties of spaces and semantic elements such as openings and installations. The model is an extension of the IndoorGML standard navigation module with a twofold benefit: the extension facilitated the data import from the international standard CityGML and introduced the semantics of various fixtures in indoor space of buildings making the navigation model more suitable for human needs. Several experiments have been conducted by extracting networks from CityGML data and performing a comparison with other network construction techniques. The second contribution of the paper is an algorithm for the automatic extraction of the navigation network. Such an algorithm is a hybrid solution between medial axis approaches and visibility graph approaches. Normally, medial axes approaches are a good representation of human navigation in narrow corridors, especially to avoid obstacles, but introduce distortions in open space. On the other hand, visibility approaches work better in open spaces. In our extraction technique, the resulting network takes advantages of both approaches and better mimics human beings’ navigation in indoor environments.

Retrosplenial Cortical Representations of Space and Future Goal Locations Develop with Learning

Recent findings suggest that long-term spatial and contextual memories depend on the retrosplenial cortex (RSC) [1-5]. RSC damage impairs navigation in humans and rodents [6-8], and the RSC is closely interconnected with brain regions known to play a role in navigation, including the hippocampus and anterior thalamus [9, 10]. Navigation-related neural activity is seen in humans [11] and rodents, including spatially localized firing [12, 13], directional firing [12, 14, 15], and responses to navigational cues [16]. RSC neuronal activity is modulated by allocentric, egocentric, and route-centered spatial reference frames [17, 18], consistent with an RSC role in integrating different kinds of navigational information [19]. However, the relationship between RSC firing patterns and spatial memory remains largely unexplored, as previous physiology studies have not employed behavioral tasks with a clear memory demand. To address this, we trained rats on a continuous T-maze alternation task and examined RSC firing patterns throughout learning. We found that the RSC developed a distributed population-level representation of the rat's spatial location and current trajectory to the goal as the rats learned. After the rats reached peak performance, RSC firing patterns began to represent the upcoming goal location as the rats approached the choice point. These neural simulations of the goal emerged at the same time that lesions impaired alternation performance, suggesting that the RSC gradually acquired task representations that contribute to navigational decision-making.

Finding our way Supernavigators David Barrie The Experiment, 2019. 321 pp. Wayfinding M. R. O'Connor St. Martin's Press, 2019. 366 pp.

Rediscovering the joy of the journey in the age of turn-by-turn directions

Spatial Navigation in Rats and Humans: A Neuropsychological Perspective

AbstractBackground and objective:In a landmark publication, O’Keefe &amp; Dostrovsky (1971) presented a model for spatial navigation in the rat, the cognitive map theory. In this theory they proposed that the processing and storage of spatial information for spatial navigation takes place in the hippocampus. The theory was extended to include the contribution of the grid cells in the medial entorhinal cortex (Hafting et al. 2005). The cognitive map theory has been widely applied to spatial navigation in humans as well as rats. In this paper, an alternative theory is proposed in which spatial processing takes place in the right parieto-temporo-occipital area in humans, and that damage to this area causes a fragmentation in the sense of space, affecting the recall of both visual and tactile spatial information.Method:A group of eight subjects with damage to the right parieto-temporo-occipital area and a fragmented sense of space was assessed on tests of spatial navigation and memory and the results were compared with a group of patients with damage to the right hippocampus. Other comparison groups included left and right hemisphere subjects with normal spatial functioning.Results:The results suggest that, in the human, damage to the right parieto-temporo-occipital area causes a fragmentation in the sense of space, as well as an impaired memory for spatial material in both the visual and tactile modalities. These results support a model of spatial navigation in which the integrity of the right parieto-temporo-occipital area, and not the right hippocampus, is a necessary condition for the processing of spatial information in humans. An alternative explanation for the functioning of the right hippocampus is also presented.

Self-Driving Car with Cloud Computing

The research goal to build a monocular vision self-driving car with cloud computing prototype using Raspberry Pi as a processing chip. An HD camera along with an ultrasonic sensor is used to provide necessary data from the real world to the car. The car is capable of reaching the given destination safely and intelligently thus avoiding the risk of human errors. Many algorithms like object detection, action/correction detection are combined together to provide the necessary control to the car. Technological advances are pacing in an unmanageable manner. The more a nation invests in upcoming technologies, the more incentives it enjoys in the long run. These incentives branch from finer infrastructure to augmentation of the jobs sector. The prime focus of technology nowadays is to moderate the human intervention in day-to-day tasks. Machine Learning, Artificial Intelligence, and Computer Vision being the big players in doing so. We have reached a time wherein we can automate our menial tasks like driving. Insane, right? This segment of Computer Vision is popularly known as Autonomous Cars. These are vehicles that perceive their environment and move without human navigation. Self-driving cars to become more reliant on indestructible inbuilt road sensors that are embedded in the road and ‘turned on’ during preventive road construction maintenance or in emergencies alerting each vehicle to reduce speed or halt. Ultimately, we can expect the road beneath us to become more communicative with self-driving cars. It will not be long until self-driving cars by default will likely keep an activity log for service and debugging. Privacy, of course, will be an issue, whether it is the insurance company, the car maker, a local dealer, or even police authorities all seeking another means to track our every coming and going. Crucial components of the future will be the mobile networks, ad hoc (car to car) networks, vehicles to and from road sensors, and satellite communications.

Incidental learning of allocentric and egocentric strategies by both men and women in a dual-strategy virtual Morris Water Maze.

In studies of human navigation, an underlying assumption is that, by nature, navigators are proficient with and strongly biased toward using only one strategy, either allocentric (cognitive mapping) or egocentric (stimulus-response based). Further, research often suggests that males are allocentric navigators whereas females are egocentric navigators. We tested these binary assumptions using two versions of a virtual Morris water maze (MWM). The Dual-strategy maze could be solved using either an allocentric or an egocentric strategy. Preferred strategy was tested by alternating test and probe trials. Two "Forced-strategy" probe trials tested navigators' ability to use their non-preferred strategy. Participants then completed the Place maze that was best solved using an allocentric strategy. In the Dual-strategy maze, there was no particular order of acquisition of a preferred strategy and a quarter of participants switched strategies; this switching was bilateral (from egocentric to allocentric and vice-versa). Navigators were most competent in the use of their preferred strategy. Importantly, navigators did learn (incidentally) information related to their non-preferred strategy and were capable of using that strategy. This pattern of results was shown for both males and females, although females did show a stronger preference for egocentric navigation than did males. We concluded that navigators can use all environmental information available to them and that the tendency to view people as innately allocentric or egocentric navigators does not allow for more nuanced investigations of navigational ability. Such investigations would better inform research into deficits in spatial ability in clinical populations.

How does navigation system behavior influence human behavior?

Navigation systems are ubiquitous tools to assist wayfinders of the mobile information society with various navigational tasks. Whenever such systems assist with self-localization and path planning, they reduce human effort for navigating. Automated navigation assistance benefits navigation performance, but research seems to show that it negatively affects attention to environment properties, spatial knowledge acquisition, and retention of spatial information. Very little is known about how to design navigation systems for pedestrian navigation that increase both navigation performance and spatial knowledge acquisition. To this end, we empirically tested participants (N = 64) using four different navigation system behaviors (between-subject design). Two cognitive processes with varying levels of automation, self-localization and allocation of attention, define navigation system behaviors: either the system automatically executes one of the processes (high level of automation), or the system leaves the decision of when and where to execute the process to the navigator (low level of automation). In two experimental phases, we applied a novel empirical framework for evaluating spatial knowledge acquisition in a real-world outdoor urban environment. First, participants followed a route assisted by a navigation system and, simultaneously, incidentally acquired spatial knowledge. Second, participants reversed the route using the spatial knowledge acquired during the assisted phase, this time without the aid of the navigation system. Results of the route-following phase did not reveal differences in navigation performance across groups using different navigation system behaviors. However, participants using systems with higher levels of automation seemed not to acquire enough spatial knowledge to reverse the route without navigation errors. Furthermore, employing novel methods to analyze mobile eye tracking data revealed distinct patterns of human gaze behavior over time and space. We thus can demonstrate how to increase spatial knowledge acquisition without harming navigation performance when using navigation systems, and how to influence human navigation behavior with varying navigation system behavior. Thus, we provide key findings for the design of intelligent automated navigation systems in real-world scenarios.