Landmark Navigation Research Articles

Learning to use landmarks for navigation amplifies their representation in retrosplenial cortex.

Visual landmarks provide powerful reference signals for efficient navigation by altering the activity of spatially tuned neurons, such as place cells, head direction cells, and grid cells. To understand the neural mechanism by which landmarks exert such strong influence, it is necessary to identify how these visual features gain spatial meaning. In this study, we characterized visual landmark representations in mouse retrosplenial cortex (RSC) using chronic two-photon imaging of the same neuronal ensembles over the course of spatial learning. We found a pronounced increase in landmark-referenced activity in RSC neurons that, once established, remained stable across days. Changing behavioral context by uncoupling treadmill motion from visual feedback systematically altered neuronal responses associated with the coherence between visual scene flow speed and self-motion. To explore potential underlying mechanisms, we modeled how burst firing, mediated by supralinear somatodendritic interactions, could efficiently mediate context- and coherence-dependent integration of landmark information. Our results show that visual encoding shifts to landmark-referenced and context-dependent codes as these cues take on spatial meaning during learning.

Crater Triangle Matching Algorithm Based on Fused Geometric and Regional Features

Craters are regarded as significant navigation landmarks during the descent and landing process in small body exploration missions for their universality. Recognizing and matching craters is a crucial prerequisite for visual and LIDAR-based navigation tasks. Compared to traditional algorithms, deep learning-based crater detection algorithms can achieve a higher recognition rate. However, matching crater detection results under various image transformations still poses challenges. To address the problem, a composite feature-matching algorithm that combines geometric descriptors and region descriptors (extracting normalized region pixel gradient features as feature vectors) is proposed. First, the geometric configuration map is constructed based on the crater detection results. Then, geometric descriptors and region descriptors are established within each feature primitive of the map. Subsequently, taking the salience of geometric features into consideration, composite feature descriptors with scale, rotation, and illumination invariance are generated through fusion geometric and region descriptors. Finally, descriptor matching is accomplished by computing the relative distances between descriptors and adhering to the nearest neighbor principle. Experimental results show that the composite feature descriptor proposed in this paper has better matching performance than only using shape descriptors or region descriptors, and can achieve a more than 90% correct matching rate, which can provide technical support for the small body visual navigation task.

Label-free, whole-brain in vivo mapping in an adult vertebrate with third harmonic generation microscopy.

Comprehensive understanding of interconnected networks within the brain requires access to high resolution information within large field of views and over time. Currently, methods that enable mapping structural changes of the entire brain in vivo are extremely limited. Third harmonic generation (THG) can resolve myelinated structures, blood vessels, and cell bodies throughout the brain without the need for any exogenous labeling. Together with deep penetration of long wavelengths, this enables in vivo brain-mapping of large fractions of the brain in small animals and over time. Here, we demonstrate that THG microscopy allows non-invasive label-free mapping of the entire brain of an adult vertebrate, Danionella dracula, which is a miniature species of cyprinid fish. We show this capability in multiple brain regions and in particular the identification of major commissural fiber bundles in the midbrain and the hindbrain. These features provide readily discernable landmarks for navigation and identification of regional-specific neuronal groups and even single neurons during in vivo experiments. We further show how this label-free technique can easily be coupled with fluorescence microscopy and used as a comparative tool for studies of other species with similar body features to Danionella, such as zebrafish (Danio rerio) and tetras (Trochilocharax ornatus). This new evidence, building on previous studies, demonstrates how small size and relative transparency, combined with the unique capabilities of THG microscopy, can enable label-free access to the entire adult vertebrate brain.

Accurate semantic segmentation of small-body craters for navigation

Feature extraction and matching play integral roles in autonomous vision navigation. As a typical morphological feature, craters can be used as navigation landmarks. However, the spin of small bodies introduces substantial variations in illumination and viewing angles within acquired images, thereby posing challenges to feature extraction and matching algorithms. Addressing these challenges, a deep learning-based approach emerges as pivotal in mitigating the limitations of existing navigation algorithms. This paper presents an algorithm designed to segment noteworthy craters and irregular regions on such small bodies, thereby furnishing crucial autonomous navigation data for missions involving uncharted terrains. By seamlessly integrating deep learning techniques with conventional computer vision-based feature description methods, the algorithm achieves matching segmentation results of noteworthy precision. The experimental results show that the algorithm has better results compared to the existing mainstream segmentation networks. And it can achieve the accurate matching of segmentation results.

The bearings and distances isolines combinations of the navigational landmarks pair for the vessel fixed position determination

The issues of applying basic navigation parameters (bearing and distance), and their isolines traditionally used in navigation practice in coastal waters are studied in the paper. A new distinctive feature is the use of isolines combinations (three distance arcs, two isoazimuths, a hyperbola and an ellipse) based on measured bearings and distances to the pair of landmarks to obtain the vessel fixed positions with high precision. The traditional navigation methods (the theory of isolines, the generalized method of lines position, the method of least squares) are involved as a mathematical framework. The mathematical argumentation and the isolines characteristics of bearings and distances, their combinations (difference and sum of distances, difference of bearings) are given. The solutions of the surplus equations of the lines position under random error influence by the least squares method and the accuracy assessment of the vessel fix position by the radial error including the reduced variant to compensate the systematic errors are suggested. The principal points of the research are supported by the graphic interpretation, and the given expressions are adjusted to the practical application and navigation systems software development. The computer simulation that shows the radial error of the fixed position by seven position lines is at least two times less than by any pair of position lines has been carried out. The formalization of the proposed methods in the automatic navigation systems or in autonomous ship control systems will enable the navigator on board or at operating the vessel remotely to solve the problems of the navigation information processing in coastal and congested waters on a new level.

Moorea lagoon fishers’ mental maps: An exploratory analysis of Polynesian spatial knowledge

AbstractThis empirical study builds upon prior research concerning cultural influences on spatial mental representations in Oceania. A comprehensive examination of 93 mental maps sourced from 59 lagoon fishers of Moorea (French Polynesia) reveals interesting facts about the way they organize and share their spatial knowledge. Firstly, consistent with previous studies across Oceania, Polynesian fishers exhibit a preference for the allocentric perspective when representing their environment. Secondly, they generally rely on marine landmarks for navigation, with a particular emphasis on four entities: the reef barrier, maritime beacons, coral outcrops, and a key chromatic marker — Moana (blue in Tahitian) — indicating the depth of the lagoon. Finally, the factor analysis we conducted highlights two significant facts: (1) a geographical self‐censorship, demonstrated by the low presence or even the absence of landmarks useful for locating their fishing spots; (2) a continuum between the surface and the depths of the lagoon, showing that surface fishers (line, net, troll, etc.) have a proven knowledge of seabed topography, whereas underwater speargun fishers also rely on landmarks located above the water.

Real-Time Road Intersection Detection in Sparse Point Cloud Based on Augmented Viewpoints Beam Model.

Road intersection is a kind of important navigation landmark, while existing detection methods exhibit clear limitations in terms of their robustness and efficiency. A real-time algorithm for road intersection detection and location in large-scale sparse point clouds is proposed in this paper. Different from traditional approaches, our method establishes the augmented viewpoints beam model to perceive the road bifurcation structure. Explicitly, the spatial features from point clouds are jointly extracted in various viewpoints in front of the robot. In addition, the evaluation metrics are designed to self-assess the quality of detection results, enabling our method to optimize the detection process in real time. Considering the scarcity of datasets for intersection detection, we also collect and annotate a VLP-16 point cloud dataset specifically for intersections, called NCP-Intersection. Quantitative and qualitative experiments demonstrate that the proposed method performs favorably against the other parallel methods. Specifically, our method performs an average precision exceeding 90% and an average processing time of approximately 88 ms/frame.

Stereophotoclinometry for OSIRIS-REx Spacecraft Navigation

We summarize a decade of effort by the Origins, Spectral Interpretation, Resource Identification, and Security–Regolith Explorer (OSIRIS-REx) mission team to build up the unique capabilities, processes, and procedures required to accomplish the unprecedented navigation performance required during proximity operations at asteroid (101955) Bennu. Stereophotoclinometry was a key technology used for digital terrain model (DTM) generation and landmark navigation, enabling estimation of spacecraft trajectories and Bennu’s geophysical parameters. We outline the concept of operations for OSIRIS-REx landmark navigation and the wide array of testing and verification efforts leading up to OSIRIS-REx’s arrival at Bennu. We relate the outcome of these efforts to the experiences during proximity operations. We discuss navigation and DTM performance during operations, including detailed lessons learned to carry forward for future missions.

Design and evaluation of radiation disaster prevention map based on evacuation behavior

An effective disaster prevention map ensures public safety and efficient evacuation during emergencies. Emergency evacuation information design in Taiwan is in its nascent stages. This study focuses on individuals' understanding, behavior, and decision-making during disaster prevention to devise suitable disaster prevention map norms. We examined the Emergency Planning Zone's existing disaster prevention methods and surveyed the community to understand their disaster prevention concepts and needs. We conducted two experiments: the first tested the comprehension of existing disaster prevention maps and identified their issues, while the second evaluated a redesigned map based on Experiment 1's findings. We discovered that all age groups agree on needing accurate, fast information and diverse evacuation route options. Experiment 1 revealed disproportionate assembly point icons on the existing map, leading to navigation difficulties. The map also failed to mark landmarks, road names, and blocked intersections accurately. The redesigned map in Experiment 2 addressed these issues, showing that improving map information design aids recognition and memory, and bridges wayfinding behavior gaps in people with different spatial abilities. We suggest marking evacuation routes on maps, placing corresponding signs on-site, and locating assembly points near landmarks for easier navigation.

Positioning an electric wheelchair in 2D grid map based on natural landmarks for navigation using Q-learning

Self-mobility electric wheelchairs are very useful for people with disabilities, so they can move without help in indoor environments. To create one selfmobility electric wheelchair, modern methods for control such as computer vision and machine learning can be applied. In particular, this electric wheelchair can move from any position in the indoor environment to the desired destination. For accuracy, natural landmarks are used and the navigation of the wheelchair is determined using a Q-learning reinforcement learning algorithm. In particular, this algorithm is applied to find the best path for the wheelchair to reach the destination. The article proposes a method to build one 2D grid map for wheelchair movement based on natural landmarks in the indoor environment. The new point of this method is that the position of the wheelchair can be accurately determined from a certain landmark instead of many landmarks applied in traditional methods. Some practical experiments were performed to illustrate the effectiveness of the proposed method in the indoor environment. This proposed method can be developed in more complex environments with natural landmarks.

A technical vision system for controlling the movement of a quadcopter

In this paper, based on the analysis of possible options for the autonomous movement of a quadcopter along a predetermined trajectory, the combination of a technical vision system (TVS) and IMU is proposed. Flying through a system of navigation landmarks was applied to solve navigation tasks during autonomous movement along a predetermined route using TVS. There are several approaches to implementing TVS to control UAV movement, depending on the accepted landmark system and the features of the route definition. For the previous description and further detection of navigation landmarks in the TVS image, the application of feature extraction methods is proposed, as it enables the selection of objects of different structures, including natural landmarks. A block diagram of a quadcopter control system with the application of TVS is developed. A methodology for calculating the main parameters of the TVS units is proposed, based on the specified requirements for the accuracy of the quadcopter's heading determination by navigation landmarks. The paper also proposes an algorithm for controlling the movement of a quadcopter using a system of visual navigation landmarks which define the flight route. After finding the image of the next navigation landmark in the TVS frame according to the selected feature system, its center and position in the frame relative to the quadcopter's heading are determined. The obtained data is used to determine and change the direction of the movement of the quadcopter by changing the yaw angle, which is transmitted to the control system. The suggested approaches may be useful in solving various problems of using TVS for controlling UAV movement.

Landmark-based spatial navigation across the human lifespan.

Human spatial cognition has been mainly characterized in terms of egocentric (body-centered) and allocentric (world-centered) wayfinding bhavior. It was hypothesized that allocentric spatial coding, as a special high-level cognitive ability, develops later and deteriorates earlier than the egocentric one throughout lifetime. We challenged this hypothesis by testing the use of landmarks versus geometric cues in a cohort of 96 deeply phenotyped participants, who physically navigated an equiangular Y maze, surrounded by landmarks or an anisotropic one. The results show that an apparent allocentric deficit in children and aged navigators is caused specifically by difficulties in using landmarks for navigation while introducing a geometric polarization of space made these participants as efficient allocentric navigators as young adults. This finding suggests that allocentric behavior relies on two dissociable sensory processing systems that are differentially affected by human aging. Whereas landmark processing follows an inverted-U dependence on age, spatial geometry processing is conserved, highlighting its potential in improving navigation performance across the lifespan.

Exploiting Environmental Information Using HsMMs for Smartphone User Tracking

The extensive deployment of wireless infrastructure provides alternative low-cost methods for location awareness of mobile phone users (MPUs) in indoor environments by processing the received signal strength (RSS) of the mobile phone. In such a signal-processing framework, hidden Markov models (HMMs) are often used to model the uncertainties of RSS data and incorporate environmental information into localization. Since hidden semi-Markov models (HsMMs) outperform HMMs in their ability to model state duration more flexibly, employing HsMMs for indoor user positioning is a promising research direction. In this aspect, a user’s personal preference for staying in a particular area, and the functionality of certain areas, such as a dining room, as well as navigation landmarks, can be utilized in the HsMM to assist localization. This article proposes an online HsMM forward recursion (HsMM-FR) algorithm to incorporate this information for real-time smartphone user tracking. We apply the proposed HsMM-FR algorithm to simulated, synthesized, and real RSS datasets in typical indoor environments for validation.

Utility of Environmental Complexity as a Predictor of Alzheimer’s Disease Diagnosis: A Big-Data Machine Learning Approach

BackgroundRural-urban differences and spatial navigation deficits have received much attention in Alzheimer’s Disease research. While individual environmental and neighborhood factors have been independently investigated, their integrative, multifactorial effects on Alzheimer’s diagnosis have not. Here we explore this “environmental complexity” for predictive power in classifying Alzheimer’s from cognitively-normal status.MethodsWe utilized data from the National Alzheimer’s Coordinating Center (NACC) uniform data set containing annual visits since 2005 and selected individuals with multiple visits and who remained in their zipcode (N = 22,553). We georeferenced each subject with 3-digit zipcodes of their residences since entering the program. We calculated environmental complexity measures using geospatial tools from street networks and landmarks for spatial navigation in subjects’ zipcode zones. Zipcode zones were grouped into two cognitive classes (Cognitively-Normal and Alzheimer’s-inclined) based on the ratios of AD and dementia subjects to all subjects in an individual zipcode zone. We randomly selected 80% of the data to train a neural network classifier model on environmental complexity measures to predict the cognitive class for each zone, controlling for salient demographic variables. The remaining 20% served as the test set for performance evaluation.ResultsOur proposed model reached excellent classification ability on the testing data: 83.87% accuracy, 95.23% precision, 83.33% recall, and 0.8889 F1-score (F1-score=1 for perfect prediction). The most salient features of “Alzheimer’s-inclined” zipcode zones included longer street-length average, higher circuity, and slightly fewer points of interest. Most “cognitively-normal” zipcode zones appeared in or near urban areas with high environmental complexity measures.ConclusionEnvironmental complexity, reflected in frequency and density of street networks and landmarks features, predicted with high precision the cognitive status of 3-digit zipcode zones based on the etiologic diagnoses and observed cognitive impairment of NACC subjects residing in these zones. The zipcode zones vary widely in size (1.6 km2 to 35,241 km2), and large zipcode zones suffer high spatial heterogeneity. Other proven AD risk factors, such as PM2.5, disperse across zones, and so do individual’s activities, leading to spatial uncertainty. Nevertheless, the model classifies diagnosis well, establishing the need for prospective experiments to quantify effects of environmental complexity on Alzheimer’s development.

Investigating the Effects of Computer-Generated Contextual Landmarks on Short-Term Recall of E-Texts

E-texts have many advantages over their paper counterparts, especially when they are reflowable and available as open educational resources (OERs). Unfortunately, research suggests that e-texts are, on the whole, less memorable than p-texts, in part due to their relative lack of visual navigational landmarks that help to anchor recall. The Landmarks project team is, therefore, building an application that inserts computer-generated artificial imperfections – abstract or representational landmarks – into the display of e-texts, that remain consistently associated with text passages even when documents are reflowed or reformatted. We hypothesize that it may consequently be easier to recall the associated contents. The application is designed to provide the means to present modified open texts using a range of generated landmarks and variations on them, and to test recall of the content. In this initial pilot study, results of tests for readers receiving different landmarks will be compared, with the intent of identifying promising approaches to use for future studies.

Investigating the Effects of Computer-Generated Contextual Landmarks on Short-Term Recall of E-Texts

E-texts have many advantages over their paper counterparts, especially when they are reflowable and available as open educational resources (OERs). Unfortunately, research suggests that e-texts are, on the whole, less memorable than p-texts, in part due to their relative lack of visual navigational landmarks that help to anchor recall. The Landmarks project team is, therefore, building an application that inserts computer-generated artificial imperfections – abstract or representational landmarks – into the display of e-texts, that remain consistently associated with text passages even when documents are reflowed or reformatted. We hypothesize that it may consequently be easier to recall the associated contents. The application is designed to provide the means to present modified open texts using a range of generated landmarks and variations on them, and to test recall of the content. In this initial pilot study, results of tests for readers receiving different landmarks will be compared, with the intent of identifying promising approaches to use for future studies.

Optimal crater landmark selection based on optical navigation performance factors for planetary landing

Planetary craters are natural navigation landmarks that widely exist and are easily observed. Optical navigation based on crater landmarks has become an important autonomous navigation method for planetary landing. Due to the increase in observed crater landmarks and the limitation of onboard computation, the selection of good crater landmarks has gradually become a research hotspot in the field of landmark-based optical navigation. This paper designs a fast crater landmark selection method, which not only considers the configuration observability of crater subsets but also focuses on the influence on navigation performance arising from the measurement uncertainty and the matching confidence of craters, which is different from other landmark selection methods. The factor of measurement uncertainty, which is anisotropic, correlated and nonidentically distributed, is quantified and integrated into selection based on crater pairing detection and localization error evaluation. In addition, the concept of the crater matching confidence factor is introduced, which reflects the possibility of 2D projection measurements corresponding to 3D positions. Combined with the configuration observability factor, the crater landmark selection indicator is formed. Finally, the effectiveness of the proposed method is verified by Monte Carlo simulations.

Desert grassland scorpions might navigate by touch and taste

Since the advent of satellites and Google Maps, most of us just plug in to find our way home. But not the intrepid birds, bees and ants that negotiate the planet. They depend on more traditional means of navigation, orienting by the stars, sun and local views. But Douglas Gaffin from the University of Oklahoma, USA, suspects that scorpions may use another strategy. The arachnids are equipped with a pair of touch- and taste-sensitive brushes, known as pectines, located beneath their abdomens, which can rub along the ground as the animals tour around. Could the fearsome creatures be tasting the ground and reading the surface by touch with their pectines to navigate home? To find out, Gaffin with colleagues Maria Muñoz and Mariëlle Hoefnagels (both from the University of Oklahoma) visited the University of New Mexico Sevilleta Field Station to gather desert grassland scorpions (Paruroctonus utahensis) to bring back to the lab, to find out how the animals navigate when they have to depend on their pectines alone.‘We tested various combinations of sand, soil and arena configurations before we settled on our final design of water heater drainage pans surrounded with blackout curtains to prevent the scorpions from seeing landmarks for visual navigation’, says Gaffin, who also provided the scorpions with a mound of sand to burrow in. Then, the team glued a small crystal to the back of each animal that would reflect light during the day and infrared light at night, to allow the team to film and keep track of each individual as it ambled around an enclosure after dark. If the animals were able to navigate using their pectines alone for guidance, Gaffin reasoned that the scorpions would circulate in ever expanding circles around their new homes to define the lie of the land to build a map of their surroundings for later use.Collecting 1500 h of movies as 23 scorpions constructed burrows in the mound of sand and in a featureless flat arena, Gaffin, Muñoz and Hoefnagels saw 18 of the arachnids emerge from their new burrows and begin exploring the region around their home, looping and completing up to 10 exploratory circuits. The scorpions appeared to be performing learning walks, just like navigating ants that depend on local knowledge, learned when they first emerge from their home and circulate around their burrow. The scorpions were probably gleaning information about their surroundings from their touch- and taste-sensitive pectines alone, as they were unable to see any landmarks. And, when the trio cunningly rotated the scorpions’ arenas – first by 90 deg, and then a day later by a further 90 deg – the pioneering arachnids successfully found their repositioned burrows, instead of sticking stubbornly to exploring the burrows' original location. They were tasting and reading the feel of the sand with their pectines to return continually to their burrow homes, even though the homes had moved by ∼15 cm when the scientists rotated the desert arenas.So, the desert scorpions’ pectines appear to provide the animals with their sense of place, but that does not rule out the possibility that the arachnids also depend on vision for navigation when provided with a more informative view. And Gaffin suspects that scorpions also use their own internally calibrated odometer to keep track of their location relative to home when they venture out, once they have a sense of the lie of the land.

Development of Landmark Use for Navigation in Children: Effects of Age, Sex, Working Memory and Landmark Type

The use of landmarks for navigation develops throughout childhood. Here, we examined the developmental trajectory of egocentric and allocentric navigation based on landmark information in an on-screen virtual environment in 39 5–6-year-olds, 43 7–8-year-olds, and 41 9–10-year-olds. We assessed both categorical performance, indicating the notion of location changes based on the landmarks, as well as metrical performance relating to the precision of the representation of the environment. We investigated whether age, sex, spatial working memory, verbal working memory, and verbal production of left and right contributed to the development of navigation skills. In egocentric navigation, Categorical performance was already above chance at 5 years of age and was positively related to visuo-spatial working memory and the production of left/right, whereas metrical performance was only related to age. Allocentric navigation started to develop between 5 and 8 years of age and was related to sex, with boys outperforming girls. Both boys and girls seemed to rely more on directional landmark information as compared to positional landmark information. To our knowledge, this study is the first to give insight into the relative contribution of different cognitive abilities to navigation skills in school-aged children.

Finding landmarks - An investigation of viewing behavior during spatial navigation in VR using a graph-theoretical analysis approach.

Vision provides the most important sensory information for spatial navigation. Recent technical advances allow new options to conduct more naturalistic experiments in virtual reality (VR) while additionally gathering data of the viewing behavior with eye tracking investigations. Here, we propose a method that allows one to quantify characteristics of visual behavior by using graph-theoretical measures to abstract eye tracking data recorded in a 3D virtual urban environment. The analysis is based on eye tracking data of 20 participants, who freely explored the virtual city Seahaven for 90 minutes with an immersive VR headset with an inbuild eye tracker. To extract what participants looked at, we defined "gaze" events, from which we created gaze graphs. On these, we applied graph-theoretical measures to reveal the underlying structure of visual attention. Applying graph partitioning, we found that our virtual environment could be treated as one coherent city. To investigate the importance of houses in the city, we applied the node degree centrality measure. Our results revealed that 10 houses had a node degree that exceeded consistently two-sigma distance from the mean node degree of all other houses. The importance of these houses was supported by the hierarchy index, which showed a clear hierarchical structure of the gaze graphs. As these high node degree houses fulfilled several characteristics of landmarks, we named them "gaze-graph-defined landmarks". Applying the rich club coefficient, we found that these gaze-graph-defined landmarks were preferentially connected to each other and that participants spend the majority of their experiment time in areas where at least two of those houses were visible. Our findings do not only provide new experimental evidence for the development of spatial knowledge, but also establish a new methodology to identify and assess the function of landmarks in spatial navigation based on eye tracking data.