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19 results for “visual navigation”
Sidewalk Environment for Visual Navigation
<p>This dataset contains low and high resolution panoramic images, coordinates, labels and a connectivity graph. In order to run this simulated environment, you will need at least one copy of the panoramic images, which are available in low (84x224 pixels) or high resolution (1280x3840 pixels). For more information, visit https://mweiss17.github.io/SEVN/.</p>
Bodily and Visual-Cognitive Navigation Aids to Enhance Spatial Recall in Mild Cognitive Impairment
<p>Individuals with mild cognitive impairment (MCI) syndrome often report navigation difficulties, accompanied by impairments in egocentric and allocentric spatial memory. However, studies have shown that both bodily cues (e.g., motor commands, proprioception, vestibular information) and visual-cognitive cues (e.g., maps, directional arrows, attentional markers) can support spatial memory in MCI. These aids offer valuable insights for designing navigation training programs in aging. Fifteen MCI patients were recruited for this study. Their egocentric and allocentric memory recall performances were tested through a navigation task with five different virtual reality (VR) assistive encoding procedures (bodily, vision only, interactive allocentric map, reduced executive load, free navigation without cues). Bodily condition consisted of an immersive VR setup to engage self-motion cues, vision only condition consisted of passive navigation without interaction, in the interactive allocentric map condition patients could use a bird-view map, in the reduced executive load condition directional cues and attentional markers were employed, and during free navigation no aid was implemented. Bodily condition improved spatial memory compared to vision only and free navigation without cues. In addition, the interactive allocentric map was superior to the free navigation without cues. Surprisingly, the reduced executive load was comparable to vison only condition. Moreover, a detrimental impact of free navigation was observed on allocentric memory across testing trials. These findings challenge the notion of an amodal representation of space in aging, suggesting that spatial maps can be affected by the modality in which the environment was originally encoded.</p>
Carbon dioxide and blood-feeding shift visual cue tracking during navigation in Aedes aegypti mosquitoes
<p>Hematophagous mosquitoes need a blood meal to complete their reproductive cycle. To accomplish this, female mosquitoes seek vertebrate hosts, land on them, and bite. As their eggs mature, they shift attention away from hosts and towards finding sites to lay eggs. We asked whether females were more tuned to visual cues when a host-related signal, carbon dioxide, was present, and further examined the effect of a blood meal, which shifts behavior to ovipositing. Using a custom, tethered-flight arena that records wing stroke changes while displaying visual cues, we found the presence of CO2 enhances visual attention towards discrete stimuli and improves contrast sensitivity for host-seeking <em>Aedes aegypti</em> mosquitoes. Conversely, intake of a blood meal reverses vertical bar tracking, a stimulus that non-fed females readily follow. This switch in behavior suggests that physiological status modulates visual attention in mosquitoes, a phenomenon that has been described before in olfaction but not in visually-driven behaviors.</p>
Figure 1. The use of visual servo control for helicopter stabilization-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Visual servoing is an approach to control motion of a helicopter using information feedback<br> from a camera mounted on it. For their tremendous potential applications in various areas including<br> environmental monitoring and anti-terrorism, unmanned small helicopters are being extensively<br> studied in robotics and control in recent years. However, the research advance in dynamic control of<br> small helicopters is limited due to highly coupled non- linear dynamics and the existence of various<br> uncertain- ties. Many people studied controller design based on a Publisher Item Identifier.<br> linearized or simplified model, but the controllers developed under linearized models cannot<br> guarantee dynamic stability rigorously. Another effort is application of modern non-linear control<br> theory to helicopter control because small helicopter are good test beds for sophisticated control<br> techniques for their small size and highly coupled dynamics [4].</p>
A dataset for robotic outdoor visual navigation with multiple passages through trajectory segments
<p>The images were captured by a fisheye camera and a magnetic compass was used to acquire the orientation data. The datasets are split in two folders:<br> 1) LEARN: In order to learn a new place, the robot camera captures 15 images over a 360 degrees panorama. During this process, the robot stays still in order to avoid distortions in the representation of the place.<br> 2) EXPLO: When exploring the environment (i.e. the rest of the time), the robot only captures 7 images per panorama, for the purpose of faster place recognition. Images are captured while the robot is moving. Various exploration panoramas are recorded around the trajectory performed in the learning panoramas (see traj.pdf).<br> <br> The average distance between two learning panoramas is 0.93 +/- 0.03 meters<br> The average distance traveled during an exploration panoramas is 0.71 +/- 0.01 meters<br> <br> DATASET A<br> ---------<br> - 20 meters long<br> - 22 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 5 exploration trajectories<br> - A_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - A_parallel: 29 exploration panoramas<br> - A_diagonal1: 28 exploration panoramas<br> - A_diagonal2: 30 exploration panoramas<br> - A_diagonal3: 29 exploration panoramas<br> <br> DATASET B<br> ---------<br> - 20 meters long<br> - 21 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 4 exploration trajectories<br> - B_on_learned: 29 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - B_parallel: 29 exploration panoramas<br> - B_diagonal1: 29 exploration panoramas<br> - B_diagonal2: 29 exploration panoramas<br> <br> DATASET C<br> ---------<br> - 23.1 meters long<br> - 25 learning panoramas (i.e. sets of 15 images captured while robot is stopped)<br> - 2 exploration trajectories<br> - C_on_learned: 34 exploration panoramas (i.e. sets of 7 images captured while robot is moving)<br> - C_parallel: 34 exploration panoramas<br> <br> <br> <br> PANO_INFO FILE STRUCTURE<br> ------------------------<br> Every folder containing images also contains an info file, named either learn_pano_info.SAVE or explo_pano_info.SAVE. Each line corresponds to an image. The structures is the following:<br> - column 1: id = image_id + 1<br> - column 2: azimuth of the center of the image in degrees/360 (value in [0,1])<br> - column 3: elevation of the center of the image. irrelevant in this database (equal to 0).<br> - column 4: type of panorama: equal to 1 if learning and to 0 if exploration.<br> - column 5: end of panorama: equal to 1 if it corresponds to the last image of a panorama.<br> <br> <br> REFERENCES<br> ----------<br> The dataset was used in the paper: Belkaid, M., Cuperlier, N., and Gaussier, P. Combining local and global visual information in context-based neurorobotic navigation. In Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), pages 4947-4954, doi: 10.1109/IJCNN.2016.7727851, 2016.<br> <br> </p>
Landmark-Aware Visual Navigation (LAVN) Dataset
<p>We present a \textbf{L}andmark-\textbf{A}ware \textbf{V}isual \textbf{N}avigation (LAVN) dataset to allow for supervised learning of human-centric exploration policies. We collect RGB observation and human point-click pairs as a human annotator explores virtual and real world environments with the goal of full coverage exploration of the space. These human point-clicks serve as direct supervision for waypoint prediction when learning to explore in environments. The human annotators also provide distinct landmark examples along each trajectory, which we intuit will simplify the task of map or graph building and localization. </p>
Carbon dioxide and blood-feeding shift visual cue tracking during navigation in Aedes aegypti mosquitoes
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Spatial modulation of visual responses arises in cortex with active navigation
<p>During navigation, the visual responses of neurons in mouse primary visual cortex (V1) are modulated by the animal's spatial position. Here we show that this spatial modulation is similarly present across multiple higher visual areas but negligible in the main thalamic pathway into V1. Similar to hippocampus, spatial modulation in visual cortex strengthens with experience and with active behavior. Active navigation in a familiar environment, therefore, enhances the spatial modulation of visual signals starting in the cortex.</p>
The codes and datasets for the paper titled "Don't Confuse! Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users"
<h3>Project Title:</h3> <p>Redrawing GUI Navigation Flow in Mobile Apps for Visually Impaired Users</p> <h3>Description:</h3> <p>This project enhances GUI navigation accessibility for visually impaired users by analyzing GUI structures, identifying issues, and optimizing navigation flow.</p> <h3>Contents:</h3> <ol> <li><strong>Risk Warnings</strong></li> <li><strong>Variable Explanations</strong></li> <li><strong>Function Descriptions</strong></li> <li><strong>Usage Instructions</strong></li> <li><strong>Contact Information</strong></li> </ol> <h3>1. Risk Warnings:</h3> <ul> <li>Navigation analysis focuses on visible nodes only.</li> <li>Code maintenance issue in loop C.</li> <li>Prior reading of the "Info" button warning is essential.</li> <li>Potential information loss in the reordering algorithm.</li> </ul> <h3>2. Variable Explanations:</h3> <ul> <li>Constants: parameter1, parameter2, outputSign.</li> <li>Global Variables: nodeString, nodeList, intToRect, intToDir, intToInfo, infoToInt, intToSubRect, intToSubKind.</li> <li>Local Variables: sortedSon, sign, acceptable.</li> </ul> <h3>3. Function Descriptions:</h3> <ul> <li><strong>isNodeVisibleOnScreen</strong>: Checks if a node is visible on the screen.</li> <li><strong>Gestalt</strong>: Conducts a depth-first search traversal of all nodes and records the Gestalt_inspired order.</li> <li><strong>checkNodeNecessity</strong>: Further checks if a node is necessary for navigation.</li> <li><strong>DFS</strong>: Used for the reordering algorithm.</li> </ul> <h3>4. Usage Instructions:</h3> <ul> <li>Ensure thorough understanding of risk warnings.</li> <li>Modify and maintain the code as necessary.</li> <li>Read the warning prompt before using the "Info" button.</li> <li>Exercise caution with potential information loss in reordering.</li> </ul> <h3>5. Contact Information:</h3> <ul> <li>Developer: Mengxi Zhang</li> <li>Email: <a target="_new">zmxalakay@126.com</a></li> </ul> <p><strong>Note</strong>: This README provides a brief overview. Refer to the User_Guidelines documentation for detailed information.</p>
Data from: Navigating the scales of diversity in subtropical and coastal fish assemblages ascertained by eDNA and visual surveys
<p>Environmental DNA (eDNA) metabarcoding emerges as a powerful method, allowing a more exhaustive investigation of fish fauna than any other methods. Yet, the general use of eDNA as a replacement of traditional methods such as visual surveys or physical sampling remains debatable. Therefore, a prior understanding of eDNA's spatial resolution is necessary. This study aimed to compare the assessments of fish diversity at three spatial scales by eDNA, underwater visual census (UVC), and diver-operated video (DOV) surveys across 21 reef sites in northern Taiwan. The specific objectives were to explore the regional species pool (γ-diversity), reveal spatial patterns of fish assemblages (β-diversity), and disentangle the relationships between fish assemblages and benthic composition (α-diversity). At the γ-diversity level, a total of 438 marine fish species were detected across methods. eDNA exhibits an extraordinary power to explore the regional species pool given sufficient replication, a power which is unachievable by DOV and UVC. At the β-diversity level, all the methods successfully revealed the same spatial patterns of beta diversity and the distance decay of similarity in fish assemblages. At the α-diversity level, none of the methods is capable of investigating the entire resident fish fauna, but visual surveys are more suitable for scrutinizing interactions between fish and benthos. Instead of undiscriminatingly recommending a combination of eDNA with traditional survey methods, we suggest implementing specific surveys in accordance with the ecological questions of interest.</p>
AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments - Supplementary Data
<p><strong>Introduction</strong></p> <p>In this zip, we release the auxiliary data that is beneficial to execute the implementation of AVLEN described in our paper AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments by Sudipta Paul, Amit K Roy-Chowdhury, and Anoop Cherian, NeurIPS, 2022.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped data is 4.6G</li> <li>The unzipped folder contains: (i) a README.md file and (ii) ./AVLEN-data folder. The latter contains the following zip files. Please see the AVLEN code to see how to unzip these files into their respective folders. <ul> <li>ckpt.119.pth -- 61M </li> <li>connectivity.zip -- 1.4M </li> <li>pretrained_weights.zip -- 1.7G</li> <li>ResNet-152-imagenet.zip -- 2.9G</li> <li>semantic_audionav_dialog_approx.zip -- 2.7M</li> <li>soundspaces.zip -- 479K</li> <li>speaker_model_weights.zip -- 51M</li> </ul> </li> </ul> <p><strong>Other Resources</strong></p> <p>For the implementation of AVLEN that uses the data shared here, please visit <a href="https://www.merl.com/publications/TR2022-131">MERL TR2022-131</a>.</p> <p><strong>Citation</strong></p> <p>If you use AVLEN in your research, please cite our paper:</p> <pre><code>@InProceedings{paul2022avlen, title={AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments}, booktitle={Advances in Neural Information Processing Systems}, author={Paul, Sudipta and Roy-Chowdhury, Amit and Cherian, Anoop}, volume={35}, pages={6236--6249}, year={2022} }</code></pre> <p><strong>Copyright and License</strong></p> <p>The AVLEN dataset is released under CC-BY-SA-4.0 license.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2023 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre> <p> </p>
Data from: Navigating the scales of diversity in subtropical and coastal fish assemblages ascertained by eDNA and visual surveys
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Spatial modulation of visual responses arises in cortex with active navigation
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Activation of Human Visual Area V6 during Egocentric Navigation with and without visual Experience
<p>V6 is a retinotopic area located in the dorsal visual stream that integrates eye movements with retinal and visuo-motor signals. Despite the known role of V6 in visual motion, it is unknown whether it is involved in navigation and how sensory experiences shape its functional properties. We explored the involvement of V6 in egocentric navigation in sighted and in congenitally blind (CB) participants navigating via an in-house distance-to-sound sensory substitution device (SSD), the EyeCane. We performed two fMRI experiments on two independent datasets. In the first experiment, CB and sighted participants navigated the same mazes. The sighted performed the mazes via vision, while the CB via audition. The CB performed the mazes before and after a training session using the EyeCane SSD. In a second experiment a group of sighted people performed a motor topography task. Our results show that right V6 (rhV6) is selectively involved in egocentric navigation independently of the sensory modality used. Indeed, after training, rhV6 of CB is selectively recruited for auditory navigation, similar to rhV6 in the sighted. Moreover, we found activation for body movement in area V6, that can putatively contribute to its involvement in egocentric navigation. Taken together, our findings suggest that area rhV6 is a unique hub that transforms spatially relevant sensory information into an egocentric representation for navigation. While vision is clearly the dominant modality, rhV6 is in fact a supramodal area that can develop its selectivity for navigation in the absence of visual experience.</p>
Advanced Spatiomotor Rehabilitation for Navigation in Blindness & Visual Impairment
ClinicalTrials.gov study NCT05377853. IPD Sharing: Not stated. Countries: 1. Publications: 7.
Brain Stimulation and Visually-guided Navigation
ClinicalTrials.gov study NCT04961645. IPD Sharing: NO. Countries: 1. Publications: 0.
Data from: Visual and non-visual navigation in blind patients with a retinal prosthesis
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Data from: Neuronal connectome of a sensory-motor circuit for visual navigation
Animals use spatial differences in environmental light levels for visual navigation; however, how light inputs are translated into coordinated motor outputs remains poorly understood. Here we reconstruct the neuronal connectome of a four-eye visual circuit in the larva of the annelid Platynereis using serial-section transmission electron microscopy. In this 71-neuron circuit, photoreceptors connect via three layers of interneurons to motorneurons, which innervate trunk muscles. By combining eye ablations with behavioral experiments, we show that the circuit compares light on either side of the body and stimulates body bending upon left-right light imbalance during visual phototaxis. We also identified an interneuron motif that enhances sensitivity to different light intensity contrasts. The Platynereis eye circuit has the hallmarks of a visual system, including spatial light detection and contrast modulation, illustrating how image-forming eyes may have evolved via intermediate stages contrasting only a light and a dark field during a simple visual task.
Data from: Neuronal connectome of a sensory-motor circuit for visual navigation
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.