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26 results for “Indoor Localization”
Bluetooth indoor localization Dataset
<p><strong>Bluetooth indoor localization Dataset</strong>: Collected to perform experimentation on how bluetooth signal strengths can be used to determine one of the indoor locations. The dataset has 6 transmission power, from Tx01 to Tx06; and in each dataset have 5 features with the bluetooth RSSI because the enviroment have 5 BLE4.0 and 1 categorical target that is the sector where the person is located (15 sectors total).</p>
Indoor Localization Dataset
<p>The dataset contains information concerning the older people’s movement inside their homes regarding their indoor location in the home setting.</p> <p>The dataset is recorded daily with the use of smart beacon devices installed in each older person's home and monitored through the system.</p> <p>Each record of the dataset has the following fields:</p> <p>- <strong>part_id</strong>: The user ID, which should be a 4-digit number</p> <p>- <strong>ts_date</strong>: The recording date, which follows the “YYYYMMDD” format, e.g. 14 September 2017, is formatted as 20170914</p> <p>- <strong>ts_time</strong>: The recording time, which follows the “hh:mm:ss” format</p> <p>- <strong>room</strong>: The room which the person entered on the specific date and time (It is assumed that the person remained in the room till the next recording of the same day)</p>
Supplementary materials for "TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density"
<p>Supplementary materials for "TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density"</p> <p> </p> <p>For more information please refer to the data descriptor available at: https://www.sciencedirect.com/science/article/pii/S2352340924003251</p> <p>Please cite as:</p> <p>Klus, L., Klus, R., Lohan, E.S., Nurmi, J., Granell, C., Valkama, M., Talvitie, J., Casteleyn, S. and Torres-Sospedra, J., 2024. TUJI1 Dataset: Multi-device dataset for indoor localization with high measurement density. <em>Data in Brief</em>, p.110356.</p> <p> </p>
Evaluation of underfloor accelerometers through fingerprinting for indoor localization
<div><strong>Fingerprinting</strong></div> <div>Code developed to test the effectiveness of an indoor positioning system where multiple accelerometers are placed under the floor and set up to collect data. This material complements the work done for the paper "Evaluation of Underfloor Accelerometers for Enabling Location-based Services in Intelligent Environments" </div> <div>and helps readers to reproduce and validate the results presented in that paper. </div> <div> </div> <p><strong>What the code does</strong><br>The execution of the main code performs the following:<br>1. generation of sensor maps through their absolute coordinates;<br>2. noise reduction on the raw data according to the average of the stress the accelerometers are subjected at quiet;<br>3. generation of the fingerprint maps per each data set;<br>4. generation of the clean ground truth files (deleting coordinates set to zero);<br>5. computation of the n-dimensional distance between observations at a given time step and the euclidean error between the minimum distance value coordinates and the respective temporally closest ground truth ones;<br>6. same as in 5 but with intra-user fingerprint maps;<br>7. same as in 5 but with inter-user fingerprint maps;<br>8. same as in 5 but with enhanced inter-user fingerprint maps.</p> <p><strong>To run the code please read the file README.md</strong></p>
Indoor localization using Wi-Fi and IMU at National Taiwan University CSIE 5F
<p>A dataset composed of Wi-Fi fingerprints and IMU sensing data which collect by smartphone.</p> <p>We collected this dataset at National Taiwan University CSIE building 5F.</p> <p>The txt files are raw data.</p> <p>Fingerprint.txt is the Wi-Fi fingerprint set for reference map.</p> <p>Track1.txt and Track2.txt are the Wi-Fi fingerprints and IMU sensing data collect by android smartphone.</p> <p> </p> <p>The .npy files are the preprocessed data.</p> <p> </p> <p> </p> <p> </p>
Microsoft Indoor Localization Competition 2018 Dataset
<p>Detailed ground truth measurements and error visualization for each team, as well as the 3D point cloud of the evaluation area related to the Microsoft Indoor Localization Competition 2018.</p> <p>Additional details can be found here:</p> <p>https://www.microsoft.com/en-us/research/event/microsoft-indoor-localization-competition-ipsn-2018/</p>
Dataset for Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning
<p>The dataset titled <strong>"Real-Time Indoor Localization System Based on Wearable Device, Bluetooth Low Energy (BLE) Beacons, and Machine Learning</strong><strong>"</strong> was collected to support the development of an indoor localization system that operates at the room level. The dataset includes measurements of Received Signal Strength Indication (RSSI) from Bluetooth Low Energy (BLE) beacons (specifically the iBKS105 model) recorded by an ESP32 device. These RSSI values were captured across various rooms, allowing for precise localization within an indoor environment. The dataset is particularly useful for research in indoor localization system including machine learning-based localization algorithms.</p>
IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization
<p>IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization. </p> <p>This data is split up into two folders. </p> <p>"stationary_magnetometer_data" 0.1Hz samples of a RM3100 magnetometer that was kept in one position from July 2022 through February 2023. The data is partitioned into separate files for analytical convenience of our research. Each file here is a CSV with a timestamp (synchronized with chrony to a central computer) and the three components of the measured magnetic field. We did not calibrate this stationary magnetometer.</p> <p>"UAV_and_mocap_data" has many subfolders. Each subfolder is labeled by a date and a small description of the goals for that test segment. There is a single "EXPLANATION" file in each subfolder that gives more detail on the provided data. The data here includes the trajectory flown by the UAV, outdoor calibration data to adjust the raw magnetometer measurements, and IMU/motion capture data for each listed flight test. The EXPLANATION file should explain what trajectory was flown for each individual flight test. </p>
A Bluetooth 5.1 Dataset Based on Angle of Arrival and RSS for Indoor Localization
<p><strong>Overview</strong>:<br> The dataset contains measurements of Angle of Arrival (AoA) and Received Signal Strength (RSS)collected from Bluetooth 5.1 tags and a set of 4 anchor nodes deployed in an indoor environment. The data collection campaign has been conducted in a wide-open room of 110 square meters located in a wide open room.<br> The dataset includes four application scenarios covering typical use-cases for indoor localization:</p> <ol> <li>calibration: 4 anchors and 1 tag mounted on a tripod and positioned in 119 different locations;</li> <li>static: 4 anchors and 1 tag held by a person resting in 36 different locations. The tag is locked on a lanyard around the person's neck, we collect data with the person oriented toward North, South, East and West;</li> <li>mobility: 4 anchors and a person holding the tag around the neck a moving along 3 different paths;</li> <li>proximity scenario: 4 anchors and tags held by groups of people. We reproduce proximity with dyads, triplets and of groups of 4 people approach and distancing along the time.</li> </ol> <p>All the scenarios include an accurate Ground Truth (GT) annotation. The dataset is organized in four folders one for each scenario: Calibration, Static, Mobility and Proximity.</p> <p>The dataset enables the study of how AoA varies under stationary or mobility conditions, facilitating the analysis of an anchor's AoA model. In addition, the data collected can be analyzed to create a simulator of AoA values, which is useful for rapid prototyping and evaluation of indoor localization algorithms.</p> <p><strong>How to use the dataset</strong>:<br> Please, read the README.txt file detailing the dataset's format and the data collection camping. In summary, collected data include:<br> - Timestamp<br> - Tag identifier<br> - RSS on the 1<sup>st</sup> antenna's polarization<br> - AoA azimuth<br> - AoA elevation<br> - RSS on the 2<sup>nd</sup> antenna's polarization<br> - Bluetooth channel<br> - Anchor identifier</p> <p><strong>How to cite this dataset</strong>:</p> <p>- DOI number of this datsaset : 10.5281/zenodo.7759557</p> <p>- M. Girolami, F. Furfari, P. Barsocchi and F. Mavilia, "A Bluetooth 5.1 Dataset Based on Angle of Arrival and RSS for Indoor Localization," in <em>IEEE Access</em>, vol. 11, pp. 81763-81776, 2023, doi: 10.1109/ACCESS.2023.3301126.</p>
Keyahta/grabay: Indoor localization dataset
<p>HUSTData is an indoor localization dataset. It is collected from a typical lecture building D, with a total area of 482m2 in Huazhong University of Science and Technology. Source codes of our previous work using this dataset with detailed explanations are in progress. Click the GitHub button on the right or use https://github.com/Keyahta/grabay to go to the dataset. </p>
Dataset: Indoor Localization with Narrow-band, Ultra-Wideband, and Motion Capture Systems
<p><strong>Localization Dataset README</strong></p> <p>In this, data for BLE and UWB calibration is included through the use of UWB and OptiTrack Motion capture system respectively. There are two sets of data each covering two scenarios; these being walking and trolley.</p> <p>The two groups containing the data are laid out identically as indicated below.</p> <p><strong>Bluetooth Low Energy / Ultra-Wideband</strong></p> <p>| Session ID | 8 x AoA | 8 x RSSI | BLE x | BLE y | UWB x | UWB y |</p> <p>Where :</p> <p>- AoA is Angle of Arrival, with two values given for each anchor node.<br> - RSSI is the Received Signal Strength Indicator, also with two values given for each anchor node.<br> - BLE x and y location estimates [1].<br> - UWB x and y location estimates.</p> <p><strong>Number of Samples (BLE/UWB)</strong></p> <p>The datasets contains the following number of samples:</p> <p>- Walk - 4896 samples.<br> - Trolley - 4856 samples.</p> <p><strong>Ultra-Wideband / OptiTrack Motion Capture</strong></p> <p>| Session ID | 4 x CIR | 4 x PSA | Distance | UWB x | UWB y | OPT x | OPT y |</p> <p>Where:</p> <p>- CIR is Channel Impulse Response for each received signal from the Anchors to the target tag.<br> - PSA is the Preamble Symbol Accumulation, from each of the 4 Anchors to the target tag.<br> - Distances of the tag to each of the 4 anchors.<br> - UWB x and y location estimates.<br> - OptiTrack location estimates.</p> <p><strong>Number of Samples (UWB/OPT)</strong></p> <p>- Walk - 2797 samples.<br> - Trolley - 3202 samples.</p> <p><strong>Total Number of Samples : 15751</strong></p> <p>[1]: A Khan, T Farnham, R Kou, U Raza, T Premalal, A Stanoev, W Thompson, "Standing on the Shoulders of Giants: AI-driven Calibration of Localisation Technologies", IEEE Global Communications Conference (GLOBECOM) 2019</p>
Figs. 59–71 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 59–71. Habitus images, SEMs, habitat, and genitalia illustrations of Selenophorus species. 59–63) S. striatopunctatus dorsal and ventral aspects, SEM images of pronotum, elytra, and elytral striae showing setigerous puncture; 64–68) S. elytrostictus, new species, dorsal and ventral aspects, SEM images of pronotum, elytra, and elytral striae showing setigerous puncture; 69) Laguna Atascosa NWR, loma-freshwater margin; 70–71) S. elytrostictus, new species, male median lobe left lateral and dorsal views.
Figs. 72–75 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 72–75. Habitus images of Selenophorus and related genera, dorsal aspect: 72) S. maritimus; 73) Athrostictus punctatulus; 74) Discoderus discoderoides. 75) Sabal Palm Sanctuary, old-growth "core".
Figs. 44–50 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 44–50. Habitus images of Selenophorus species, dorsal aspect. 44) S. blanchardi; 45) S. pedicularius; 46) S. planipennis; 47) S. aeneopiceus; 48) S. breviusculus; 49) S. fatuus; 50) S. parumpunctatus.
Figs. 37–43 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 37–43. Habitus images and genitalia illustrations of Selenophorus species. 37) S. aequinoctialis, dorsal aspect; 38) S. palliatus, dorsal aspect; 39) S. sinuaticollis, dorsal aspect; 40–43) S. rileyi, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.
Figs. 29–36 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 29–36. Habitus images and genitalia illustrations of Selenophorus species. 29) S. chaparralus, dorsal aspect; 30) S. opalinus, dorsal aspect; 31) S. fabricii, dorsal aspect; 32) S. trepidus, dorsal aspect; 33–36) S. undatus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.
Figs. 15–18 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 15–18. Habitus images of Selenophorus species, dorsal aspect. 15) S. gagatinus; 16) S. concinnus; 17) S. semirufus; 18) S. schaefferi.
Figs. 8–14 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 8–14. Habitus images and genitalia illustrations of Selenophorus species. 8–11) S. pumilus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views; 12) S. seriatoporus, dorsal aspect; 13) S. discopunctatus, dorsal aspect; 14) S. fossulatus, dorsal aspect.
Figs. 1–7 in Indoor Radio Map localization WiFi fingerprint datasets
Figs. 1–7. Habitus images and genitalia illustrations of Selenophorus species. 1) S. contractus, dorsal aspect; 2) S. ellipticus, dorsal aspect; 3) S. granarius, dorsal aspect; 4–7) S. nonellipticus, new species, dorsal and ventral aspects, male median lobe left lateral and dorsal views.
ESP32C3 WiFi FTM RSSI Indoor Localization
<div> <div> <div> <h2><strong>Wi-Fi FTM RSSI Localization dataset</strong></h2> <p>Wi-Fi Fine Time Measurement for positioning / Indoor Localization in <strong>3 different locations</strong> and using <strong>8 different APs</strong> <br> <br>Custom APs using <strong>ESP32C3</strong> and Raw FTM is measured in nanoseconds <br> <br>Data is only measured at the Router Side <br> <br>Data is not measured at client side <br> <br>Has 4 datasets inside the zip folder with over <strong>100,000 data points</strong> <br> <br>Contains processed Wi-Fi FTM packets from various routers in: <br>1. University of Victoria, Engineering Office Wing (EOW) 3rd Floor <br>2. University of Victoria, Engineering Office Wing (EOW) 5th Floor <br>3. University of Victoria, Engineering and Computer Science (ECS) 1st Floor <br> <br>Each folder contains a training dataset and a testing dataset that is independent in time and space <br> <br>Router Time is synchronized using chrony</p> </div> </div> </div> <div> <div>Instructions: </div> <div> <div> <h2><strong>Dataset is in CSV format</strong></h2> <p>Relative Time (seconds) | X Position (meters) | Y Position (meters) | Feature 1 | Feature 2 | Feature 3 ..... <br> <br>Time resets at every new position and position accuracy is a few centimeters using LIDAR and RGBD camera <br> <br>Map is in ROS2 PGM format that can read by ROS2 programs <br> <br>Data for the paper <br> <br>Wi-Fi and Bluetooth Contact Tracing Without User Intervention</p> <p><br><a href="https://ieeexplore.ieee.org/document/9866766" rel="nofollow">https://ieeexplore.ieee.org/document/9866766</a></p> <p>Please Cite As</p> <pre><code>@article{yuen2022wi, title={Wi-Fi and Bluetooth contact tracing without user intervention}, author={Yuen, Brosnan and Bie, Yifeng and Cairns, Duncan and Harper, Geoffrey and Xu, Jason and Chang, Charles and Dong, Xiaodai and Lu, Tao}, journal={IEEE Access}, volume={10}, pages={91027--91044}, year={2022}, publisher={IEEE} }</code></pre> </div> </div> </div>
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