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11 results for “Odometry”
Dataset: GNSS PPP-RTK Tightly Coupled with Low-cost Visual Inertial Odometry aiming at Urban Canyons
<p>GNSS can provide high-precision positioning worldwide and is the preferred positioning method in autonomous driving and intelligent transportation etc. However, in complex urban environments, due to serious signal occlusion, the positioning performance of GNSS deteriorates sharply, and it even provides incorrect positioning information. To obtain robust navigation, GNSS is usually integrated with inertial measurement unit (IMU) to form a GNSS/INS integrated system. But in complex scenarios, the performance of GNSS/INS is closely related to IMU. For low-cost MEMS IMU, though it can facilitate GNSS positioning to a certain extent, it still cannot stably provide reliable positioning information. Visual sensors and IMUs can be combined to form a VINS system, which can obtain accurate local pose estimation. Therefore, adding visual information to MEMS IMU-based GNSS/INS systems can effectively suppress the divergence of MEMS IMU errors, and provide precise and reliable positioning services in GNSS-challenging environments.</p>
VIO-GNSS Dataset: Benchmarking Dataset for Sensor Fusion of Visual Inertial Odometry and GNSS Positioning
<p>This upload contains datasets for benchmarking and improving different Sensor Fusion implementations/algorithms. The documentation for these datasets can be found on <a href="https://github.com/AaltoVision/vio-gnss-dataset">GitHub</a>.</p> <p>The upload contains two datasets (version 1.0.0):</p> <ul> <li>urban_with_gnss_dead_zones (7.0 GB, ~16 minutes) <ul> <li>City streets</li> <li>A building is passed through on two occasions which makes the GNSS location signal unavailable at times.</li> <li>RTK Fix is acquired at times</li> </ul> </li> <li>suburban_nature (10.6 GB, ~19 minutes) <ul> <li>The route begins on a suburban street but quickly turns into a nature trail. Lots of vegetation</li> <li>The RTK solution is only Float or None most of the route.</li> </ul> </li> </ul> <p>Details on collecting the data:</p> <ul> <li>Software <ul> <li>The data was collected using <a href="https://github.com/AaltoVision/vio-gnss-recorder">this</a> open-source recorder. <ul> <li>Can be easily replayed using <a href="https://github.com/SpectacularAI/sdk-examples">SpectacularAI's SDK</a> (sdk-examples/python/oak/vio_replay.py)</li> </ul> </li> <li>Each dataset contains a map of the travelled route in Otaniemi, Espoo, Finland.</li> <li><strong>Necessary files to implement SLAM are included</strong> in the dataset.</li> <li>Use of NTRIP and the high precision GNSS antenna enables global positioning accuracy of only few centimeters.</li> </ul> </li> <li>Hardware <ul> <li>OAK-D stereo depth + color camera (Luxonis)</li> <li>C099-F9P GNSS module (u-blox)</li> <li>ANN-MB-00 high precision GNSS antenna (u-blox)</li> </ul> </li> </ul>
Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry
<p>This is the dataset release for the <strong><a href="https://www.sigmobile.org/mobisys/2024/" target="_blank" rel="noopener">ACM MobiSys 2024</a> </strong>paper "<strong>Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry</strong>".</p> <ul> <li><strong>Project Website:</strong> <a href="http://radarize.github.io" target="_blank" rel="noopener">https://radarize.github.io</a></li> <li><strong>Project Code:</strong> <a href="http://github.com/ConnectedSystemsLab/radarize_ae" target="_blank" rel="noopener">github.com/ConnectedSystemsLab/radarize_ae</a></li> </ul> <p>If you found this useful, please cite </p> <pre><code>@inproceedings{sie2024radarize, author = {Sie, Emerson and Wu, Xinyu and Guo, Heyu and Vasisht, Deepak}, title = {Radarize: Enhancing Radar SLAM with Generalizable Doppler-Based Odometry}, booktitle = {The 22nd ACM International Conference on Mobile Systems, Applications, and Services (ACM MobiSys '24)}, year = {2024}, doi = {https://doi.org/10.1145/3643832.3661871}, }</code></pre>
Optic flow and odometry data from intelrealsense camera
<p>Insects rely on the perception of image motion, or optic flow, to estimate their velocity relative to nearby objects. This information provides important sensory input for avoiding obstacles. However, certain behaviors, such as estimating the absolute distance to a landing target, accurately measuring absolute distance travelled, and estimating the ambient wind speed require decoupling optic flow into its component parts: absolute ground velocity and distance to nearby objects. Behavioral experiments suggest that insects perform these calculations, but their mechanism for doing so remains unknown. Here we present a novel algorithm that combines the geometry of dynamic forward motion with known features of insect visual processing to provide a hypothesis for how insects might \textit{directly} estimate absolute ground velocity from a combination of optic flow and acceleration information. Our robotics-inspired-biology approach reveals three critical requirements. First, absolute ground velocity can only be directly estimated from optic flow during times of active acceleration and deceleration. Second, spatial pooling of optic flow across a receptive field helps to alleviate the effects of noise and/or low resolution visual systems. Third, averaging velocity estimates from multiple receptive fields further helps to reject noise. Our algorithm provides a hypothesis for how insects might estimate absolute velocity from vision during active maneuvers, and also provides a theoretical framework for designing fast analog circuitry for efficient state estimation that can be applied to insect-sized robots. </p>
Doppler-only Single-scan 3D Vehicle Odometry
<p>Dataset provided with the article of the same name. Created to test the performance of 3D Doppler-capable radar odometry in outdoor scenarios. Sensors mounted on the vehicle include a 3D Doppler-capable radar, 3D lidar, and an IMU. </p>
Dataset for Vehicle Indoor Positioning in Industrial Environments with Wi-Fi, inertial, and odometry data
<p>Dataset collected in an indoor industrial environment using a mobile unit (manually pushed trolley) that resembles an industrial vehicle equipped with several sensors, namely, Wi-Fi, wheel encoder (displacement), and Inertial Measurement Unit (IMU).</p> <p>Sensors were connected to a Raspberry Pi (RPi 3B +), which collected the data from the sensors. Ground truth information was obtained with video camera pointed towards the floor, registering the times when the trolley passed by reference tags.</p> <p>List of sensors:</p> <ul> <li>4x <strong>Wi-Fi interfaces</strong>: Edimax EW7811-Un</li> <li>2x <strong>IMUs</strong>: Adafruit BNO055</li> <li>1x <strong>Absolute Encoder</strong>: US Digital A2 (attached to a wheel with a diameter of 125 mm)</li> </ul> <p>This dataset includes:</p> <ul> <li>1x <strong>Wi-Fi radio map</strong> that can be used for Wi-Fi fingerprinting.</li> <li>6x <strong>Trajectories</strong>: including sensor data + ground truth.</li> <li><strong>APs Information</strong>: list of APs in the building, including their position and transmission channel.</li> <li><strong>Floor plan:</strong> image of the building's floor plan with obstacles and non-navigable areas.</li> <li><strong>Python package</strong> provided for: <ul> <li>parsing the dataset into a data structure (Pandas dataframes).</li> <li>performing statistical analysis on the data (number of samples, time difference between consecutive samples, etc.).</li> <li>computing Dead Reckoning trajectory from a provided initial position.</li> <li>computing Wi-Fi fingerprinting position estimates.</li> <li>determining positioning error in Dead Reckoning and Wi-Fi fingerprinting.</li> <li>generating plots including the floor plan of the building, dead reckoning trajectories, and CDFs.</li> </ul> </li> </ul> <p> </p> <p>When using this dataset, please cite its data description paper:</p> <p>Silva , I.; Pendão, C.; Torres-Sospedra, J.; Moreira, A. Industrial Environment Multi-Sensor Dataset for Vehicle Indoor Tracking with Wi-Fi, Inertial and Odometry Data. <em>Data</em> <strong>2023</strong>, <em>8</em>, 157. <a href="https://doi.org/10.3390/data8100157" target="_blank" rel="noopener">https://doi.org/10.3390/data8100157</a> </p> <p> </p>
Optic flow and odometry data from intelrealsense camera
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ADVIO: An Authentic Dataset for Visual-Inertial Odometry
<p><strong>Data abstract:</strong><br> This Zenodo upload contains the ADVIO data for benchmarking and developing visual-inertial odometry methods. The data documentation is available on Github: <a href="https://github.com/AaltoVision/ADVIO">https://github.com/AaltoVision/ADVIO</a></p> <p><strong>Paper abstract:</strong><br> The lack of realistic and open benchmarking datasets for pedestrian visual-inertial odometry has made it hard to pinpoint differences in published methods. Existing datasets either lack a full six degree-of-freedom ground-truth or are limited to small spaces with optical tracking systems. We take advantage of advances in pure inertial navigation, and develop a set of versatile and challenging real-world computer vision benchmark sets for visual-inertial odometry. For this purpose, we have built a test rig equipped with an iPhone, a Google Pixel Android phone, and a Google Tango device. We provide a wide range of raw sensor data that is accessible on almost any modern-day smartphone together with a high-quality ground-truth track. We also compare resulting visual-inertial tracks from Google Tango, ARCore, and Apple ARKit with two recent methods published in academic forums. The data sets cover both indoor and outdoor cases, with stairs, escalators, elevators, office environments, a shopping mall, and metro station.</p> <p><strong>Attribution:</strong><br> If you use this data set in your own work, please cite this paper:</p> <ul> <li>Santiago Cortés, Arno Solin, Esa Rahtu, and Juho Kannala (2018). <em>ADVIO: An authentic dataset for visual-inertial odometry</em>. In European Conference on Computer Vision (ECCV). Munich, Germany.</li> </ul>
Support data for "Maritime radar odometry inspired by visual odometry"
<p>This is reduced resolution example data to accompany the code at `https://github.com/hflemmen/radar_odometry`.</p>
The UMA-VI dataset: Visual–inertial odometry in low-textured and dynamic illumination environments
<p>This article presents a visual–inertial dataset gathered in indoor and outdoor scenarios with a handheld custom sensor rig, for over 80 min in total. The dataset contains hardware-synchronized data from a commercial stereo camera (Bumblebee®2), a custom stereo rig, and an inertial measurement unit. The most distinctive feature of this dataset is the strong presence of low-textured environments and scenes with dynamic illumination, which are recurrent corner cases of visual odometry and simultaneous localization and mapping (SLAM) methods. The dataset comprises 32 sequences and is provided with ground-truth poses at the beginning and the end of each of the sequences, thus allowing the accumulated drift to be measured in each case. We provide a trial evaluation of five existing state-of-the-art visual and visual–inertial methods on a subset of the dataset. We also make available open-source tools for evaluation purposes, as well as the intrinsic and extrinsic calibration parameters of all sensors in the rig. The dataset is available for download at <a href="http://mapir.uma.es/work/uma-visual-inertial-dataset">http://mapir.uma.es/work/uma-visual-inertial-dataset</a></p>
Data from: Optic flow odometry operates independently of stride integration in carried ants
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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)
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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.