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Dataset results
5 results for “GNSS RTK”
Precision viticulture dataset for detailed vineyard mapping composed of geotagged smartphone ground images, phytosanitary status, UAV orthomosaics, 3D point clouds, and RTK GNSS data - Northern Spain, July 2022
<p>This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations.</p> <p>This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value:</p> <div> <ul> <li>Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. <em>Data in Brief</em> 46 (February 2023): 108848. <a href="https://doi.org/10.1016/j.dib.2022.108848">https://doi.org/10.1016/j.dib.2022.108848</a>.</li> <li>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ <em>Data in Brief</em>, October 2023, 109686. <a href="https://doi.org/10.1016/j.dib.2023.109686">https://doi.org/10.1016/j.dib.2023.109686</a>.</li> <li> <div> <div>Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. <em>Data in Brief</em> 46 (February 2023): 108876. <a href="https://doi.org/10.1016/j.dib.2022.108876">https://doi.org/10.1016/j.dib.2022.108876</a>.</div> <div> </div> </div> </li> </ul> </div>
Towards the Future Generation of Railway Localization Exploiting RTK and GNSS
<p>This repository contains the datasets acquired by ETH-PBL in conjunction with Unibo and SADEL during two days of testing in October 2022 near Modena, Italy.</p> <p>The data were acquired using two sensor nodes developed by ETH Zurich running a <a href="https://www.st.com/en/microcontrollers-microprocessors/stm32l452ce.html">STM32L452CEU6</a> MCU.<br> Each node collected data on the motion of the train using an <a href="https://www.st.com/en/mems-and-sensors/asm330lhh.html">ST ASM330LHH</a> automotive grade IMU as well as a <a href="https://www.u-blox.com/en/product/zed-f9p-module">u-blox ZED-F9P</a> GNSS module fed with live RTCM-data from a closeby RTK base station provided by SADEL. The base station utilized another ZED-F9P GNSS module connected to a Raspberry Pi which transmitted the generated RTCM correction packages over a raw TCP socket.<br> The data was then received using a <a href="https://www.u-blox.com/en/product/sara-r4-series">u-blox SARA-R4</a> cellular network module.</p> <p>The track was chosen as it exposes a variety of interesting GNSS environments. Encountered environments are ranging from urban over suburban to open field environments as well as one tunnel. Due to this composition, the availability of cellular connection and thus RTK correction data was patchy but mostly stable.</p> <p>The two sensor nodes were fixed to the Train Chassis, one centered in the train and the other positioned on the left side in driving orientation. Node 1 was placed on the floor in front of the driver's seat and positioned to be aligned with the center of the train in the lateral direction. A TOPGNSS TOP106 L1/L2 multi-band antenna was placed below the rear-facing windscreen also aligned with the same axis. Node 2 was mounted on a window on the left side of the train when facing in the direction of travel. This is approximately 1m above the floor and 1.4m left to the lateral center of the train. An ANN-MB00 L1/L2 antenna was attached to the outside frame of the train above the window.</p> <p>This dataset is linked with the GitHub repository at <a href="https://github.com/ETH-PBL/Railway-Precise-Localization">Railway-Precise-Localization</a> where the data format description and the pre-processing scripts are provided.</p>
BiodivAR comparative user test (GNSS vs. RTK) dataset
<p>This dataset comprises data used to evaluate and compare the BiodivAR open source web location-based AR application in a presentation submitted to the FOSS4G 2023 conference in Pritzen, Kosovo. The application is available at <https://biodivar.heig-vd.ch> and its source code at <https://github.com/MediaComem/biodivar>.</p>
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>
Data acquired at Forest with RTK GNSS
<p>These bag files were recorded to test the GNSS RTK using two different technologies. The first bag "rosbag2_2023_04_11-16_40_36" used a RENEP public RTCM base while the second bag used the a fixed based installed in our terrain. </p> <p> </p> <p>We have also recorded:</p> <p> Topic: /base/fix | Type: sensor_msgs/msg/NavSatFix | Count: 754 | Serialization Format: cdr<br> Topic: /base/navrelposned | Type: ublox_msgs/msg/NavRELPOSNED9 | Count: 754 | Serialization Format: cdr<br> Topic: /odom | Type: nav_msgs/msg/Odometry | Count: 1510 | Serialization Format: cdr<br> Topic: /rover/fix | Type: sensor_msgs/msg/NavSatFix | Count: 394 | Serialization Format: cdr<br> Topic: /rover/navrelposned | Type: ublox_msgs/msg/NavRELPOSNED9 | Count: 394 | Serialization Format: cdr<br> Topic: /rslidar_points | Type: sensor_msgs/msg/PointCloud2 | Count: 755 | Serialization Format: cdr<br> Topic: /tf | Type: tf2_msgs/msg/TFMessage | Count: 3021 | Serialization Format: cdr<br> Topic: /tf_static | Type: tf2_msgs/msg/TFMessage | Count: 1 | Serialization Format: cdr<br> <br> </p> <p> </p> <p> </p>
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