DOI: 10.5281/zenodo.7704678
title: FRUC multiple sensor forest dataset including absolute, map-referenced localization

description: <strong>FRUC Datasets (Forest environment dataset)</strong> This dataset was collected as part of the work conducted by the Forestry Robotics @ University of Coimbra team (https://www.youtube.com/@forestryroboticsuc; part of the Institute of Systems and Robotics, https://www.isr.uc.pt/) within the scope of the Safety, Exploration and Maintenance of Forests with Ecological Robotics (SEMFIRE, ref. CENTRO-01-0247-FEDER-03269; http://semfire.ingeniarius.pt/) and the Semi-Autonomous Robotic System for Forest Cleaning and Fire Prevention (SafeForest, CENTRO-01-0247-FEDER-045931) research projects. Its purpose is to allow researchers in forestry robotics to have an in-depth analysis of a florests environment; obtain an a priori map for robot operations (e.g. path plannning, landscaping, etc…) and to train segmentation algorithms; The dataset in question includes data from multiple sensors and absolute, map-referenced localization which can be used to register the sensor data to a fixed coordinate system. It was collected at the Choupal National Woods, Coimbra, Portugal (40<sup>◦</sup>13′13.3′′N;8<sup>◦</sup>26′38.1′′W). The dataset was collected during a partly clouded day in a forest environment by performing <strong>two circular loop</strong> laps amounting to a total distance of approximately <strong>800m,</strong> with a total duration of <strong>24 minutes and 26 seconds</strong>. The scenario is rich in features relevant to forestry robotics applications, including trees, bushes, tree trunks, etc. To better handle the multimodal nature of the acquired data, the dataset is bundled into rosbags, a file format used by the ROS (Robot Operating System) to record and play back data. <strong>More specifically, the datasets include:</strong> <strong>RGB Images</strong> from an Intel Realsense D435i Aligned <strong>Depth Images</strong> from an Intel Realsense D435i <strong>Point Clouds</strong> from a Livox Mid-70 LiDAR Unfiltered <strong>acceleration, gyroscopic and magnetic</strong> data from a Xsens MTi IMU Unfiltered <strong>acceleration, gyroscopic </strong>data from an Intel Realsense D435i <strong>Description of files:</strong> The dataset was split into three rosbags (<strong>choupal_0.bag</strong>, <strong>choupal_1.bag, choupal_2.bag</strong>). The <strong>rosbag_info_[x].txt </strong>contains the information of each rosbag; The <strong>sensor_box.urdf </strong>contains all the required transforms; The <strong>sensor_box.stl</strong> contains the 3D model of the apparatus; The <strong>choupal.launch </strong>publishes the sensor transforms and plays the dataset; The <strong>localization.bag</strong> contains the final graph of poses extracted with RTAB-Map republished as nav_msgs/odom. These poses are more accurate than the <strong>localization_with discrete_jumps.bag</strong> but are recorded at a lower frequency; The <strong>localization_with discrete_jumps.bag</strong> contains a map-referenced localization extracted with RTAB-Map, containing more entries than <strong>localization.bag </strong>but it contains discrete jumps (not ideal for pure mapping libraries). <strong>Usage:</strong> Extract the <em>fruc_dataset_choupal_launch.zip </em>into a catkin workspace Copy the <strong>rosbags </strong>into the <em>fruc_dataset_choupal_launch/rosbag/</em> Edit the <em>fruc_dataset_choupal_launch/launch/choupal.launch </em>file to your use case: Change the <em>file_path </em>argument if the rosbags are not in the default location; Set <em>enable_localization</em> to <em>true </em>in order to use the provided localization bags. Compile the package and source the environment: <pre><code class="language-bash">catkin_make [/your_catkin_workspace/]</code></pre> <pre><code class="language-bash">source [/your_catkin_workspace/devel/setup.bash]</code></pre> Launch the files: <pre><code class="language-bash">roslaunch fruc_dataset_choupal_launch choupal.launch</code></pre>
description: {"references": ["Crist\u00f3v\u00e3o, M\u00e1rio; Design of a Multi-Sensor Apparatus for Forestry Robotics: A case study on Forest 3D Mapping; Universidade de Coimbra, 2023"]}