Data

ForestTrav ROS Bag Files - OHM maps and robot transforms

Commonwealth Scientific and Industrial Research Organisation
Ruetz, Fabio ; Lawrance, Nicholas ; Borges, Paulo ; Hernandez, Emili ; Peynot, Thierry
Viewed: [[ro.stat.viewed]] Cited: [[ro.stat.cited]] Accessed: [[ro.stat.accessed]]
ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/44jv-n743&rft.title=ForestTrav ROS Bag Files - OHM maps and robot transforms&rft.identifier=https://doi.org/10.25919/44jv-n743&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=This data set contains recordings of field robots traversing vegetated environments. Data are recorded as rosbags -- the standard data recording format of the Robot Operating System (ROS), an open-source middleware for robotics. Each bag file contains the topics relevant to reproduce results or train new models from the related ForestTrav open-source code base (journal publication and code repository linked below in Related Links).The included topics in each bag file are:Topic |Message type |Data description--------/r*/ohm| sensor_msgs/PointCloud2| Ohm point cloud data/r*/tf| tf/tfMessage| Robot transform/r*/tf_static| tf2_msgs/TFMessage| Static robot transforms/[robot]/logging_state| std_msgs/UInt8| Collision state/[robot]/logging_trigger| std_msgs/UInt8| Collision trigger stateMessages are timestamped via standard ROS protocols. ROS bag files can be read using ROS tools. Example commands can be found here: https://wiki.ros.org/rosbag/Tutorials/reading%20msgs%20from%20a%20bag%20fileLineage: The data was recorded in situ on a robotic platform using a lidar-IMU perception pack, processed using CSIRO's Wildcat SLAM navigation&rft.creator=Ruetz, Fabio &rft.creator=Lawrance, Nicholas &rft.creator=Borges, Paulo &rft.creator=Hernandez, Emili &rft.creator=Peynot, Thierry &rft.date=2025&rft.edition=v2&rft.relation=https://doi.org/10.1109/ACCESS.2024.3373004&rft_rights=Creative Commons Attribution 4.0 International Licence https://creativecommons.org/licenses/by/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO, Queensland University of Technology (QUT) 2025.&rft_subject=Robotics&rft_subject=Traversability Estimation&rft_subject=Robot data&rft_subject=Field robotics&rft_subject=Control engineering, mechatronics and robotics&rft_subject=ENGINEERING&rft.type=dataset&rft.language=English Access the data

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Creative Commons Attribution 4.0 International Licence
https://creativecommons.org/licenses/by/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO, Queensland University of Technology (QUT) 2025.

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This data set contains recordings of field robots traversing vegetated environments. Data are recorded as rosbags -- the standard data recording format of the Robot Operating System (ROS), an open-source middleware for robotics. Each bag file contains the topics relevant to reproduce results or train new models from the related ForestTrav open-source code base (journal publication and code repository linked below in "Related Links").

The included topics in each bag file are:

Topic
|Message type
|Data description
--------

/r*/ohm
| sensor_msgs/PointCloud2
| Ohm point cloud data

/r*/tf
| tf/tfMessage
| Robot transform

/r*/tf_static
| tf2_msgs/TFMessage
| Static robot transforms

/[robot]/logging_state
| std_msgs/UInt8
| Collision state

/[robot]/logging_trigger
| std_msgs/UInt8
| Collision trigger state

Messages are timestamped via standard ROS protocols.

ROS bag files can be read using ROS tools. Example commands can be found here: https://wiki.ros.org/rosbag/Tutorials/reading%20msgs%20from%20a%20bag%20file
Lineage: The data was recorded in situ on a robotic platform using a lidar-IMU perception pack, processed using CSIRO's Wildcat SLAM navigation

Available: 2025-02-06

Data time period: 2020-06-01 to 2025-01-20

This dataset is part of a larger collection

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ACN 633 798 857