A 3D LiDAR Dataset of ITU Heterogeneous Robot Team

Aybakan, Abdulbaki; Haddeler, Garen; Akay, Mehmet Caner; Ervan, Osman; Temeltaş, Hakan

doi:10.1145/3373724.3373734

Cited by 6 publications

(3 citation statements)

References 16 publications

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“…For example, one study developed the feature detection with the Millimeter-Wave (MMW) radar on the top of the Husky A200 and implemented a filter named the Probability Hypothesis Density (PHD) with the SLAM (Adams et al, 2014;Dissanayake et al, 2001). Another study was conducted to collect the data with an Xsens MTi-3 IMU and Velodyne VLP-16 LiDAR after installing the sensors on the top of the HUSKY A200 (Aybakan et al, 2019).…”

Section: Husky A200mentioning

confidence: 99%

Uncrewed Ground Vehicles (UGVs) and Nature-Inspired Designed Robot DIGIT and SPOT: A Review

Muktadir

Hamoush

et al. 2022

American Journal of Engineering and Applied Sciences

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Researchers around the world are developing autonomous robot systems to assist humans with tedious, hazardous, complex, and timeconsuming tasks. Today, autonomous robots are used for a variety of applications, ranging from delivering food to operating on patients. A robotic system can be operated with or without human assistance, depending on whether it is manned or unmanned. Additionally, the robot may be classified as an Unmanned Aerial Vehicle (UAV), Unmanned Surface Vehicle (USV), or Unmanned Ground Vehicle (UGV), depending on its operational conditions. An autonomous robot may be equipped with manipulators and advanced sensors, such as a LiDAR camera, which can collect 3D spatial data. The present study reviews recent research and development of unmanned ground vehicles, including two nature-inspired designed robots called SPOT and DIGIT and presents its technical data. In addition, this study introduces the challenges and applications of these robots. A novel type of comparison was made in this study between some UGVs, human and animalinspired designed robots, where researchers were able to get an idea of different robot basic specifications that could be used to select these robots for future research. As a result of this study, two more studies will be conducted in the future.

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Section: Husky A200mentioning

confidence: 99%

Uncrewed Ground Vehicles (UGVs) and Nature-Inspired Designed Robot DIGIT and SPOT: A Review

Muktadir

Hamoush

et al. 2022

American Journal of Engineering and Applied Sciences

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“…Most datasets for UGVs on outdoor environments offer real tridimensional (3D) data for autonomous driving on roadways [ 4 , 5 ], but also for Simultaneous Localization and Mapping (SLAM) [ 6 , 7 ], direct motion control [ 8 ], precision agriculture [ 9 , 10 ], planetary exploration [ 11 , 12 ] or Search and Rescue (SAR) [ 13 ]. These datasets usually provide the vehicle pose ground-truth, and, increasingly, they also include tagged exteroceptive data from range and image sensors [ 14 , 15 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the open-source Robot Operating System (ROS) [ 33 ] has been integrated into Gazebo to mimic the same software of the mobile robot [ 28 ]. ROS also provides tools to easily record and replay sensor data using bag files [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Automatically Annotated Dataset of a Ground Mobile Robot in Natural Environments via Gazebo Simulations

Sánchez

Morales

Martínez

et al. 2022

Sensors

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This paper presents a new synthetic dataset obtained from Gazebo simulations of an Unmanned Ground Vehicle (UGV) moving on different natural environments. To this end, a Husky mobile robot equipped with a tridimensional (3D) Light Detection and Ranging (LiDAR) sensor, a stereo camera, a Global Navigation Satellite System (GNSS) receiver, an Inertial Measurement Unit (IMU) and wheel tachometers has followed several paths using the Robot Operating System (ROS). Both points from LiDAR scans and pixels from camera images, have been automatically labeled into their corresponding object class. For this purpose, unique reflectivity values and flat colors have been assigned to each object present in the modeled environments. As a result, a public dataset, which also includes 3D pose ground-truth, is provided as ROS bag files and as human-readable data. Potential applications include supervised learning and benchmarking for UGV navigation on natural environments. Moreover, to allow researchers to easily modify the dataset or to directly use the simulations, the required code has also been released.

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