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

Abstract

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.

Reinforcement and Curriculum Learning for Off-Road Navigation of an UGV with a 3D LiDAR

“…Nevertheless, finding suitable data to train these algorithms is not always easy, as they usually require manual tagging, which is a slow and error-prone process. To overcome this problem, synthetic datasets have been published, where data is directly acquired from a robotic simulator [ 26 ], making it free of labeling errors and reducing human labor.…”

Waypoint Generation in Satellite Images Based on a CNN for Outdoor UGV Navigation

“…Gazebo is an open-source 3D robotics simulator with integrated physics engine [26] that allows the development of realistic models of complex environments [25]. In this way, it has been used as a simulation environment for technological challenges [27].…”

“…Reliable CNN training requires a lot of images labelled pixel by pixel as input, which can be available in public datasets [21,22]. Synthetic data are a relevant alternative for training both traditional machine [23] and deep learning methods [24] because ground truth data can be labelled automatically, which avoids tedious and error-prone manual or assisted tagging [25].…”

Simulation and Implementation of RSCUAD Walking Robot Based on ROS and Gazebo Simulator

“…In the virtual robot, a configuration is made according to the ROS protocol to enable the virtual robot to run in the Gazebo simulator [12]- [14]. In this research, the program flow is designed as shown in Fig.…”