Dataset for classifying and estimating the position, orientation, and dimensions of a list of primitive objects

Abstract

Abstract Objectives Robotic systems are moving toward more interaction with the environment, which requires improving environmental perception methods. The concept of primitive objects simplified the perception of the environment and is frequently used in various fields of robotics, significantly in the grasping challenge. After reviewing the related resources and datasets, we could not find a suitable dataset for our purpose, so we decided to create a dataset to train deep neural networks to classify a primitive object and estimate its position, orientation, and dimensions described in this report. Data description This dataset contains 8000 virtual data for four primitive objects, including sphere, cylinder, cube, and rectangular sheet with dimensions between 10 to 150 mm, and 200 real data of these four types of objects. Real data are provided by Intel Realsense SR300 3D camera, and virtual data are generated using the Gazebo simulator. Raw data are generated in.pcd format in both virtual and real types. Data labels include values of the object type and its position, orientation, and dimensions.

Keywords

• Object detection dataset
• Object dimensions estimation
• Orientation detection
• Pose detection
• Primitive object
• Sim-to-real transfer