Abstract
A real-world reconstruction from a computer graphics tool is one of the main issues in two different communities: robotics and artificial intelligence, both of them under different points of view such as computer science, perception and machine vision. A real scene can be reconstructed by generating of point clouds with the help of depth sensors, rotational elements and mathematical transformations according to the mechanical design. This paper presents the development of a three-dimensional laser range finder based on a two-dimensional laser scanner Hokuyo URG-04LX-UG01 and a step motor. The design and kinematic model of the system to generate 3D point clouds are presented with an experimental acquisition algorithm implemented on Robotic Operative System ROS in Python language. The quality of the generated reconstruction is improved with a calibration algorithm based on a model parameter optimization from a reference surface, the results from the calibrated model were compared with a commercial low-cost device. The concurrent application of the system permits the viewing of the scene from different perspectives. The output files can be easily visualized with Python or MATLAB and used for surface reconstruction, scene classification or mapping. In this way, typical robotic tasks can be realized, highlighting autonomous navigation, collision avoidance, grasp calculation and handling of objects.
Supported by Universidad de Ibagué.
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Acknowledgments
This research is being developed with the partial support of the “Gobernación del Tolima” under “Proyecto Talento Humano” - Research Culture. The results presented in this paper have been obtained with the assistance of students from the Research Hotbed on Robotics (SI2C), Research Group D+TEC, Universidad de Ibagué, Ibagué-Colombia.
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Murcia, H.F., Monroy, M.F., Mora, L.F. (2018). 3D Scene Reconstruction Based on a 2D Moving LiDAR. In: Florez, H., Diaz, C., Chavarriaga, J. (eds) Applied Informatics. ICAI 2018. Communications in Computer and Information Science, vol 942. Springer, Cham. https://doi.org/10.1007/978-3-030-01535-0_22
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