Abstract
This paper presents a prototype system using a commercially available drone for semi-autonomous point cloud mapping, for which post-processing of data is of key importance to achieve sufficient accuracy. The focus of this work is to improve the result by updating the software for drone control and post-processing. The mapping is done by utilizing a mobile app that initiates a drone movement such as to move one meter forward or 90\(^\circ \) rotation. There is also an option to utilize the ultrasonic sensors and rotate 360\(^\circ \), in increments, to map the surroundings area. 3D vertices retrieved from the mapping is stored into a database. A Python app is later utilized to retrieve mapped vertices where post-processing and visualization methods are applied. The result can be stored as a file and can be imported into other software for further processing. The primary value of the proposed solution is that it is a very cost-effective method for mapping spaces in 3D and then generating a mesh for further processing.
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Acknowledgement
We would like to acknowledge professor Kåre Synnes and the institution of Luleå University of Technology for the guidance and the economic support regarding hardware, aircraft pilot license and facilities to work in. They have played an important role in making this project possible.
We would also like to thank the co-authors of our last paper, Erik Sörensen and Antonio Saldaña, for the help in laying the foundation on which this paper was built.
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Johan Olsson - Author, de-noise and drone controller.
Hugo Pettersson - Author, DBSCAN clustering and drone controller.
Dennis Trollsfjord - Author, mesh generation and drone controller.
Kåre Synnes - Study guidance and paper review.
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Olsson, J., Pettersson, H., Trollsfjord, D., Synnes, K. (2023). Semi-autonomous Point Cloud Mapping and Post-processing of Data. In: Bravo, J., Ochoa, S., Favela, J. (eds) Proceedings of the International Conference on Ubiquitous Computing & Ambient Intelligence (UCAmI 2022). UCAmI 2022. Lecture Notes in Networks and Systems, vol 594. Springer, Cham. https://doi.org/10.1007/978-3-031-21333-5_51
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DOI: https://doi.org/10.1007/978-3-031-21333-5_51
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