Abstract
3D LiDaR-based perception has been used in robotics for obtaining accurate representations of the robot surroundings. As pipes are one of the most common objects in industrial environments, cylinder detection systems provide valuable information for robot navigation in industrial scenarios. However, most cylinder detection approaches using 3D LiDaRs suffer from high computational requirements and, therefore, their on-line implementation on real robots is limited for certain applications. This work proposes a computationally-light probabilistic approach to pipe detection and tracking suitable for aerial robot mapping and navigation. The proposed method was tested in both simulation and real scenarios. Through a combination of previous estimates and the localisation of the robot, the proposed approach is capable of reducing the computational cost of the RANSAC algorithm while keeping high detection accuracy.
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References
Ahmed, M.F., Haas, C.T., Haas, R.: Automatic detection of cylindrical objects in built facilities. J. Comput. Civil Eng. 28(3), 04014009 (2014)
Caballero, A., Suarez, A., Real, F., Vega, V.M., Bejar, M., Rodriguez-Castaño, A., Ollero, A.: First experimental results on motion planning for transportation in aerial long-reach manipulators with two arms. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 8471–8477. IEEE (2018)
Chaperon, T., Goulette, F.: Extracting cylinders in full 3D data using a random sampling method and the Gaussian image. In: Vision Modeling and Visualization Conference 2001 (VMV-01) (2001)
Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24(6), 381–395 (1981)
Guerra, E., Munguía, R., Grau, A.: UAV visual and laser sensors fusion for detection and positioning in industrial applications. Sensors 18(7), 2071 (2018)
Kumar, G.A., Patil, A., Patil, R., Park, S., Chai, Y.: A LiDAR and IMU integrated indoor navigation system for UAVs and its application in real-time pipeline classification. Sensors 17(6), 1268 (2017)
Paneque, L., Martínez-de Dios, J., Ollero, A.: Multi-sensor 6-DoF localization for aerial robots in complex GNSS-denied environments. In: 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE (2019)
Liu, Y.J., Zhang, J.B., Hou, J.C., Ren, J.C., Tang, W.Q.: Cylinder detection in large-scale point cloud of pipeline plant. IEEE Trans. Visual Comput. Graphics 19(10), 1700–1707 (2013)
Nguyen, A., Le, B.: 3D point cloud segmentation: a survey. In: 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), pp. 225–230. IEEE (2013)
Ollero, A., Heredia, G., Franchi, A., Antonelli, G., Kondak, K., Sanfeliu, A., Viguria, A., Martinez-De Dios, J.R., Pierri, F., Cortés, J., et al.: The AEROARMS project: aerial robots with advanced manipulation capabilities for inspection and maintenance. IEEE Rob. Autom. Mag. (2018)
Qiu, R., Zhou, Q.Y., Neumann, U.: Pipe-run extraction and reconstruction from point clouds. In: European Conference on Computer Vision, pp. 17–30. Springer (2014)
Real, F., Torres-Gonzalez, A., Ramon Soria, P., Capitan, J., Ollero, A.: UAL: an abstraction layer for unmanned vehicles. In: 2nd International Symposium on Aerial Robotics (ISAR) (2018)
Suarez, A., Sanchez-Cuevas, P., Fernandez, M., Perez, M., Heredia, G., Ollero, A.: Lightweight and compliant long reach aerial manipulator for inspection operations. In: 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 6746–6752. IEEE (2018)
Szeliski, R.: Computer Vision: Algorithms and Applications. Springer Science & Business Media, London (2010)
Zhang, K., Kovac, M., Chermprayong, P., Xiao, F.: An autonomous aerial robot with integrated delta manipulator for aerial repair. IEEE Rob. Autom. Mag. (2019)
Acknowledgements
This work was supported by the European Research Council as part of GRIFFIN ERC Advanced Grant 2017 (Action 788247), the H2020 AEROARMS project under contract 644271, and the ARM-EXTEND project funded by the Spanish National R&D plan.
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Gómez Eguíluz, A., Paneque, J.L., Martínez-de Dios, J.R., Ollero, A. (2020). Online Detection and Tracking of Pipes During UAV Flight in Industrial Environments. In: Silva, M., Luís Lima, J., Reis, L., Sanfeliu, A., Tardioli, D. (eds) Robot 2019: Fourth Iberian Robotics Conference. ROBOT 2019. Advances in Intelligent Systems and Computing, vol 1092. Springer, Cham. https://doi.org/10.1007/978-3-030-35990-4_3
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DOI: https://doi.org/10.1007/978-3-030-35990-4_3
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