Abstract
One of the most challenging tasks of UAV is to quickly and safely autonomous navigation and reconstruction in unknown and GNSS-denied environments. In this paper, we present a feasible design and development of a UAV system that is capable of autonomous navigation and rapid reconstruction in GNSS-denied environments. A multiple sensor information fusion based SLAM method is proposed to achieve UAV’s localization and reconstruction without GNSS reception, and a local path planner is then proposed based on A* and 3DVFH+ that iteratively searches for the optimal local path. Each subsystem is separately tested for their effectiveness, and comprehensive experiments are conducted to evaluate the performance of the presented system in GNSS-denied complex environments.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Qin, H., Meng, Z., Meng, W., Chen, X., Sun, H., Lin, F., Ang, M.H.: Autonomous exploration and mapping system using heterogeneous UAVs and UGVs in GPS-denied environments. IEEE Trans. Veh. Technol. 68(2), 1 (2018)
Motlagh, N.H., Bagaa, M., Taleb, T.: UAV-based IoT platform: a crowd surveillance use case. IEEE Commun. Maga. 55(2), 128–134 (2017)
Ham, Y., Han, K.K., Lin, J.J., Golparvar-Fard, M.: Visual monitoring of civil infrastructure systems via camera-equipped unmanned aerial vehicles (UAVs): a review of related works. Vis. Eng. 4(1), 1 (2016)
Mohta, K., Watterson, M., Mulgaonkar, Y., Liu, S., Qu, C., Makineni, A., Saulnier, K., Sun, K., Zhu, A., Delmerico, J., et al.: Fast, autonomous flight in GPS-denied and cluttered environments. J. Field Robot. 35(1), 101–120 (2018)
Leutenegger, S., Lynen, S., Bosse, M., Siegwart, R., Furgale, P.: Keyframe-based visual-inertial odometry using nonlinear optimization. Int. J. Robot. Res. 34(3), 314–334 (2014)
Cui, J.Q., Lai, S., Dong, X., Chen, B.M.: Autonomous navigation of UAV in foliage environment. J. Intell. Robot. Syst. 84(1–4), 259–276 (2016)
Zhang, J., Singh, S.: Laser-visual-inertial odometry and mapping with high robustness and low drift. J. Field Robot. 35(5), 1242–1264 (2018)
Zhang, J., Singh, S.: Loam: lidar odometry and mapping in real-time. In: Robotics: Science and Systems, vol. 2, p. 9 (2014)
Labbé, M., Michaud, F.: RTAB-Map as an open-source lidar and visual simultaneous localization and mapping library for large-scale and long-term online operation. J. Field Robot. 36(2), 416–446 (2019)
Whelan, T., Salas-Moreno, R.F., Glocker, B., Davison, A.J., Leutenegger, S.: Elasticfusion: real-time dense slam and light source estimation. Int. J. Robot. Res. 35(14), 1697–1716 (2016)
Dai, A., Nießner, M., Zollhöfer, M., Izadi, S., Theobalt, C.: Bundlefusion: real-time globally consistent 3d reconstruction using on-the-fly surface reintegration. ACM Trans. Gr. 36(4), 1 (2017)
Park, M.G., Jeon, J.H., Lee, M.C.: Obstacle avoidance for mobile robots using artificial potential field approach with simulated annealing. In: 2001 IEEE International Symposium on Industrial Electronics Proceedings, vol. 3, pp. 1530–1535. IEEE (2001)
Borenstein, J.: The vector field histogram-fast obstacle avoidance for mobile robots. IEEE Trans. Syst. Man Cybernet. 19, 1179–1187 (1989)
Ulrich, I., Borenstein, J.: Vfh*: local obstacle avoidance with look-ahead verification. In: 2000 IEEE International Conference on Robotics and Automation, vol. 3, pp. 2505–2511. IEEE (2000)
Whelan, T., Johannsson, H., Kaess, M., Leonard, J.J., McDonald, J.: Robust real-time visual odometry for dense RGB-D mapping. In: 2013 IEEE International Conference on Robotics and Automation, pp. 5724–5731. IEEE (2013)
Vanneste, S., Bellekens, B., Weyn, M.: 3dvfh+: real-time three-dimensional obstacle avoidance using an octomap. In: MORSE 2014 Model-Driven Robot Software Engineering: Proceedings of the 1st International Workshop on Model-Driven Robot Software Engineering co-located with International Conference on Software Technologies: Applications and Foundations (STAF 2014), vol. 1319, pp. 91–102 (2014)
Acknowledgements
This work is supported by the National Natural Science Foundation of China (61803309, 61703343), Fundamental Research Funds for the Central Universities (3102019ZDHKY02, 3102018JCC003). Natural Science Foundation of Shaanxi Province (2018JQ6070, 2019JM-254), China Postdoctoral Science Foundation (2018M633574) and Key Research and Development Project of Shaanxi Province (2020ZDLGY06-02).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, Z. et al. (2022). High-Accuracy Robust SLAM and Real-Time Autonomous Navigation of UAV in GNSS-denied Environments. In: Yan, L., Duan, H., Yu, X. (eds) Advances in Guidance, Navigation and Control . Lecture Notes in Electrical Engineering, vol 644. Springer, Singapore. https://doi.org/10.1007/978-981-15-8155-7_92
Download citation
DOI: https://doi.org/10.1007/978-981-15-8155-7_92
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-8154-0
Online ISBN: 978-981-15-8155-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)