Abstract
For small and low-cost UAVs, real-time autonomous obstacle avoidance is a challenging problem. Due to the weight limit of carrying additional sensors such as radar, laser, and etc., vision-based autonomous obstacle avoidance has become a popular trend for small drones. In the past, several studies have used vision-based approaches for the three-dimensional reconstruction or depth estimation in obstacle avoidance. In this paper, the concepts of emulating human behavior to avoid an obstacle is used in the experiments. Two photos can be compared by detecting and matching the features with the principle of size expansion when an obstacle is approaching, and the matched features can construct a convex hull. This convex hull is treated as an obstacle which needs to be avoided. This method is fast and effective. In this study, several feature detection and matching algorithms are compared by experiments, and constructed convex hulls are used to compare the accuracy of obstacle detection. The larger the overlapped region between the convex hull and real object, the better the obstacle detection accuracy. This study analyzes and summarizes the experimental results to find which algorithm could obtain better accuracy and computation time, i.e. which could be more appropriate to utilized in UAV vision-based obstacle avoidance.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Potena, C., Nardi, D., Pretto, A.: Joint vision-based navigation, control and obstacle avoidance for UAVs in dynamic environments. In: Proceedings of the 2019 European Conference on Mobile Robots (ECMR) (2019)
Lu, Y., Xue, Z., Xia, G.-S., Zhang, L.: A survey on vision-based UAV navigation. Geo-spatial Inf. Sci. 21(1), 21–32 (2018)
De Croon, G., de Weerdt, E., De Wagter, C., Remes, B.: The appearance variation cue for obstacle avoidance. In: Proceedings of the 2010 IEEE International Conference on Robotics and Biomimetics (ROBIO), Tianjin, China, 14–18 December 2010, pp. 1606–1611 (2010)
Mori, T., Scherer, S.: First results in detecting and avoiding frontal obstacles from a monocular camera for micro unmanned aerial vehicles. In: Proceedings of the 2013 IEEE International Conference on Robotics and Automation (ICRA), Karlsruhe, Germany, 6–10 May 2013, pp. 1750–1757 (2013)
Hrabar, S.: 3D path planning and stereo-based obstacle avoidance for rotorcraft UAVs. In: IROS (2008)
Na, I., Han, N.S., Jeong, H.: Stereo-based road obstacle detection and tracking. In: ICACT (2011)
Merz, T., Kendoul, F.: Beyond visual range obstacle avoidance and infrastructure inspection by an autonomous helicopter. In: International Conference on Intelligent Robots and Systems (IROS), p. 187217 (2011)
Bachrach, A., Prentice, S., He, R., Roy, N.: Range - robust autonomous navigation in GPS-denied environments. J. Field Robot. 28(5), 644666 (2011)
Beyeler, A., Zufferey, J.-C., Floreano, D.: 3D vision-based navigation for indoor microflflyers. In: Proceedings 2007 IEEE International Conference on Robotics and Automation, pp. 1336–1341. IEEE (2007)
Green, W., Oh, P.: Optic-flow-based collision avoidance. IEEE Robot. Autom. Mag. 15(1), 96–103 (2008)
Al-Kaff, A., Meng, Q.: Monocular vision-based obstacle detection/avoidance for unmanned aerial vehicles. In: 2016 IEEE Intelligent Vehicles Symposium (IV) Gothenburg, Sweden, 19–22 June 2016 (2016)
Al-Kaff, A., Garcí, F.: Obstacle detection and avoidance system based on monocular camera and size expansion algorithm for UAVs. Sensors 17, 1061 (2017)
Acknowledgement
This work is supported by the Fujian Provincial Natural Science Foundation in China (Project Number: 2017J01730) and the Education Department of Fujian Province (Project Number: GY-Z19005).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Sung, TW., Zhao, B., Chang, KC. (2020). Experimental Comparison of Different Feature Detection Algorithms for UAV Obstacle Avoidance. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_79
Download citation
DOI: https://doi.org/10.1007/978-3-030-44289-7_79
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-44288-0
Online ISBN: 978-3-030-44289-7
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)