Abstract
Autonomous flight within a forest canopy represents a key challenge for generalised scene understanding on-board a future Unmanned Aerial Vehicle (UAV) platforms. Here we present an approach for automatic trail navigation within such an unstructured environment that successfully generalises across differing image resolutions - allowing UAV with varying sensor payload capabilities to operate equally in such challenging environmental conditions. Specifically, this work presents an optimised deep neural network architecture, capable of state-of-the-art performance across varying resolution aerial UAV imagery, that improves forest trail detection for UAV guidance even when using significantly low resolution images that are representative of low-cost search and rescue capable UAV platforms.
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
Apvrille, L., Tanzi, T., Dugelay, J.L.: Autonomous drones for assisting rescue services within the context of natural disasters. In: General Assembly and Scientific Symposium, pp. 1–4. IEEE (2014)
Câmara, D.: Cavalry to the rescue: drones fleet to help rescuers operations over disasters scenarios. In: Conference on Antenna Measurements & Applications, pp. 1–4. IEEE (2014)
Christiano, P., et al.: Transfer from simulation to real world through learning deep inverse dynamics model. arXiv preprint arXiv:1610.03518 (2016)
Dietrich, J.T.: Riverscape mapping with helicopter-based structure-from-motion photogrammetry. Geomorphology 252, 144–157 (2016)
Gandhi, D., Pinto, L., Gupta, A.: Learning to fly by crashing. arXiv preprint arXiv:1704.05588 (2017)
Gaszczak, A., Breckon, T.P., Han, J.: Real-time people and vehicle detection from UAV imagery. In: Intelligent Robots and Computer Vision XXVIII: Algorithms and Techniques, vol. 7878, pp. 78780B. International Society for Optics and Photonics (2011)
Giusti, A., et al.: A machine learning approach to visual perception of forest trails for mobile robots. IEEE Robot. Autom. Lett. 1(2), 661–667 (2016)
He, K., Zhang, X., Ren, S., Sun, J.: Spatial pyramid pooling in deep convolutional networks for visual recognition. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 346–361. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_23
Kelchtermans, K., Tuytelaars, T.: How hard is it to cross the room?-training (recurrent) neural networks to steer a UAV. arXiv preprint arXiv:1702.07600 (2017)
Koh, L.P., Wich, S.A.: Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Trop. Conserv. Sci. 5(2), 121–132 (2012)
Mueller, M., Casser, V., Smith, N., Ghanem, B.: Teaching UAV to race using UE4Sim. arXiv preprint arXiv:1708.05884 (2017)
Paneque-Gálvez, J., McCall, M.K., Napoletano, B.M., Wich, S.A., Koh, L.P.: Small drones for community-based forest monitoring: an assessment of their feasibility and potential in tropical areas. Forests 5(6), 1481–1507 (2014)
Puri, A.: A survey of unmanned aerial vehicles (UAV) for traffic surveillance. Department of computer science and engineering, University of South Florida, pp. 1–29 (2005)
Rémy, G., Senouci, S.-M., Jan, F., Gourhant, Y.: Sar drones: drones for advanced search and rescue missions. Journées Nationales des Communications dans les Transports 1, 1–3 (2013)
Smolyanskiy, N., Kamenev, A., Smith, J., Birchfield, S.: Toward low-flying autonomous MAV trail navigation using deep neural networks for environmental awareness. In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Vancouver, BC, pp. 4241–4247 (2017)
Tai, L., Liu, M.: Deep-learning in mobile robotics-from perception to control systems: a survey on why and why not. arXiv preprint arXiv:1612.07139 (2016)
Yoo J, Hong Y, Yoon S.: Autonomous UAV navigation with domain adaptation. arXiv preprint arXiv:1712.03742 (2017)
Zhang, T., Kahn, G., Levine, S., Abbeel, P.: Learning deep control policies for autonomous aerial vehicles with MPC-guided policy search. In: 2016 International Conference on Robotics and Automation 2016, pp. 528–535. IEEE (2016)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Maciel-Pearson, B.G., Carbonneau, P., Breckon, T.P. (2018). Extending Deep Neural Network Trail Navigation for Unmanned Aerial Vehicle Operation Within the Forest Canopy. In: Giuliani, M., Assaf, T., Giannaccini, M. (eds) Towards Autonomous Robotic Systems. TAROS 2018. Lecture Notes in Computer Science(), vol 10965. Springer, Cham. https://doi.org/10.1007/978-3-319-96728-8_13
Download citation
DOI: https://doi.org/10.1007/978-3-319-96728-8_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-96727-1
Online ISBN: 978-3-319-96728-8
eBook Packages: Computer ScienceComputer Science (R0)