Imperial College London
Alternate

ProfessorChristos-SavvasBouganis

Faculty of Engineering, Department of Electrical and Electronic Engineering

Professor of Intelligent Digital Systems
 
 
 
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Contact

 

+44 (0)20 7594 6144christos-savvas.bouganis Website

 
 
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Location

 

904Electrical EngineeringSouth Kensington Campus

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Summary

 

Publications

Citation

BibTex format

@inproceedings{Kouris:2019:10.1109/IROS.2018.8594204,
author = {Kouris, A and Bouganis, C},
doi = {10.1109/IROS.2018.8594204},
publisher = {IEEE},
title = {Learning to fly by myself: a self-supervised CNN-based approach for autonomous navigation},
url = {http://dx.doi.org/10.1109/IROS.2018.8594204},
year = {2019}
}
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RIS format (EndNote, RefMan)

TY  - CPAPER
AB  - Nowadays, Unmanned Aerial Vehicles (UAVs)are becoming increasingly popular facilitated by their extensive availability. Autonomous navigation methods can act as an enabler for the safe deployment of drones on a wide range of real-world civilian applications. In this work, we introduce a self-supervised CNN-based approach for indoor robot navigation. Our method addresses the problem of real-time obstacle avoidance, by employing a regression CNN that predicts the agent's distance-to-collision in view of the raw visual input of its on-board monocular camera. The proposed CNN is trained on our custom indoor-flight dataset which is collected and annotated with real-distance labels, in a self-supervised manner using external sensors mounted on an UAV. By simultaneously processing the current and previous input frame, the proposed CNN extracts spatio-temporal features that encapsulate both static appearance and motion information to estimate the robot's distance to its closest obstacle towards multiple directions. These predictions are used to modulate the yaw and linear velocity of the UAV, in order to navigate autonomously and avoid collisions. Experimental evaluation demonstrates that the proposed approach learns a navigation policy that achieves high accuracy on real-world indoor flights, outperforming previously proposed methods from the literature.
AU  - Kouris,A
AU  - Bouganis,C
DO  - 10.1109/IROS.2018.8594204
PB  - IEEE
PY  - 2019///
TI  - Learning to fly by myself: a self-supervised CNN-based approach for autonomous navigation
UR  - http://dx.doi.org/10.1109/IROS.2018.8594204
UR  - http://hdl.handle.net/10044/1/69929
ER  -
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