Bioinspired vision-only UAV attitude rate estimation using machine learning

This paper presents a bioinspired system for attitude rate estimation using visual sensors for aerial vehicles. The sensorial system consists of three small low-resolution cameras (10×8 pixels), and is based on insect ocelli, a set of three simple eyes related to flight stabilization. Most previous approaches inspired by the ocellar system use model-based techniques and consider different assumptions, like known light source direction. Here, a learning approach is employed, using Artificial Neural Networks, in which the system is trained to recover the angular rates in different illumination scenarios with unknown light source direction. We present a study using real data in an indoor setting, in which we evaluate different network architectures and inputs.

[1]  Yann LeCun,et al.  A multirange architecture for collision-free off-road robot navigation , 2009 .

[2]  Dario Floreano,et al.  Fly-inspired visual steering of an ultralight indoor aircraft , 2006, IEEE Transactions on Robotics.

[3]  Roland Siegwart,et al.  Real-time onboard visual-inertial state estimation and self-calibration of MAVs in unknown environments , 2012, 2012 IEEE International Conference on Robotics and Automation.

[4]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[5]  Anastasios I. Mourikis,et al.  High-precision, consistent EKF-based visual-inertial odometry , 2013, Int. J. Robotics Res..

[6]  Gaurav S. Sukhatme,et al.  Combined optic-flow and stereo-based navigation of urban canyons for a UAV , 2005, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[7]  Roland Siegwart,et al.  Monocular‐SLAM–based navigation for autonomous micro helicopters in GPS‐denied environments , 2011, J. Field Robotics.

[8]  Stéphane Viollet,et al.  A biomimetic vision-based hovercraft accounts for bees’ complex behaviour in various corridors , 2014, Bioinspiration & biomimetics.

[9]  Dario Floreano,et al.  Miniature curved artificial compound eyes , 2013, Proceedings of the National Academy of Sciences.

[10]  Aníbal Ollero,et al.  Vision-Based Odometry and SLAM for Medium and High Altitude Flying UAVs , 2009, J. Intell. Robotic Syst..

[11]  M. Mizunami,et al.  Functional diversity of neural organization in insect ocellar systems , 1995, Vision Research.

[12]  Jürgen Schmidhuber,et al.  A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots , 2016, IEEE Robotics and Automation Letters.

[13]  J. Chahl,et al.  Biomimetic Attitude and Orientation Sensors , 2012, IEEE Sensors Journal.

[14]  Kevin Y. Ma,et al.  Controlling free flight of a robotic fly using an onboard vision sensor inspired by insect ocelli , 2014, Journal of The Royal Society Interface.

[15]  Davide Scaramuzza,et al.  REMODE: Probabilistic, monocular dense reconstruction in real time , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[16]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[17]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[18]  Francesc Moreno-Noguer,et al.  On-board real-time pose estimation for UAVs using deformable visual contour registration , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[19]  Heinrich H. Bülthoff,et al.  Behavior-oriented vision for biomimetic flight control , 2002 .

[20]  James Sean Humbert,et al.  Bio-inspired modeling and implementation of the ocelli visual system of flying insects , 2014, Biological Cybernetics.

[21]  Robert J. Wood,et al.  Science, technology and the future of small autonomous drones , 2015, Nature.

[22]  Vijay Kumar,et al.  Opportunities and challenges with autonomous micro aerial vehicles , 2012, Int. J. Robotics Res..

[23]  Morgan Quigley,et al.  ROS: an open-source Robot Operating System , 2009, ICRA 2009.

[24]  Aníbal Ollero,et al.  Vision-based multi-UAV position estimation , 2006, IEEE Robotics & Automation Magazine.