Learning monocular reactive UAV control in cluttered natural environments

Autonomous navigation for large Unmanned Aerial Vehicles (UAVs) is fairly straight-forward, as expensive sensors and monitoring devices can be employed. In contrast, obstacle avoidance remains a challenging task for Micro Aerial Vehicles (MAVs) which operate at low altitude in cluttered environments. Unlike large vehicles, MAVs can only carry very light sensors, such as cameras, making autonomous navigation through obstacles much more challenging. In this paper, we describe a system that navigates a small quadrotor helicopter autonomously at low altitude through natural forest environments. Using only a single cheap camera to perceive the environment, we are able to maintain a constant velocity of up to 1.5m/s. Given a small set of human pilot demonstrations, we use recent state-of-the-art imitation learning techniques to train a controller that can avoid trees by adapting the MAVs heading. We demonstrate the performance of our system in a more controlled environment indoors, and in real natural forest environments outdoors.

[1]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[2]  Dean Pomerleau,et al.  ALVINN, an autonomous land vehicle in a neural network , 2015 .

[3]  Stefan Schaal,et al.  Is imitation learning the route to humanoid robots? , 1999, Trends in Cognitive Sciences.

[4]  Jonathan P. How,et al.  Receding horizon control of autonomous aerial vehicles , 2002, Proceedings of the 2002 American Control Conference (IEEE Cat. No.CH37301).

[5]  Zijiang J. He,et al.  Perceiving distance accurately by a directional process of integrating ground information , 2004, Nature.

[6]  E. R. Davies,et al.  Machine vision - theory, algorithms, practicalities , 2004 .

[7]  James J. Kuffner,et al.  Planning 3-D Path Networks in Unstructured Environments , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[8]  Ashutosh Saxena,et al.  High speed obstacle avoidance using monocular vision and reinforcement learning , 2005, ICML.

[9]  David M. Bradley,et al.  Boosting Structured Prediction for Imitation Learning , 2006, NIPS.

[10]  Sebastian Scherer,et al.  Flying Fast and Low Among Obstacles , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[11]  Dah-Jye Lee,et al.  Two-frame structure from motion using optical flow probability distributions for unmanned air vehicle obstacle avoidance , 2010, Machine Vision and Applications.

[12]  Yann LeCun,et al.  Learning long‐range vision for autonomous off‐road driving , 2009, J. Field Robotics.

[13]  Arun K. Somani,et al.  Monocular vision SLAM for indoor aerial vehicles , 2009, 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[14]  James Sean Humbert,et al.  Implementation of wide-field integration of optic flow for autonomous quadrotor navigation , 2009, Auton. Robots.

[15]  Brett Browning,et al.  A survey of robot learning from demonstration , 2009, Robotics Auton. Syst..

[16]  Nicholas Roy,et al.  Autonomous Flight in Unknown Indoor Environments , 2009 .

[17]  Dario Floreano,et al.  Vision-based control of near-obstacle flight , 2009, Auton. Robots.

[18]  Horst Bischof,et al.  Motion estimation with non-local total variation regularization , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[19]  J. Andrew Bagnell,et al.  Efficient Reductions for Imitation Learning , 2010, AISTATS.

[20]  Ashutosh Saxena,et al.  Autonomous MAV flight in indoor environments using single image perspective cues , 2011, 2011 IEEE International Conference on Robotics and Automation.

[21]  Mandyam V Srinivasan,et al.  Visual control of navigation in insects and its relevance for robotics , 2011, Current Opinion in Neurobiology.

[22]  Sanjiv Singh,et al.  A cascaded method to detect aircraft in video imagery , 2011, Int. J. Robotics Res..

[23]  Geoffrey J. Gordon,et al.  A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning , 2010, AISTATS.

[24]  Vijay Kumar,et al.  Minimum snap trajectory generation and control for quadrotors , 2011, 2011 IEEE International Conference on Robotics and Automation.

[25]  Roland Siegwart,et al.  Onboard IMU and monocular vision based control for MAVs in unknown in- and outdoor environments , 2011, 2011 IEEE International Conference on Robotics and Automation.

[26]  Horst Bischof,et al.  Dense reconstruction on-the-fly , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[27]  Nicholas Roy,et al.  State estimation for aggressive flight in GPS-denied environments using onboard sensing , 2012, 2012 IEEE International Conference on Robotics and Automation.

[28]  Albert S. Huang,et al.  Estimation, planning, and mapping for autonomous flight using an RGB-D camera in GPS-denied environments , 2012, Int. J. Robotics Res..

[29]  Marc Pollefeys,et al.  Vision-based autonomous mapping and exploration using a quadrotor MAV , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[30]  Frank Dellaert,et al.  Saliency detection and model-based tracking: a two part vision system for small robot navigation in forested environment , 2012, Defense, Security, and Sensing.

[31]  Vijay Kumar,et al.  Towards a swarm of agile micro quadrotors , 2012, Robotics: Science and Systems.

[32]  C. Stolk.  The Radon transform , 2014 .