Onboard Stereo Vision for Drone Pursuit or Sense and Avoid

We describe a new, on-board, short range perception system that enables micro aerial vehicles (MAVs) to detect, track, and follow or avoid nearby drones (within 2-20 meters) in GPS-denied environments. Each vehicle is able to sense its neighborhood and adapt its motion accordingly without use of centralized reasoning or inter-vehicle communication. To enable a lightweight, low power solution, on-board stereo cameras are used for detection and tracking with depth images, while a downward-looking camera and an inertial measurement unit are used to estimate the position of the observer without use of GPS. We illustrate the robustness and accuracy of this approach through real-time, outdoor leader-follower experiments with three quadrotors. Our experiments show that state-of-art trackers are far less robust in detection against cluttered background. This demonstrates that stereo vision is a highly effective approach to perception for safe navigation of multiple MAVs in close proximity.

[1]  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.

[2]  Bernard Ghanem,et al.  A Benchmark and Simulator for UAV Tracking , 2016, ECCV.

[3]  Andreas Geiger,et al.  Are we ready for autonomous driving? The KITTI vision benchmark suite , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Andreas Geiger,et al.  Efficient Large-Scale Stereo Matching , 2010, ACCV.

[5]  Peter I. Corke,et al.  Image processing algorithms for UAV "sense and avoid" , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[6]  H. Kuhn The Hungarian method for the assignment problem , 1955 .

[7]  Vincent Lepetit,et al.  Vision-based Unmanned Aerial Vehicle detection and tracking for sense and avoid systems , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[8]  Davide Scaramuzza,et al.  SVO: Fast semi-direct monocular visual odometry , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[9]  Heiko Hirschmüller,et al.  Stereo Processing by Semiglobal Matching and Mutual Information , 2008, IEEE Trans. Pattern Anal. Mach. Intell..

[10]  Vijay Kumar,et al.  Autonomous deployment of swarms of micro-aerial vehicles in cooperative surveillance , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[11]  Majid Mirmehdi,et al.  DS-KCF: a real-time tracker for RGB-D data , 2016, Journal of Real-Time Image Processing.

[12]  Rui Caseiro,et al.  High-Speed Tracking with Kernelized Correlation Filters , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  G. C. H. E. de Croon,et al.  Hear-and-Avoid for Micro Air Vehicles , 2010 .

[14]  Philip H. S. Torr,et al.  Staple: Complementary Learners for Real-Time Tracking , 2015, Computer Vision and Pattern Recognition.

[15]  Bernard Ghanem,et al.  Context-Aware Correlation Filter Tracking , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[17]  Dit-Yan Yeung,et al.  Visual Object Tracking for Unmanned Aerial Vehicles: A Benchmark and New Motion Models , 2017, AAAI.

[18]  Guanghui Wang,et al.  Vision-Based Real-Time Aerial Object Localization and Tracking for UAV Sensing System , 2017, IEEE Access.

[19]  Eleni I. Vlahogianni,et al.  Unmanned Aerial Aircraft Systems for Transportation Engineering: Current Practice and Future Challenges , 2016 .

[20]  Zhe,et al.  The Visual Object Tracking VOT2015 Challenge Results , 2015, 2015 IEEE International Conference on Computer Vision Workshop (ICCVW).

[21]  Vijay Kumar,et al.  Design of small, safe and robust quadrotor swarms , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).

[22]  Miguel A. Olivares-Méndez,et al.  Robust real-time vision-based aircraft tracking from Unmanned Aerial Vehicles , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[23]  Larry H. Matthies,et al.  Vision-Based Obstacle Avoidance for Micro Air Vehicles Using an Egocylindrical Depth Map , 2016, ISER.

[24]  Michael Felsberg,et al.  Accurate Scale Estimation for Robust Visual Tracking , 2014, BMVC.

[25]  Larry H. Matthies,et al.  Stereo vision-based obstacle avoidance for micro air vehicles using disparity space , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).

[26]  Jianke Zhu,et al.  A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration , 2014, ECCV Workshops.

[27]  Jianxiong Xiao,et al.  Tracking Revisited Using RGBD Camera: Unified Benchmark and Baselines , 2013, 2013 IEEE International Conference on Computer Vision.

[28]  Matthew J. Rutherford,et al.  Radar-based detection and identification for miniature air vehicles , 2011, 2011 IEEE International Conference on Control Applications (CCA).

[29]  Giancarmine Fasano,et al.  Airborne Multisensor Tracking for Autonomous Collision Avoidance , 2006, 2006 9th International Conference on Information Fusion.

[30]  Young K. Kwag,et al.  UAV based collision avoidance radar sensor , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[31]  Davide Scaramuzza,et al.  Appearance-based Active, Monocular, Dense Reconstruction for Micro Aerial Vehicles , 2014, Robotics: Science and Systems.