Automatic navigation and landing of an indoor AR. drone quadrotor using ArUco marker and inertial sensors

Within the next few years, unmanned quadrotors are likely to become an important vehicle in humans' daily life. However, their automatic navigation and landing in indoor environments are among the commonly discussed topics in this regard. In fact, the quadrotor should be able to automatically find the landing point from the nearby position, navigate toward it, and finally, land on it accurately and smoothly. In this paper, we proposed a low-cost and thorough solution to this problem by using both bottom-facing and front-facing cameras of the drone. In addition, in the case that vision data were unavailable, inertial measurements alongside a Kalman filter were used to navigate the drone to achieve the promising continuity and reliability. An AR.Drone 2.0 quadrotor, as well as an ArUco marker, were employed in order to test the proposed method experimentally. The results indicated that the drone successfully landed on the predefined position with an acceptable time and accuracy.

[1]  O. Faugeras.  Three-dimensional computer vision: a geometric viewpoint , 1993 .

[2]  Andreas Zell,et al.  Multi-camera visual SLAM for autonomous navigation of micro aerial vehicles , 2017, Robotics Auton. Syst..

[3]  Peter S. Maybeck,et al.  Stochastic Models, Estimation And Control , 2012 .

[4]  P. B. Sujit,et al.  Autonomous Quadrotor Landing Using Vision and Pursuit Guidance , 2017 .

[5]  Vijay Kumar,et al.  Estimation, Control, and Planning for Aggressive Flight With a Small Quadrotor With a Single Camera and IMU , 2017, IEEE Robotics and Automation Letters.

[6]  Rogelio Lozano,et al.  Autonomous take-off and landing on a colored platform , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[7]  Anitha Ganesan,et al.  Vision-Based Heading and Lateral Deviation Estimation for Indoor Navigation of a Quadrotor , 2017 .

[8]  Hriday Bavle,et al.  Towards fully autonomous landing on moving platforms for rotary Unmanned Aerial Vehicles , 2017, 2017 International Conference on Unmanned Aircraft Systems (ICUAS).

[9]  Wilbert G. Aguilar,et al.  Visual SLAM with a RGB-D Camera on a Quadrotor UAV Using on-Board Processing , 2017, IWANN.

[10]  Rita Cunha,et al.  Landing of a Quadrotor on a Moving Target Using Dynamic Image-Based Visual Servo Control , 2016, IEEE Transactions on Robotics.

[11]  Mohammad Shahid,et al.  Dynamic modeling and stabilization of quadrotor using PID controller , 2014, 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI).

[12]  Martin Brossard,et al.  Unscented Kalman Filtering on Lie Groups for Fusion of IMU and Monocular Vision , 2017 .

[13]  Shen Lincheng,et al.  On-board vision autonomous landing techniques for quadrotor: A survey , 2016, 2016 35th Chinese Control Conference (CCC).

[14]  Hilbert J. Kappen,et al.  Efficient Optical Flow and Stereo Vision for Velocity Estimation and Obstacle Avoidance on an Autonomous Pocket Drone , 2016, IEEE Robotics and Automation Letters.

[15]  Dang Quang Nguyen,et al.  Autonomous Landing of a Quadcopter on a High-Speed Ground Vehicle , 2017 .

[16]  Vincenzo Lippiello,et al.  Emergency landing for a quadrotor in case of a propeller failure: A PID based approach , 2014, 2014 IEEE International Symposium on Safety, Security, and Rescue Robotics (2014).

[17]  Flavio Fontana,et al.  Autonomous, Vision‐based Flight and Live Dense 3D Mapping with a Quadrotor Micro Aerial Vehicle , 2016, J. Field Robotics.

[18]  Daniel Lélis Baggio,et al.  Mastering OpenCV with Practical Computer Vision Projects , 2012 .

[19]  V. Lepetit,et al.  EPnP: An Accurate O(n) Solution to the PnP Problem , 2009, International Journal of Computer Vision.

[20]  Mário Sarcinelli Filho,et al.  Navigation and Cooperative Control Using the AR.Drone Quadrotor , 2016, J. Intell. Robotic Syst..

[21]  T. Başar,et al.  A New Approach to Linear Filtering and Prediction Problems , 2001 .

[22]  Robbert van Ginkel,et al.  Autonomous Landing of a Quadcopter on a Predefined Marker , 2013 .

[23]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[24]  Emanuele Frontoni,et al.  Unmanned Ground and Aerial Vehicles in extended range indoor and outdoor missions , 2014, 2014 International Conference on Unmanned Aircraft Systems (ICUAS).

[25]  Maria Magnusson.  Short on camera geometry and camera calibration , 2010 .

[26]  Gary R. Bradski,et al.  Learning OpenCV - computer vision with the OpenCV library: software that sees , 2008 .

[27]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[28]  Adha Imam Cahyadi,et al.  Inertial Navigation for Quadrotor Using Kalman Filter with Drift Compensation , 2017 .

[29]  Yunseong Lee,et al.  Design and implementation of autonomous wireless charging station for rotary-wing UAVs , 2016 .

[30]  Wei Bai,et al.  Visual landing system of UAV based on ADRC , 2017, 2017 29th Chinese Control And Decision Conference (CCDC).

[31]  Tiauw Hiong Go,et al.  Automatic landing system design using sliding mode control , 2014 .

[32]  David Hyunchul Shim,et al.  Vision-based UAV landing on the moving vehicle , 2016, 2016 International Conference on Unmanned Aircraft Systems (ICUAS).

[33]  Dimitra Panagou,et al.  Vision-based target tracking and autonomous landing of a quadrotor on a ground vehicle , 2017, 2017 American Control Conference (ACC).

[34]  Keiichi Abe,et al.  Topological structural analysis of digitized binary images by border following , 1985, Comput. Vis. Graph. Image Process..

[35]  S. Shankar Sastry,et al.  A vision system for landing an unmanned aerial vehicle , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[36]  Gonzalo Pajares,et al.  Recognition of a landing platform for unmanned aerial vehicles by using computer vision-based techniques , 2017, Expert Syst. Appl..

[37]  Andreas Zell,et al.  Automatic Take Off, Tracking and Landing of a Miniature UAV on a Moving Carrier Vehicle , 2011, J. Intell. Robotic Syst..

[38]  Siu O’Young,et al.  Marker-guided auto-landing on a moving platform , 2017 .

[39]  Qingyu Chen,et al.  Multi-resolution visual fiducial and assistant navigation system for unmanned aerial vehicle landing , 2017 .

[40]  T. Krajnik,et al.  Cooperative μUAV-UGV autonomous indoor surveillance , 2012, International Multi-Conference on Systems, Sygnals & Devices.

[41]  Francisco José Madrid-Cuevas,et al.  Automatic generation and detection of highly reliable fiducial markers under occlusion , 2014, Pattern Recognit..

[42]  Haibin Duan,et al.  Implementation of autonomous visual tracking and landing for a low-cost quadrotor , 2013 .

[43]  Steven Lake Waslander,et al.  Coordinated landing of a quadrotor on a skid-steered ground vehicle in the presence of time delays , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[44]  T. Carreira.  Quadcopter Automatic Landing on a Docking Station , 2013 .

[45]  Patrick Benavidez,et al.  Landing of an Ardrone 2.0 quadcopter on a mobile base using fuzzy logic , 2014, 2014 World Automation Congress (WAC).

[46]  James Diebel,et al.  Representing Attitude : Euler Angles , Unit Quaternions , and Rotation Vectors , 2006 .

[47]  Zhanglong Wang,et al.  Autonomous landing of multi-rotors UAV with monocular gimbaled camera on moving vehicle , 2017, 2017 13th IEEE International Conference on Control & Automation (ICCA).

[48]  Sunil Shah.  Real-time Image Processing on Low Cost Embedded Computers , 2014 .

[49]  Emanuele Frontoni,et al.  Visual Based Landing for an Unmanned Quadrotor , 2016, J. Intell. Robotic Syst..

[50]  Jerome Le Ny,et al.  Autonomous Landing of a Multirotor Micro Air Vehicle on a High Velocity Ground Vehicle , 2016, ArXiv.

[51]  Eric N. Johnson,et al.  Estimation Techniques in Robust Vision-Based Landing of Aerial Vehicles , 2017 .