Visual 3D self localization with 8 gram circuit board for very compact and fully autonomous unmanned aerial vehicles

We describe an algorithm and hardware system of a 3D self-localization method for very cost effective hovering robots. The hardware system consists of very compact calculation and sensor units, instead of using a high performance calculation unit like a laptop PC. In the experiment, the comparison with the ground truth sensor is discussed and implementation of the system on a palm-sized quad-copter is studied to realize a very compact, on-board, fully autonomous quad-copter.

[1]  Vijay Kumar,et al.  Autonomous multi-floor indoor navigation with a computationally constrained MAV , 2011, 2011 IEEE International Conference on Robotics and Automation.

[2]  Peter I. Corke,et al.  Monocular vision based autonomous navigation for a cost-effective MAV in GPS-denied environments , 2013, 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[3]  Raffaello D'Andrea,et al.  Cooperative quadrocopter ball throwing and catching , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[4]  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.

[5]  Vincent Lepetit,et al.  BRIEF: Binary Robust Independent Elementary Features , 2010, ECCV.

[6]  Roland Siegwart,et al.  Vision based MAV navigation in unknown and unstructured environments , 2010, 2010 IEEE International Conference on Robotics and Automation.

[7]  Koichi Hori,et al.  Designing hardware and software systems toward very compact and fully autonomous quadrotors , 2013, 2013 IEEE/ASME International Conference on Advanced Intelligent Mechatronics.

[8]  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.

[9]  Robert E. Mahony,et al.  Hovering flight and vertical landing control of a VTOL Unmanned Aerial Vehicle using optical flow , 2008, 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[10]  Vijay Kumar,et al.  Cooperative manipulation and transportation with aerial robots , 2009, Auton. Robots.

[11]  Sebastian Madgwick,et al.  Estimation of IMU and MARG orientation using a gradient descent algorithm , 2011, 2011 IEEE International Conference on Rehabilitation Robotics.

[12]  Wolfram Burgard,et al.  Towards a navigation system for autonomous indoor flying , 2009, 2009 IEEE International Conference on Robotics and Automation.

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

[14]  G. Klein,et al.  Parallel Tracking and Mapping for Small AR Workspaces , 2007, 2007 6th IEEE and ACM International Symposium on Mixed and Augmented Reality.

[15]  H. Jin Kim,et al.  Onboard flight control of a micro quadrotor using single strapdown optical flow sensor , 2012, 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  Tom Drummond,et al.  Faster and Better: A Machine Learning Approach to Corner Detection , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.