Combining Stereo Imaging, Inertial and Altitude Sensing Systems for the Quad-Rotor

This chapter is devoted to the design and implementation of a stereo-vision, inertial and altitude sensing system for a quad-rotor. The objective is to enable the vehicle to autonomously perform take-off, relative positioning, navigation and landing when evolving in unstructured, indoors, and GPS-denied environments. A real-time comparison study between a Luenberger observer, a Kalman filter and a complementary filter is also addressed, with the purpose of validating the most effective approach for combining the different sensing technologies.

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

[2]  Nicholas Roy,et al.  Stereo vision and laser odometry for autonomous helicopters in GPS-denied indoor environments , 2009, Defense + Commercial Sensing.

[3]  Larry H. Matthies,et al.  Visual odometry on the Mars Exploration Rovers , 2005, 2005 IEEE International Conference on Systems, Man and Cybernetics.

[4]  Oleg A. Yakimenko,et al.  Linear parametrically varying systems with brief instabilities: an application to integrated vision/IMU navigation , 2001, Proceedings of the 40th IEEE Conference on Decision and Control (Cat. No.01CH37228).

[5]  J.-Y. Bouguet,et al.  Pyramidal implementation of the lucas kanade feature tracker , 1999 .

[6]  Rogelio Lozano,et al.  Stabilization and Trajectory Tracking of a Quad-Rotor Using Vision , 2011, J. Intell. Robotic Syst..

[7]  Nicholas Roy,et al.  Planning in information space for a quadrotor helicopter in a GPS-denied environment , 2008, 2008 IEEE International Conference on Robotics and Automation.

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

[9]  Richard Szeliski,et al.  Visual odometry and map correlation , 2004, CVPR 2004.

[10]  Gaurav S. Sukhatme,et al.  Vision-based navigation through urban canyons , 2009 .

[11]  Niko Sünderhauf,et al.  Stereo Odometry – A Review of Approaches , 2007 .

[12]  Richard A. Brown,et al.  Introduction to random signals and applied kalman filtering (3rd ed , 2012 .

[13]  I.I. Kaminer,et al.  Linear parametrically varying systems with brief instabilities: an application to vision/inertial navigation , 2004, IEEE Transactions on Aerospace and Electronic Systems.

[14]  G. L. Santosuosso,et al.  Adaptive Observer and Kalman Filtering , 2008 .

[15]  Rogelio Lozano,et al.  Real-Time Stabilization of an Eight-Rotor UAV Using Optical Flow , 2009, IEEE Transactions on Robotics.

[16]  Robert C. Bolles,et al.  Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.

[17]  S. Shankar Sastry,et al.  An Invitation to 3-D Vision: From Images to Geometric Models , 2003 .

[18]  Dario Floreano,et al.  Toward 30-gram Autonomous Indoor Aircraft: Vision-based Obstacle Avoidance and Altitude Control , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[19]  Gordon Wyeth,et al.  Helicopter automation using a low-cost sensing system , 2003 .

[20]  Carlo Tomasi,et al.  Good features to track , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[21]  S. Umeyama,et al.  Least-Squares Estimation of Transformation Parameters Between Two Point Patterns , 1991, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  G.L. Santosuosso,et al.  Unmanned Aerial Vehicle Speed Estimation via Nonlinear Adaptive Observers , 2007, 2007 American Control Conference.

[23]  Peter I. Corke An inertial and visual sensing system for a small autonomous helicopter , 2004 .

[24]  Thomas B. Moeslund,et al.  Long-Term Occupancy Analysis Using Graph-Based Optimisation in Thermal Imagery , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[25]  James R. Bergen,et al.  Visual odometry , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[26]  Larry H. Matthies,et al.  Visual odometry on the Mars exploration rovers - a tool to ensure accurate driving and science imaging , 2006, IEEE Robotics & Automation Magazine.

[27]  Mohamed Boutayeb,et al.  A simple time-varying observer for speed estimation of UAV , 2008 .

[28]  Rogelio Lozano,et al.  Real-time stabilization and tracking of a four-rotor mini rotorcraft , 2004, IEEE Transactions on Control Systems Technology.

[29]  Gaurav S. Sukhatme,et al.  Combined Visual and Inertial Navigation for an Unmanned Aerial Vehicle , 2008, FSR.

[30]  Max Donath,et al.  American Control Conference , 1993 .

[31]  Peter I. Corke,et al.  Omnidirectional visual odometry for a planetary rover , 2004, 2004 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (IEEE Cat. No.04CH37566).

[32]  B. Tamm International Federation of Automatic Control , 1992, Concise Encyclopedia of Modelling & Simulation.

[33]  Dimitri Jeltsema,et al.  Proceedings of the 17th International Symposium on Mathematical Theory of Networks and Systems , 2006 .

[34]  Tony J. Dodd,et al.  Active Bayesian perception for angle and position discrimination with a biomimetic fingertip , 2013, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems.