A hybrid localization approach for UAV in GPS denied areas

In this paper, the localization problem of an autonomous unmanned aerial vehicle in case of losing the GPS signal is handled. A vision-based solution approach is proposed consisting of two phases. In the first phase a hybrid map is constructed. Such map consists of a set of reduced features obtained by information-theoretic analysis. This enables faster UAV localization processing without degenerating the accuracy. The features are represented by local descriptors which are additionally tagged with their metric position. The second phase localizes the UAV using the map, which is performed on two scales. A fast and coarse topological location is identified based on matching features of images taken by the camera with the local descriptors information in the map. This guides the UAV in fast and safe emergency homing. A second precise metric position can be estimated in extension with respect to a previously identified topological location and with the aid of the features' metric position information. This can assist the UAV navigation in case the mission should be completed without interruption despite the GPS signal loss.

[1]  Hugh Durrant-Whyte,et al.  Simultaneous localization and mapping (SLAM): part II , 2006 .

[2]  Jonghyuk Kim,et al.  6DoF SLAM aided GNSS/INS Navigation in GNSS Denied and Unknown Environments , 2005 .

[3]  Jiawei Han,et al.  Data Mining: Concepts and Techniques , 2000 .

[4]  Henrik Ohlsson,et al.  Geo-referencing for UAV navigation using environmental classification , 2010, 2010 IEEE International Conference on Robotics and Automation.

[5]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[6]  Howie Choset,et al.  Hierarchical simultaneous localization and mapping , 2003, Proceedings 2003 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2003) (Cat. No.03CH37453).

[7]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[8]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[9]  Guoqiang Mao,et al.  Design of an Extended Kalman Filter for UAV Localization , 2007, 2007 Information, Decision and Control.

[10]  Essameddin Badreddin,et al.  Information-theoretic environment modeling for efficient topological localization , 2010, 2010 10th International Conference on Intelligent Systems Design and Applications.

[11]  Essameddin Badreddin,et al.  Hierarchical localization using entropy-based feature map and triangulation techniques , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.

[12]  Essameddin Badreddin,et al.  Building efficient topological maps for mobile robot localization: An evaluation study on COLD benchmarking database , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[13]  Hugh F. Durrant-Whyte,et al.  Simultaneous localization and mapping: part I , 2006, IEEE Robotics & Automation Magazine.

[14]  Sharon S. Welch,et al.  The spacecraft control laboratory experiment optical attitude measurement system , 1991 .

[15]  Howie Choset,et al.  Hybrid localization using the hierarchical atlas , 2007, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[16]  G. Conte,et al.  An Integrated UAV Navigation System Based on Aerial Image Matching , 2008, 2008 IEEE Aerospace Conference.

[17]  Jinling Wang,et al.  INTEGRATION OF GPS / INS / VISION SENSORS TO NAVIGATE UNMANNED AERIAL VEHICLES , 2008 .

[18]  Essameddin Badreddin,et al.  Collision avoidance in a recursive nested behaviour control structure for Unmanned Aerial Vehicles , 2010, 2010 IEEE International Conference on Systems, Man and Cybernetics.