Experimental Analysis of Overhead Data Processing To Support Long Range Navigation

Long range navigation by unmanned ground vehicles continues to challenge the robotics community. Efficient navigation requires not only intelligent on-board perception and planning systems, but also the effective use of prior knowledge of the vehicle's environment. This paper describes a system for supporting unmanned ground vehicle navigation through the use of heterogeneous overhead data. Semantic information is obtained through supervised classification, and vehicle mobility is predicted from available geometric data. This approach is demonstrated and validated through over 50 kilometers of autonomous traversal through complex natural environments

[1]  F. J. Kriegler,et al.  Preprocessing Transformations and Their Effects on Multispectral Recognition , 1969 .

[2]  Reid G. Simmons,et al.  Recent progress in local and global traversability for planetary rovers , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).

[3]  G. Sithole.  FILTERING OF LASER ALTIMETRY DATA USING A SLOPE ADAPTIVE FILTER , 2001 .

[4]  William Whittaker,et al.  First experiment in sun-synchronous exploration , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[5]  Edward Tunstel,et al.  Rover autonomy for long range navigation and science data acquisition on planetary surfaces , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[6]  William Whittaker,et al.  Mission planning for the Sun-Synchronous Navigation Field Experiment , 2002, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292).

[7]  Tommy Chang,et al.  Using a priori data for prediction and object recognition in an autonomous mobile vehicle , 2003, SPIE Defense + Commercial Sensing.

[8]  Alonzo Kelly,et al.  Real-Time, Multi-Perspective Perception for Unmanned Ground Vehicles , 2003 .

[9]  Martial Hebert,et al.  Experimental Results in Using Aerial LADAR Data for Mobile Robot Navigation , 2003, FSR.

[10]  Allan Aasbjerg Nielsen,et al.  Detection of buildings through multivariate analysis of spectral, textural, and shape based features , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[11]  Roberto Manduchi,et al.  Supervised Parametric Classification of Aerial LiDAR Data , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[12]  Martial Hebert,et al.  Natural terrain classification using 3-d ladar data , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[13]  Anthony Stentz,et al.  Field D*: An Interpolation-Based Path Planner and Replanner , 2005, ISRR.

[14]  Xin Yang,et al.  A two-stage level set evolution scheme for man-made objects detection in aerial images , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[15]  Myung Hwangbo,et al.  Results in Combined Route Traversal and Collision Avoidance , 2005, FSR.

[16]  R. Simmons,et al.  Navigation regimes for off-road autonomy , 2005 .

[17]  William Whittaker,et al.  Mission-directed path planning for planetary rover exploration , 2005 .

[18]  J. Andrew Bagnell,et al.  Terrain Classification from Aerial Data to Support Ground Vehicle Navigation , 2006 .