Obstacle detection from overhead imagery using self-supervised learning for Autonomous Surface Vehicles

We describe a technique for an Autonomous Surface Vehicle (ASV) to learn an obstacle map by classifying overhead imagery. Classification labels are supplied by a front-facing sonar, mounted under the water line on the ASV. We use aerial imagery from two online sources for each of two water bodies (a small lake and a harbor) and train classifiers using features generated from each image source separately, followed by combining their output. Data collected using a sonar mounted on the ASV were used to generate the labels in the experimental study. The results show that we are able to generate accurate obstacle maps well-suited for ASV navigation.

[1]  Franz S. Hover,et al.  A Simple Reactive Obstacle Avoidance Algorithm and Its Application in Singapore Harbor , 2009, FSR.

[2]  Xiaqing Wu,et al.  Tree detection from aerial imagery , 2009, GIS.

[3]  Gaurav S. Sukhatme,et al.  Cooperative control of autonomous surface vehicles for oil skimming and cleanup , 2011, 2011 IEEE International Conference on Robotics and Automation.

[4]  J. Curcio,et al.  SCOUT - a low cost autonomous surface platform for research in cooperative autonomy , 2005, Proceedings of OCEANS 2005 MTS/IEEE.

[5]  Jacoby Larson,et al.  Autonomous navigation and obstacle avoidance for unmanned surface vehicles , 2006, SPIE Defense + Commercial Sensing.

[6]  Vladimir Kolmogorov,et al.  An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision , 2004, IEEE Trans. Pattern Anal. Mach. Intell..

[7]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[8]  D. Caron,et al.  Design and Development of a Wireless Robotic Networked Aquatic Microbial Observing System , 2007 .

[9]  Robert T. Collins,et al.  Autonomous river navigation , 2004, SPIE Optics East.

[10]  André Dias,et al.  Autonomous Surface Vehicle Docking Manoeuvre with Visual Information , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[11]  Horst Bischof,et al.  On robustness of on-line boosting - a competitive study , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[12]  John J. Leonard,et al.  Mapping Complex Marine Environments with Autonomous Surface Craft , 2010, ISER.

[13]  Nicolai Petkov,et al.  Comparison of texture features based on Gabor filters , 2002, IEEE Trans. Image Process..

[14]  Gaurav S. Sukhatme,et al.  Obstacle detection and avoidance for an Autonomous Surface Vehicle using a profiling sonar , 2011, 2011 IEEE International Conference on Robotics and Automation.

[15]  Yoram Singer,et al.  Improved Boosting Algorithms Using Confidence-rated Predictions , 1998, COLT' 98.

[16]  Terrance L. Huntsberger,et al.  Stereo vision–based navigation for autonomous surface vessels , 2011, J. Field Robotics.

[17]  M. Caccia,et al.  Autonomous Surface Craft: prototypes and basic research issues , 2006, 2006 14th Mediterranean Conference on Control and Automation.

[18]  David Silver,et al.  Experimental Analysis of Overhead Data Processing To Support Long Range Navigation , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[19]  Y. Freund,et al.  Discussion of the Paper \additive Logistic Regression: a Statistical View of Boosting" By , 2000 .

[20]  Yoav Freund,et al.  A decision-theoretic generalization of on-line learning and an application to boosting , 1995, EuroCOLT.

[21]  Christopher Assad,et al.  360‐degree visual detection and target tracking on an autonomous surface vehicle , 2010, J. Field Robotics.

[22]  N. Ranganathan,et al.  Gabor filter-based edge detection , 1992, Pattern Recognit..

[23]  Mart Tamre,et al.  Aerial imagery terrain classification for long-range autonomous navigation , 2009, 2009 International Symposium on Optomechatronic Technologies.

[24]  Leo Breiman,et al.  Classification and Regression Trees , 1984 .

[25]  John J. Leonard,et al.  Navigation of unmanned marine vehicles in accordance with the rules of the road , 2006, Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006..

[26]  J. Andrew Bagnell,et al.  Improving Robot Navigation Through Self-Supervised Online Learning , 2006, Robotics: Science and Systems.

[27]  Gaurav S. Sukhatme,et al.  Cooperative caging using autonomous aquatic surface vehicles , 2010, 2010 IEEE International Conference on Robotics and Automation.

[28]  Thomas Mauthner,et al.  Semantic Classification in Aerial Imagery by Integrating Appearance and Height Information , 2009, ACCV.

[29]  Oliver Brock,et al.  High Performance Outdoor Navigation from Overhead Data using Imitation Learning , 2009 .