Benthic habitat mapping with autonomous underwater vehicles

We provide an outline of an autonomous benthic habitat mapping algorithm. This algorithm enables real-time on-board classification of images gathered by an autonomous underwater vehicle (AUV), with the ability to classify aquatic vegetation at a resolution approaching the species level. The algorithm is generic with respect to both the classification task and the imaging equipment being used. For example, it may be used to detect objects rather than habitat types using hyperspectral rather than visible light cameras.

[1]  Javier González,et al.  Toward a Unified Bayesian Approach to Hybrid Metric--Topological SLAM , 2008, IEEE Transactions on Robotics.

[2]  Sunil Narumalani,et al.  High-resolution ocean color remote sensing of benthic habitats: a case study at the Roatan island, Honduras , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[3]  T. Pearson,et al.  Objective Selection of Sensitive Species Indicative of Pollution-Induced Change in Benthic Communities. I. Comparative Methodology , 1982 .

[4]  Yoav Y. Schechner,et al.  Clear underwater vision , 2004, Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004..

[5]  D. B. Judd,et al.  Spectral Distribution of Typical Daylight as a Function of Correlated Color Temperature , 1964 .

[6]  Peter I. Corke,et al.  Experiments with Underwater Robot Localization and Tracking , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.

[7]  Peter I. Corke,et al.  A Hybrid AUV Design for Shallow Water Reef Navigation , 2005, Proceedings of the 2005 IEEE International Conference on Robotics and Automation.

[8]  John J. Leonard,et al.  Cooperative AUV Navigation Using a Single Surface Craft , 2009, FSR.

[9]  John M. Melack,et al.  Remote sensing of aquatic vegetation: theory and applications , 2008, Environmental monitoring and assessment.

[10]  John J. Leonard,et al.  Autonomous mapping with an AUV: an approach for ground truthing of remote sensing data , 1996, OCEANS 96 MTS/IEEE Conference Proceedings. The Coastal Ocean - Prospects for the 21st Century.

[11]  David C. Slaughter,et al.  Autonomous robotic weed control systems: A review , 2008 .

[12]  Scott D. Noble,et al.  Site-specific weed management: sensing requirements— what do we need to see? , 2005, Weed Science.

[13]  A. Dekker,et al.  Seagrass species: are they spectrally distinct? , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[14]  B. Lundén,et al.  Assessment of changes in the seagrass-dominated submerged vegetation of tropical Chwaka Bay (Zanzibar) using satellite remote sensing , 2006 .

[15]  C. Frid,et al.  Using indicator species to assess the state of macrobenthic communities , 2003, Hydrobiologia.

[16]  David M. Lane,et al.  Feature extraction and data association for AUV concurrent mapping and localisation , 2001, Proceedings 2001 ICRA. IEEE International Conference on Robotics and Automation (Cat. No.01CH37164).

[17]  I. M. Scotford,et al.  Applications of Spectral Reflectance Techniques in Northern European Cereal Production: A Review , 2005 .

[18]  Reyer Zwiggelaar,et al.  A review of spectral properties of plants and their potential use for crop/weed discrimination in row-crops , 1998 .

[19]  Bjørn Jalving,et al.  Positioning accuracy for the HUGIN detailed seabed mapping UUV , 1998, IEEE Oceanic Engineering Society. OCEANS'98. Conference Proceedings (Cat. No.98CH36259).

[20]  D. Parsons,et al.  Fine-scale habitat change in a marine reserve, mapped using radio-acoustically positioned video transects , 2004 .

[21]  Michel Chapron,et al.  A multiresolution based method for recognizing weeds in corn fields , 2000, Proceedings 15th International Conference on Pattern Recognition. ICPR-2000.

[22]  Chris Roelfsema,et al.  Mapping seagrass species, cover and biomass in shallow waters : An assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton Bay (Australia) , 2008 .

[23]  I.T. Ruiz,et al.  Decentralised Simultaneous Localisation and Mapping for AUVs , 2007, OCEANS 2007 - Europe.

[24]  R. N. Carpenter,et al.  Concurrent mapping and localization and map matching on autonomous underwater vehicles , 2001, MTS/IEEE Oceans 2001. An Ocean Odyssey. Conference Proceedings (IEEE Cat. No.01CH37295).

[25]  Emanuele Trucco,et al.  Automatic indexing of underwater survey video: algorithm and benchmarking method , 2003 .

[26]  Peter I. Corke,et al.  Visual Motion Estimation for an Autonomous Underwater Reef Monitoring Robot , 2005, FSR.

[27]  Peter I. Corke,et al.  Low-cost vision-based AUV guidance system for reef navigation , 2004, IEEE International Conference on Robotics and Automation, 2004. Proceedings. ICRA '04. 2004.

[28]  M.D. Dunbabin,et al.  Large-Scale Habitat Mapping Using Vision-Based AUVs: Experiences, Challenges & Vehicle Design , 2007, OCEANS 2007 - Europe.