3D Reconstruction of Underwater Structures

被引:67
作者
Beall, Chris [1 ]
Lawrence, Brian J. [1 ]
Ila, Viorela [2 ,3 ]
Dellaert, Frank [1 ]
机构
[1] Georgia Inst Technol, Coll Comp Bldg, 801 Atlantic Dr, Atlanta, GA 30332 USA
[2] Georgia Inst Technol, Atlanta, GA 30332 USA
[3] UPC, CSIC, Inst Robot Informat Ind, Barcelona, Spain
来源
IEEE/RSJ 2010 INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2010) | 2010年
基金
美国国家科学基金会;
关键词
D O I
10.1109/IROS.2010.5649213
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Environmental change is a growing international concern, calling for the regular monitoring, studying and preserving of detailed information about the evolution of underwater ecosystems. For example, fragile coral reefs are exposed to various sources of hazards and potential destruction, and need close observation. Computer vision offers promising technologies to build 3D models of an environment from two-dimensional images. The state of the art techniques have enabled high-quality digital reconstruction of large-scale structures, e.g., buildings and urban environments, but only sparse representations or dense reconstruction of small objects have been obtained from underwater video and still imagery. The application of standard 3D reconstruction methods to challenging underwater environments typically produces unsatisfactory results. Accurate, full camera trajectories are needed to serve as the basis for dense 3D reconstruction. A highly accurate sparse 3D reconstruction is the ideal foundation on which to base subsequent dense reconstruction algorithms. In our application the models are constructed from synchronized high definition videos collected using a wide baseline stereo rig. The rig can be hand-held, attached to a boat, or even to an autonomous underwater vehicle. We solve this problem by employing a smoothing and mapping toolkit developed in our lab specifically for this type of application. The result of our technique is a highly accurate sparse 3D reconstruction of underwater structures such as corals.
引用
收藏
页码:4418 / 4423
页数:6
相关论文
共 17 条
[1]  
[Anonymous], 2006, SPEECHED ROBUST FEAT
[2]  
BOUGUET J, 2004, CALIBRATION TOOLBOX
[3]  
Bouguet J.-Y., 2004, Camera calibration toolbox for Matlab
[4]  
Brandou V., 2007, OCEANS 2007 - Europe, P1, DOI 10.1109/OCEANSE.2007.4302315
[5]   MonoSLAM: Real-time single camera SLAM [J].
Davison, Andrew J. ;
Reid, Ian D. ;
Molton, Nicholas D. ;
Stasse, Olivier .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2007, 29 (06) :1052-1067
[6]  
Dellaert F., 2005, Robotics: Science and Systems (RSS)
[7]   Square root SAM: Simultaneous localization and mapping via square root information smoothing [J].
Dellaert, Frank ;
Kaess, Michael .
INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, 2006, 25 (12) :1181-1203
[8]   Exactly sparse delayed-state filters for view-based SLAM [J].
Eustice, Ryan M. ;
Singh, Hanumant ;
Leonard, John J. .
IEEE TRANSACTIONS ON ROBOTICS, 2006, 22 (06) :1100-1114
[9]   RANDOM SAMPLE CONSENSUS - A PARADIGM FOR MODEL-FITTING WITH APPLICATIONS TO IMAGE-ANALYSIS AND AUTOMATED CARTOGRAPHY [J].
FISCHLER, MA ;
BOLLES, RC .
COMMUNICATIONS OF THE ACM, 1981, 24 (06) :381-395
[10]  
Harris C, 1988, ALVEY VISION C, V15, P10, DOI DOI 10.5244/C.2.23