A Bayesian approach to optimal sensor placement
International Journal of Robotics Research
Surface reconstruction from unorganized points
SIGGRAPH '92 Proceedings of the 19th annual conference on Computer graphics and interactive techniques
A volumetric method for building complex models from range images
SIGGRAPH '96 Proceedings of the 23rd annual conference on Computer graphics and interactive techniques
Autonomous Exploration: Driven by Uncertainty
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Theoretic Sensor Data Selection for Active Object Recognition and State Estimation
IEEE Transactions on Pattern Analysis and Machine Intelligence
3D Mapping of Underwater Caves
IEEE Computer Graphics and Applications
Feature Space Trajectory Methods for Active Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Conditional Feature Sensitivity: A Unifying View on Active Recognition and Feature Selection
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Poisson surface reconstruction
SGP '06 Proceedings of the fourth Eurographics symposium on Geometry processing
Algorithms for subset selection in linear regression
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
The Traveling Salesman Problem: A Computational Study (Princeton Series in Applied Mathematics)
Efficient Multi-robot Search for a Moving Target
International Journal of Robotics Research
Consolidation of unorganized point clouds for surface reconstruction
ACM SIGGRAPH Asia 2009 papers
Efficient informative sensing using multiple robots
Journal of Artificial Intelligence Research
Gaussian process modeling of large-scale terrain
Journal of Field Robotics - Three-Dimensional Mapping, Part 1
Developing visual sensing strategies through next best view planning
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Pose-graph visual SLAM with geometric model selection for autonomous underwater ship hull inspection
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
Spectral registration of noisy sonar data for underwater 3D mapping
Autonomous Robots
Active vision in robotic systems: A survey of recent developments
International Journal of Robotics Research
Stochastic covering and adaptivity
LATIN'06 Proceedings of the 7th Latin American conference on Theoretical Informatics
Adaptive submodularity: theory and applications in active learning and stochastic optimization
Journal of Artificial Intelligence Research
iSAM: Incremental Smoothing and Mapping
IEEE Transactions on Robotics
Vision sensor planning for 3-D model acquisition
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Post-processing of scanned 3D surface data
SPBG'04 Proceedings of the First Eurographics conference on Point-Based Graphics
Three-dimensional coverage planning for an underwater inspection robot
International Journal of Robotics Research
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We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). Unlike a large body of prior work, we focus on planning the views of the AUV to improve the quality of the inspection, rather than maximizing the accuracy of a given data stream. We formulate the inspection planning problem as an extension to Bayesian active learning, and we show connections to recent theoretical guarantees in this area. We rigorously analyze the benefit of adaptive re-planning for such problems, and we prove that the potential benefit of adaptivity can be reduced from an exponential to a constant factor by changing the problem from cost minimization with a constraint on information gain to variance reduction with a constraint on cost. Such analysis allows the use of robust, non-adaptive planning algorithms that perform competitively with adaptive algorithms. Based on our analysis, we propose a method for constructing 3D meshes from sonar-derived point clouds, and we introduce uncertainty modeling through non-parametric Bayesian regression. Finally, we demonstrate the benefit of active inspection planning using sonar data from ship hull inspections with the Bluefin-MIT Hovering AUV.