Uncertainty-driven view planning for underwater inspection

Geoffrey A. Hollinger, Brendan Englot, Franz Hover, Urbashi Mitra, Gaurav S. Sukhatme

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

52 Scopus citations

Abstract

We discuss the problem of inspecting an underwater structure, such as a submerged ship hull, with an autonomous underwater vehicle (AUV). In such scenarios, the goal is to construct an accurate 3D model of the structure and to detect any anomalies (e.g., foreign objects or deformations). We propose a method for constructing 3D meshes from sonar-derived point clouds that provides watertight surfaces, and we introduce uncertainty modeling through non-parametric Bayesian regression. Uncertainty modeling provides novel cost functions for planning the path of the AUV to minimize a metric of inspection performance. We draw connections between the resulting cost functions and submodular optimization, which provides insight into the formal properties of active perception problems. In addition, we present experimental trials that utilize profiling sonar data from ship hull inspection.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Robotics and Automation, ICRA 2012
Pages4884-4891
Number of pages8
DOIs
StatePublished - 2012
Event 2012 IEEE International Conference on Robotics and Automation, ICRA 2012 - Saint Paul, MN, United States
Duration: 14 May 201218 May 2012

Publication series

NameProceedings - IEEE International Conference on Robotics and Automation
ISSN (Print)1050-4729

Conference

Conference 2012 IEEE International Conference on Robotics and Automation, ICRA 2012
Country/TerritoryUnited States
CitySaint Paul, MN
Period14/05/1218/05/12

Fingerprint

Dive into the research topics of 'Uncertainty-driven view planning for underwater inspection'. Together they form a unique fingerprint.

Cite this