/Mapping 3D Underwater Environments with Smoothed Submaps

Mapping 3D Underwater Environments with Smoothed Submaps

Mark VanMiddlesworth, Michael Kaess, Franz Hover and John J. Leonard
Conference Paper, Conference on Field and Service Robotics (FSR), December, 2013

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This paper presents a technique for improved mapping of complex un- derwater environments. Autonomous underwater vehicles (AUVs) are becoming valuable tools for inspection of underwater infrastructure, and can create 3D maps of their environment using high-frequency profiling sonar. However, the quality of these maps is limited by the drift in the vehicle’s navigation system. We have devel- oped a technique for simultaneous localization and mapping (SLAM) by aligning point clouds gathered over a short time scale using the iterative closest point (ICP) algorithm. To improve alignment, we have developed a system for smoothing these “submaps” and removing outliers. We integrate the constraints from submap align- ment into a 6-DOF pose graph, which is optimized to estimate the full vehicle tra- jectory over the duration of the inspection task. We present real-world results using the Bluefin Hovering AUV, as well as analysis of a synthetic data set.

BibTeX Reference
author = {Mark VanMiddlesworth and Michael Kaess and Franz Hover and John J. Leonard},
title = {Mapping 3D Underwater Environments with Smoothed Submaps},
booktitle = {Conference on Field and Service Robotics (FSR)},
year = {2013},
month = {December},