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.

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},
} 2017-09-13T10:39:11-04:00