154 research outputs found
Map building fusing acoustic and visual information using autonomous underwater vehicles
Author Posting. © The Author(s), 2012. This is the author's version of the work. It is posted here by permission of John Wiley & Sons for personal use, not for redistribution. The definitive version was published in Journal of Field Robotics 30 (2013): 763–783, doi:10.1002/rob.21473.We present a system for automatically building 3-D maps of underwater terrain fusing
visual data from a single camera with range data from multibeam sonar. The six-degree
of freedom location of the camera relative to the navigation frame is derived as part of the
mapping process, as are the attitude offsets of the multibeam head and the on-board velocity
sensor. The system uses pose graph optimization and the square root information smoothing
and mapping framework to simultaneously solve for the robot’s trajectory, the map, and
the camera location in the robot’s frame. Matched visual features are treated within the
pose graph as images of 3-D landmarks, while multibeam bathymetry submap matches are
used to impose relative pose constraints linking robot poses from distinct tracklines of the
dive trajectory. The navigation and mapping system presented works under a variety of
deployment scenarios, on robots with diverse sensor suites. Results of using the system to
map the structure and appearance of a section of coral reef are presented using data acquired
by the Seabed autonomous underwater vehicle.The work described herein was funded by the National Science Foundation Censsis ERC under grant number
EEC-9986821, and by the National Oceanic and Atmospheric Administration under grant number
NA090AR4320129
Self consistent bathymetric mapping from robotic vehicles in the deep ocean
Submitted In partial fulfillment of the requirements for the degree of Doctor of Philosophy at the Massachusetts Institute of Technology and Woods Hole Oceanographic Institution
June 2005Obtaining accurate and repeatable navigation for robotic vehicles in the deep ocean is difficult
and consequently a limiting factor when constructing vehicle-based bathymetric maps.
This thesis presents a methodology to produce self-consistent maps and simultaneously
improve vehicle position estimation by exploiting accurate local navigation and utilizing
terrain relative measurements.
It is common for errors in the vehicle position estimate to far exceed the errors associated
with the acoustic range sensor. This disparity creates inconsistency when an area
is imaged multiple times and causes artifacts that distort map integrity. Our technique
utilizes small terrain "submaps" that can be pairwise registered and used to additionally
constrain the vehicle position estimates in accordance with actual bottom topography.
A delayed state Kalman filter is used to incorporate these sub-map registrations as relative
position measurements between previously visited vehicle locations. The archiving of
previous positions in a filter state vector allows for continual adjustment of the sub-map
locations. The terrain registration is accomplished using a two dimensional correlation and
a six degree of freedom point cloud alignment method tailored for bathymetric data. The
complete bathymetric map is then created from the union of all sub-maps that have been
aligned in a consistent manner. Experimental results from the fully automated processing
of a multibeam survey over the TAG hydrothermal structure at the Mid-Atlantic ridge are
presented to validate the proposed method.This work was funded by the CenSSIS ERC of the Nation Science Foundation under
grant EEC-9986821 and in part by the Woods Hole Oceanographic Institution through a
grant from the Penzance Foundation
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