R.M. Eustice, H. Singh, J.J. Leonard, and M.R. Walter, Visually Mapping the RMS Titanic: Convervative Covariance Estimates for SLAM Information Filters, International Journal Robotics Research, vol. 25, no. 12, pp. 1223-1242, 2006
Abstract
This paper describes a vision-based, large-area, simultaneous localization and mapping (SLAM) algorithm that respects the low-overlap imagery constraints typical of underwater vehicles while exploiting the inertial sensor information that is routinely available on such platforms. We present a novel strategy for efficiently accessing and maintaining consistent covariance bounds within a SLAM information filter, thereby greatly increasing the reliability of data association. The technique is based upon solving a sparse system of linear equations coupled with the application of constant-time Kalman updates. The method is shown to produce consistent covariance estimates suitable for robot planning and data association. Real-world results are reported for a vision-based, six degree of freedom SLAM implementation using data from a recent survey of the wreck of the RMS Titanic.
@ARTICLE{reustice-2006c,
author = {Eustice, Ryan M. and Singh, Hanumant and Leonard, John J. and Walter, Matthew R.},
title = {Visually mapping the {RMS} {Titanic}: conservative covariance estimates for {SLAM} information filters},
journal = {Intl. J. Robotics Reserach},
year = {2006},
volume = {25},
pages = {1223--1242},
number = {12},
keywords = {SLAM, data association, information filters, mobile robotics, computer vision, underwater vehicles},
}
FILE » reustice-2006c.pdf
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