/Concurrent Filtering and Smoothing

Concurrent Filtering and Smoothing

Michael Kaess, Stephen Williams, Vadim Indelman, Richard Roberts, John J. Leonard and Frank Dellaert
Conference Paper, Intl. Conf. on Information Fusion, FUSION, pp. 1300-1307, July, 2012

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Abstract

This paper presents a novel algorithm for integrat- ing real-time filtering of navigation data with full map/trajectory smoothing. Unlike conventional mapping strategies, the result of loop closures within the smoother serve to correct the real-time navigation solution in addition to the map. This solution views filtering and smoothing as different operations applied within a single graphical model known as a Bayes tree. By maintaining all information within a single graph, the optimal linear estimate is guaranteed, while still allowing the filter and smoother to operate asynchronously. This approach has been applied to simulated aerial vehicle sensors consisting of a high-speed IMU and stereo camera. Loop closures are extracted from the vision system in an external process and incorporated into the smoother when discovered. The performance of the proposed method is shown to approach that of full batch optimization while maintaining real-time operation.

BibTeX Reference
@conference{Kaess-2012-7554,
author = {Michael Kaess and Stephen Williams and Vadim Indelman and Richard Roberts and John J. Leonard and Frank Dellaert},
title = {Concurrent Filtering and Smoothing},
booktitle = {Intl. Conf. on Information Fusion, FUSION},
year = {2012},
month = {July},
pages = {1300-1307},
keywords = {Navigation, smoothing, filtering, loop closing, Bayes tree, factor graph},
}
2017-09-13T10:39:45+00:00