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“iSAM2: Incremental Smoothing and Mapping with Fluid Relinearization and Incremental Variable Reordering” by M. Kaess, H. Johannsson, R. Roberts, V. Ila, J.J. Leonard, and F. Dellaert. In Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA, (Shanghai, China), May 2011, pp. 3281-3288.
We present iSAM2, a fully incremental, graph-based version of incremental smoothing and mapping (iSAM). iSAM2 is based on a novel graphical model-based interpretation of incremental sparse matrix factorization methods, afforded by the recently introduced Bayes tree data structure. The original iSAM algorithm incrementally maintains the square root information matrix by applying matrix factorization updates. We analyze the matrix updates as simple editing operations on the Bayes tree and the conditional densities represented by its cliques. Based on that insight, we present a method to incrementally change the variable ordering which has a large effect on efficiency. We also introduce fluid relinearization, the concept of selectively relinearizing variables as needed, to obtain a fully incremental algorithm without any need for periodic batch steps.We analyze the properties of the resulting algorithm in detail, and show on various real and simulated datasets that the iSAM2 algorithm compares favorably with other recent mapping algorithms in both quality and efficiency.
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BibTeX entry:
@inproceedings{Kaess11icra,
author = {M. Kaess and H. Johannsson and R. Roberts and V. Ila and J.J.
Leonard and F. Dellaert},
title = {{iSAM2}: Incremental Smoothing and Mapping with Fluid
Relinearization and Incremental Variable Reordering},
booktitle = {Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA},
pages = {3281-3288},
address = {Shanghai, China},
month = may,
year = {2011}
}