MH-iSAM2: Multi-hypothesis iSAM using Bayes Tree and Hypo-tree

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“MH-iSAM2: Multi-hypothesis iSAM using Bayes Tree and Hypo-tree” by M. Hsiao and M. Kaess. In Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA, (Montreal, Canada), May 2019, pp. 1274-1280.

Abstract

A novel nonlinear incremental optimization algorithm MH-iSAM2 is developed to handle ambiguity in simultaneous localization and mapping (SLAM) problems in a multi-hypothesis fashion. It can output multiple possible solutions for each variable according to the ambiguous inputs, which is expected to greatly enhance the robustness of autonomous systems as a whole. The algorithm consists of two data structures: an extension of the original Bayes tree that allows efficient multi-hypothesis inference, and a Hypo-tree that is designed to explicitly track and associate the hypotheses of each variable as well as all the inference processes for optimization. With our proposed hypothesis pruning strategy, MH-iSAM2 enables fast optimization and avoids the exponential growth of hypotheses. We evaluate MH-iSAM2 using both simulated datasets and real-world experiments, demonstrating its improvements on the robustness and accuracy of SLAM systems.

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BibTeX entry:

@inproceedings{Hsiao19icra,
   author = {M. Hsiao and M. Kaess},
   title = {{MH-iSAM2}: Multi-hypothesis {iSAM} using {B}ayes Tree and
	Hypo-tree},
   booktitle = {Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA},
   pages = {1274-1280},
   address = {Montreal, Canada},
   month = may,
   year = {2019}
}
Last updated: March 26, 2021