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

Ming Hsiao and Michael Kaess
Conference Paper, Proceedings of Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA, May, 2019

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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.


@conference{Hsiao-2019-116370,
author = {Ming Hsiao and Michael Kaess},
title = {MH-iSAM2: Multi-hypothesis iSAM using Bayes Tree and Hypo-tree},
booktitle = {Proceedings of Proc. IEEE Intl. Conf. on Robotics and Automation, ICRA},
year = {2019},
month = {May},
} 2019-07-08T08:08:34-04:00