A high-performance Continuous-Time Gaussian Belief Propagation (CT-GBP) framework with fully automated symbolic factor generation and seamless Ceres interoperability targeting distributed SLAM operations!
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Hyperion is a novel, modular, distributed, high-performance optimization framework targeting both discrete- and
continuous-time SLAM (Simultaneous Localization and Mapping) applications. It stands out by offering the
first open-source C++ implementation of a Gaussian-Belief-Propagation-based Non-Linear Least Squares solver, which,
in turn, offers native support for decentralized, stochastic inference on factor graphs. In addition, Hyperion also
extends SymForce to automate the generation of high-performance
implementations for spline-related residuals from symbolic, high-level expressions. This results in the fastest,
Ceres-interoperable B- and Z-Spline implementations, achieving speedups of up to 110x over
previous state-of-the-art methods.
Hyperion will officially be released prior to
the European Conference on Computer Vision 2024 (ECCV 2024),
scheduled until September 29, 2024. Thus, stay tuned for updates and prepare to accelerate and streamline your own
continuous-time SLAM framework or spline-related research with Hyperion.
Hyperion has been accepted for publication in the European Conference on Computer Vision 2024 (ECCV 2024). Until the final version of record becomes available, please cite its archived version as follows:
@misc{Hug:etal:arXiv2024,
title={{Hyperion -- A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAM}},
author={David Hug and Ignacio Alzugaray and Margarita Chli},
year={2024},
eprint={2407.07074},
archivePrefix={arXiv},
primaryClass={cs.RO},
url={https://arxiv.org/abs/2407.07074},
}
