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hyperion package from hyperion repo

hyperion

Package Summary

Tags No category tags.
Version 1.0.0
License BSD-3-Clause
Build type AMENT_CMAKE
Use RECOMMENDED

Repository Summary

Checkout URI https://github.com/vis4rob-lab/hyperion.git
VCS Type git
VCS Version main
Last Updated 2024-11-24
Dev Status UNMAINTAINED
CI status No Continuous Integration
Released UNRELEASED
Tags No category tags.
Contributing Help Wanted (0)
Good First Issues (0)
Pull Requests to Review (0)

Package Description

Symbolic Continuous-Time Gaussian Belief Propagation Framework with Ceres Interoperability

Additional Links

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Maintainers

  • David Hug

Authors

No additional authors.


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!

Report Issues or Request Features



About

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](https://github.com/symforce-org/symforce) to automate the generation of high-performance implementations for spline-related residuals from symbolic, high-level expressions. This results in the fastest, [Ceres](http://ceres-solver.org)-interoperable B- and Z-Spline implementations, achieving speedups of up to 110x over previous state-of-the-art methods. Links to [Paper](https://www.ecva.net/papers/eccv_2024/papers_ECCV/papers/04347.pdf), [Poster](https://eccv2024.ecva.net/media/PosterPDFs/ECCV%202024/2444.png), and [Video](https://www.youtube.com/watch?v=iubwqTzA8Do).

Citation

Hyperion was presented at the [European Conference on Computer Vision 2024 (ECCV 2024)](https://eccv2024.ecva.net). Until the final version of record becomes available, please cite its archived version as follows: ``` @inproceedings{Hug:etal:ECCV2024, title={{Hyperion -- A fast, versatile symbolic Gaussian Belief Propagation framework for Continuous-Time SLAM}}, author={David Hug and Ignacio Alzugaray and Margarita Chli}, booktitle={Computer Vision -- ECCV 2024}, year={2024}, publisher={Springer Nature Switzerland}, address={Cham}, pages={215--231}, isbn={978-3-031-73404-5}, doi={10.1007/978-3-031-73404-5_13} } ```

Setup and Documentation

Additional documentation for installing and using Hyperion will be available soon. The framework comprises two main modules: a Python-based symbolic code generation module and an optimization module for performing inference on general factor graphs. Currently, three executables are provided: one for demonstrating how to [set up a minimization problem](apps/pose3_absolute/main.cpp) in Hyperion, and two others for running [tests](tests/src/hyperion_tests/tests_main.cpp) and [benchmarks](benchmarks/src/hyperion_benchmarks/benchmarks_main.cpp). Hyperion's API closely mirrors that of [Ceres](http://ceres-solver.org), offering familiarity for users of that library. Additionally, a [sample benchmark](benchmarks/benchmark_results.xml) run is included for reference, detailing the metrics reported in the paper.



Contact

Admin - David Hug, Leonhardstrasse 21, 8092 Zürich, ETH Zürich, Switzerland

License

Hyperion is distributed under the BSD-3-Clause License


CHANGELOG
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