A robust C++ sensor fusion library for online localization using factor graphs and least squares optimization.
libRSF is a robust sensor fusion library written in C++ that enables developers to solve localization problems using factor graphs and least squares optimization. It provides tools for modeling estimation problems, handling non-Gaussian noise with robust error models, and processing data in real-time with a sliding window filter. The library is designed to address challenges in accurate position estimation for applications like robotics and autonomous vehicles.
Robotics engineers, autonomous systems developers, and researchers working on real-time localization and sensor fusion problems. It's particularly suited for those implementing or experimenting with factor graph-based estimation in C++.
Developers choose libRSF for its combination of robustness, real-time capabilities, and flexibility in handling complex sensor fusion tasks. Its integration with Ceres Solver, predefined localization cost functions, and support for self-tuning Gaussian mixtures provide a powerful, open-source alternative to proprietary sensor fusion solutions.
A robust sensor fusion library for online localization.
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Includes self-tuning Gaussian mixtures and other models to effectively handle non-Gaussian noise and outliers, as highlighted in the robust error models section and related papers.
Supports online applications with marginalization for efficient processing, enabling accurate real-time localization for robotics and autonomous systems.
Integrates with the Ceres Solver for powerful least squares optimization, providing a reliable and efficient optimization framework as the core solver.
Allows estimation problems to be modeled intuitively as factor graphs, simplifying the development of complex sensor fusion systems described in the documentation.
Offers ready-to-use cost functions for various localization scenarios, reducing implementation time for common tasks like GNSS-based positioning.
Officially tested and supported only for Ubuntu 22.04 and 24.04, as stated in the README, limiting its usability in cross-platform development environments.
Requires manual installation and compilation of multiple dependencies like Ceres Solver from source, which can be complex and time-consuming, especially for beginners.
Closely tied to specific academic publications, which may result in a steeper learning curve and less focus on production-ready features or general usability.
Usage documentation is sparse and assumes prior knowledge of factor graphs, with guides linked to separate markdown files that might not cover all practical use cases comprehensively.