# GIRA: Gaussian Mixture Models for Inference and Robot Autonomy

arXiv: https://arxiv.org/abs/2307.00071 · HTML: https://mlsanity.com/paper/2307.00071/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 30 Jun 2023.

## Builds on (most cited first, 11 of 11)

- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Voxgraph: Globally Consistent, Volumetric Mapping using Signed Distance Function Submaps](https://mlsanity.com/paper/2004.13154/) (2020)
- [LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments](https://mlsanity.com/paper/2003.01744/) (2020)
- [UFOMap: An Efficient Probabilistic 3D Mapping Framework That Embraces the Unknown](https://mlsanity.com/paper/2003.04749/) (2020)
- [Multi-Resolution 3D Mapping with Explicit Free Space Representation for Fast and Accurate Mobile Robot Motion Planning](https://mlsanity.com/paper/2010.07929/) (2020)
- [Autonomous Cave Surveying with an Aerial Robot](https://mlsanity.com/paper/2003.13883/) (2020)
- [Probabilistic Point Cloud Modeling via Self-Organizing Gaussian Mixture Models](https://mlsanity.com/paper/2302.00047/) (2023)
- [Efficient volumetric mapping of multi-scale environments using wavelet-based compression](https://mlsanity.com/paper/2306.01279/) (2023)

## Built on by (most cited first, 1 of 1)

- [A Geometric Gaussian Mixture Representation of Plane Curves](https://mlsanity.com/paper/2606.06505/) (2026)

## Abstract (arXiv)

This paper introduces the open-source framework, GIRA, which implements fundamental robotics algorithms for reconstruction, pose estimation, and occupancy modeling using compact generative models. Compactness enables perception in the large by ensuring that the perceptual models can be communicated through low-bandwidth channels during large-scale mobile robot deployments. The generative property enables perception in the small by providing high-resolution reconstruction capability. These properties address perception needs for diverse robotic applications, including multi-robot exploration and dexterous manipulation. State-of-the-art perception systems construct perceptual models via multiple disparate pipelines that reuse the same underlying sensor data, which leads to increased computation, redundancy, and complexity. GIRA bridges this gap by providing a unified perceptual modeling framework using Gaussian mixture models (GMMs) as well as a novel systems contribution, which consists of GPU-accelerated functions to learn GMMs 10-100x faster compared to existing CPU implementations. Because few GMM-based frameworks are open-sourced, this work seeks to accelerate innovation and broaden adoption of these techniques.