# Incremental Multimodal Surface Mapping via Self-Organizing Gaussian Mixture Models

arXiv: https://arxiv.org/abs/2309.10900 · HTML: https://mlsanity.com/paper/2309.10900/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 39 references that are in the map.
- First posted on arXiv 19 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [iMAP: Implicit Mapping and Positioning in Real-Time](https://mlsanity.com/paper/2103.12352/) (2021)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [iSDF: Real-Time Neural Signed Distance Fields for Robot Perception](https://mlsanity.com/paper/2204.02296/) (2022)
- [Approximate Differentiable Rendering with Algebraic Surfaces](https://mlsanity.com/paper/2207.10606/) (2022)
- [SHINE-Mapping: Large-Scale 3D Mapping Using Sparse Hierarchical Implicit Neural Representations](https://mlsanity.com/paper/2210.02299/) (2022)
- [LAMP: Large-Scale Autonomous Mapping and Positioning for Exploration of Perceptually-Degraded Subterranean Environments](https://mlsanity.com/paper/2003.01744/) (2020)
- [CERBERUS: Autonomous Legged and Aerial Robotic Exploration in the Tunnel and Urban Circuits of the DARPA Subterranean Challenge](https://mlsanity.com/paper/2201.07067/) (2022)
- [FSMI: Fast computation of Shannon Mutual Information for information-theoretic mapping](https://mlsanity.com/paper/1905.02238/) (2019)
- [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)
- [Multi-Modal Active Perception for Information Gathering in Science Missions](https://mlsanity.com/paper/1712.09716/) (2017)
- [Rapid and High-Fidelity Subsurface Exploration with Multiple Aerial Robots](https://mlsanity.com/paper/2012.10788/) (2020)

## Built on by (most cited first, 5 of 5)

- [SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAM](https://mlsanity.com/paper/2312.02126/) (2023)
- [The Role of Predictive Uncertainty and Diversity in Embodied AI and Robot Learning](https://mlsanity.com/paper/2405.03164/) (2024)
- [Distance and Collision Probability Estimation from Gaussian Surface Models](https://mlsanity.com/paper/2402.00186/) (2024)
- [GI-SLAM: Gaussian-Inertial SLAM](https://mlsanity.com/paper/2503.18275/) (2025)
- [Dynamic Hand Gesture Recognition for Robot Manipulator Tasks](https://mlsanity.com/paper/2601.12918/) (2026)

## Abstract (arXiv)

This letter describes an incremental multimodal surface mapping methodology, which represents the environment as a continuous probabilistic model. This model enables high-resolution reconstruction while simultaneously compressing spatial and intensity point cloud data. The strategy employed in this work utilizes Gaussian mixture models (GMMs) to represent the environment. While prior GMM-based mapping works have developed methodologies to determine the number of mixture components using information-theoretic techniques, these approaches either operate on individual sensor observations, making them unsuitable for incremental mapping, or are not real-time viable, especially for applications where high-fidelity modeling is required. To bridge this gap, this letter introduces a spatial hash map for rapid GMM submap extraction combined with an approach to determine relevant and redundant data in a point cloud. These contributions increase computational speed by an order of magnitude compared to state-of-the-art incremental GMM-based mapping. In addition, the proposed approach yields a superior tradeoff in map accuracy and size when compared to state-of-the-art mapping methodologies (both GMM- and not GMM-based). Evaluations are conducted using both simulated and real-world data. The software is released open-source to benefit the robotics community.