# GMMap: Memory-Efficient Continuous Occupancy Map Using Gaussian Mixture Model

arXiv: https://arxiv.org/abs/2306.03740 · HTML: https://mlsanity.com/paper/2306.03740/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jun 2023.

## Built on by (most cited first, 6 of 6)

- [Evidential Semantic Mapping in Off-road Environments with Uncertainty-aware Bayesian Kernel Inference](https://mlsanity.com/paper/2403.14138/) (2024)
- [Distance and Collision Probability Estimation from Gaussian Surface Models](https://mlsanity.com/paper/2402.00186/) (2024)
- [LiteFat: Lightweight Spatio-Temporal Graph Learning for Real-Time Driver Fatigue Detection](https://mlsanity.com/paper/2507.21756/) (2025)
- [Wireless Communication as an Information Sensor for Multi-agent Cooperative Perception: A Survey](https://mlsanity.com/paper/2505.00747/) (2025)
- [A Real-Time Multi-Model Parametric Representation of Point Clouds](https://mlsanity.com/paper/2509.14773/) (2025)
- [G2SR: Geometric Methods for Fast and Memory-Efficient Gaussian-based Surface Reconstruction](https://mlsanity.com/paper/2607.14470/) (2026)

## Abstract (arXiv)

Energy consumption of memory accesses dominates the compute energy in energy-constrained robots which require a compact 3D map of the environment to achieve autonomy. Recent mapping frameworks only focused on reducing the map size while incurring significant memory usage during map construction due to multi-pass processing of each depth image. In this work, we present a memory-efficient continuous occupancy map, named GMMap, that accurately models the 3D environment using a Gaussian Mixture Model (GMM). Memory-efficient GMMap construction is enabled by the single-pass compression of depth images into local GMMs which are directly fused together into a globally-consistent map. By extending Gaussian Mixture Regression to model unexplored regions, occupancy probability is directly computed from Gaussians. Using a low-power ARM Cortex A57 CPU, GMMap can be constructed in real-time at up to 60 images per second. Compared with prior works, GMMap maintains high accuracy while reducing the map size by at least 56%, memory overhead by at least 88%, DRAM access by at least 78%, and energy consumption by at least 69%. Thus, GMMap enables real-time 3D mapping on energy-constrained robots.