# Language-Embedded Gaussian Splats (LEGS): Incrementally Building Room-Scale Representations with a Mobile Robot

arXiv: https://arxiv.org/abs/2409.18108 · HTML: https://mlsanity.com/paper/2409.18108/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2024.

## Built on by (most cited first, 21 of 21)

- [MemER: Scaling Up Memory for Robot Control via Experience Retrieval](https://mlsanity.com/paper/2510.20328/) (2025)
- [4D LangSplat: 4D Language Gaussian Splatting via Multimodal Large Language Models](https://mlsanity.com/paper/2503.10437/) (2025)
- [PolaRiS: Scalable Real-to-Sim Evaluations for Generalist Robot Policies](https://mlsanity.com/paper/2512.16881/) (2025)
- [3D Vision-Language Gaussian Splatting](https://mlsanity.com/paper/2410.07577/) (2024)
- [Viser: Imperative, Web-based 3D Visualization in Python](https://mlsanity.com/paper/2507.22885/) (2025)
- [Neural Fields in Robotics: A Survey](https://mlsanity.com/paper/2410.20220/) (2024)
- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [Adapt3R: Adaptive 3D Scene Representation for Domain Transfer in Imitation Learning](https://mlsanity.com/paper/2503.04877/) (2025)
- [Feature4X: Bridging Any Monocular Video to 4D Agentic AI with Versatile Gaussian Feature Fields](https://mlsanity.com/paper/2503.20776/) (2025)
- [EmbodiedSplat: Personalized Real-to-Sim-to-Real Navigation with Gaussian Splats from a Mobile Device](https://mlsanity.com/paper/2509.17430/) (2025)
- [Mobi-π: Mobilizing Your Robot Learning Policy](https://mlsanity.com/paper/2505.23692/) (2025)
- [Multimodal LLM Guided Exploration and Active Mapping using Fisher Information](https://mlsanity.com/paper/2410.17422/) (2024)
- [Bayesian Fields: Task-driven Open-Set Semantic Gaussian Splatting](https://mlsanity.com/paper/2503.05949/) (2025)
- [OpenGS-Fusion: Open-Vocabulary Dense Mapping with Hybrid 3D Gaussian Splatting for Refined Object-Level Understanding](https://mlsanity.com/paper/2508.01150/) (2025)
- [SLARM: Streaming and Language-Aligned Reconstruction Model for Dynamic Scenes](https://mlsanity.com/paper/2603.22893/) (2026)
- [CoRe-GS: Coarse-to-Refined Gaussian Splatting with Semantic Object Focus](https://mlsanity.com/paper/2509.04859/) (2025)
- [Fix False Transparency by Noise Guided Splatting](https://mlsanity.com/paper/2510.15736/) (2025)
- [Lang3D-XL: Language Embedded 3D Gaussians for Large-scale Scenes](https://mlsanity.com/paper/2512.07807/) (2025)
- [A Study of the Framework and Real-World Applications of Language Embedding for 3D Scene Understanding](https://mlsanity.com/paper/2508.05064/) (2025)
- [UniFField: A Generalizable Unified Neural Feature Field for Visual, Semantic, and Spatial Uncertainties in Any Scene](https://mlsanity.com/paper/2510.06754/) (2025)
- [TrianguLang: Geometry-Aware Semantic Consensus for Pose-Free 3D Localization](https://mlsanity.com/paper/2603.08096/) (2026)

## Abstract (arXiv)

Building semantic 3D maps is valuable for searching for objects of interest in offices, warehouses, stores, and homes. We present a mapping system that incrementally builds a Language-Embedded Gaussian Splat (LEGS): a detailed 3D scene representation that encodes both appearance and semantics in a unified representation. LEGS is trained online as a robot traverses its environment to enable localization of open-vocabulary object queries. We evaluate LEGS on 4 room-scale scenes where we query for objects in the scene to assess how LEGS can capture semantic meaning. We compare LEGS to LERF and find that while both systems have comparable object query success rates, LEGS trains over 3.5x faster than LERF. Results suggest that a multi-camera setup and incremental bundle adjustment can boost visual reconstruction quality in constrained robot trajectories, and suggest LEGS can localize open-vocabulary and long-tail object queries with up to 66% accuracy.