# OMCL: Open-vocabulary Monte Carlo Localization

arXiv: https://arxiv.org/abs/2512.15557 · HTML: https://mlsanity.com/paper/2512.15557/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2025.

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

- [Instant Colorization of Gaussian Splats](https://mlsanity.com/paper/2604.17155/) (2026)

## Abstract (arXiv)

Robust robot localization is an important prerequisite for navigation, but it becomes challenging when the map and robot measurements are obtained from different sensors. Prior methods are often tailored to specific environments, relying on closed-set semantics or fine-tuned features. In this work, we extend Monte Carlo Localization with vision-language features, allowing OMCL to robustly compute the likelihood of visual observations given a camera pose and a 3D map created from posed RGB-D images or aligned point clouds. These open-vocabulary features enable us to associate observations and map elements from different modalities, and to natively initialize global localization through natural language descriptions of nearby objects. We evaluate our approach using Matterport3D and Replica for indoor scenes and demonstrate generalization on SemanticKITTI for outdoor scenes.