# Thegra: Graph-based SLAM for Thermal Imagery

arXiv: https://arxiv.org/abs/2602.08531 · HTML: https://mlsanity.com/paper/2602.08531/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 46 references that are in the map.
- First posted on arXiv 9 Feb 2026.

## Builds on (most cited first, 16 of 16)

- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM: a Versatile and Accurate Monocular SLAM System](https://mlsanity.com/paper/1502.00956/) (2015)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [LightGlue: Local Feature Matching at Light Speed](https://mlsanity.com/paper/2306.13643/) (2023)
- [Direct Sparse Odometry](https://mlsanity.com/paper/1607.02565/) (2016)
- [MAGSAC: marginalizing sample consensus](https://mlsanity.com/paper/1803.07469/) (2018)
- [XFeat: Accelerated Features for Lightweight Image Matching](https://mlsanity.com/paper/2404.19174/) (2024)
- [GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://mlsanity.com/paper/2304.02008/) (2023)
- [ViViD++: Vision for Visibility Dataset](https://mlsanity.com/paper/2204.06183/) (2022)
- [Sparse Depth Enhanced Direct Thermal-infrared SLAM Beyond the Visible Spectrum](https://mlsanity.com/paper/1902.10892/) (2019)
- [DeepTIO: A Deep Thermal-Inertial Odometry with Visual Hallucination](https://mlsanity.com/paper/1909.07231/) (2019)
- [Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks](https://mlsanity.com/paper/2104.07196/) (2021)
- [TP-TIO: A Robust Thermal-Inertial Odometry with Deep ThermalPoint](https://mlsanity.com/paper/2012.03455/) (2020)
- [Visual-Thermal Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments](https://mlsanity.com/paper/1903.01656/) (2019)
- [Edge-based Monocular Thermal-Inertial Odometry in Visually Degraded Environments](https://mlsanity.com/paper/2210.10033/) (2022)
- [Towards Long Term SLAM on Thermal Imagery](https://mlsanity.com/paper/2403.19885/) (2024)

## Abstract (arXiv)

Thermal imaging provides a practical sensing modality for visual SLAM in visually degraded environments such as low illumination, smoke, or adverse weather. However, thermal imagery often exhibits low texture, low contrast, and high noise, complicating feature-based SLAM. In this work, we propose a sparse monocular graph-based SLAM system for thermal imagery that leverages general-purpose learned features -- the SuperPoint detector and LightGlue matcher, trained on large-scale visible-spectrum data to improve cross-domain generalization. To adapt these components to thermal data, we introduce a preprocessing pipeline to enhance input suitability and modify core SLAM modules to handle sparse and outlier-prone feature matches. We further incorporate keypoint confidence scores from SuperPoint into a confidence-weighted factor graph to improve estimation robustness. Evaluations on public thermal datasets demonstrate that the proposed system achieves reliable performance without requiring dataset-specific training or fine-tuning a desired feature detector, given the scarcity of quality thermal data. Code will be made available upon publication.