# Towards Long Term SLAM on Thermal Imagery

arXiv: https://arxiv.org/abs/2403.19885 · HTML: https://mlsanity.com/paper/2403.19885/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 17 of its 43 references that are in the map.
- First posted on arXiv 28 Mar 2024.

## Builds on (most cited first, 17 of 17)

- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras](https://mlsanity.com/paper/2108.10869/) (2021)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [VINS-Mono: A Robust and Versatile Monocular Visual-Inertial State Estimator](https://mlsanity.com/paper/1708.03852/) (2017)
- [DISK: Learning local features with policy gradient](https://mlsanity.com/paper/2006.13566/) (2020)
- [LIFT: Learned Invariant Feature Transform](https://mlsanity.com/paper/1603.09114/) (2016)
- [R2D2: Repeatable and Reliable Detector and Descriptor](https://mlsanity.com/paper/1906.06195/) (2019)
- [R2Former: Unified Retrieval and Reranking Transformer for Place Recognition](https://mlsanity.com/paper/2304.03410/) (2023)
- [GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://mlsanity.com/paper/2304.02008/) (2023)
- [Visual-Inertial Mapping with Non-Linear Factor Recovery](https://mlsanity.com/paper/1904.06504/) (2019)
- [Sparse Depth Enhanced Direct Thermal-infrared SLAM Beyond the Visible Spectrum](https://mlsanity.com/paper/1902.10892/) (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)
- [Edge-based Monocular Thermal-Inertial Odometry in Visually Degraded Environments](https://mlsanity.com/paper/2210.10033/) (2022)
- [Design and Evaluation of a Generic Visual SLAM Framework for Multi-Camera Systems](https://mlsanity.com/paper/2210.07315/) (2022)
- [SRVIO: Super Robust Visual Inertial Odometry for dynamic environments and challenging Loop-closure conditions](https://mlsanity.com/paper/2201.05386/) (2022)

## Built on by (most cited first, 4 of 4)

- [MrGS: Multi-modal Radiance Fields with 3D Gaussian Splatting for RGB-Thermal Novel View Synthesis](https://mlsanity.com/paper/2511.22997/) (2025)
- [Thegra: Graph-based SLAM for Thermal Imagery](https://mlsanity.com/paper/2602.08531/) (2026)
- [LST-SLAM: A Stereo Thermal SLAM System for Kilometer-Scale Dynamic Environments](https://mlsanity.com/paper/2602.20925/) (2026)
- [Route-MHT: Multimodal Transformer Guardrails for Thermal Visual Place Recognition](https://mlsanity.com/paper/2607.04745/) (2026)

## Abstract (arXiv)

Visual SLAM with thermal imagery, and other low contrast visually degraded environments such as underwater, or in areas dominated by snow and ice, remain a difficult problem for many state of the art (SOTA) algorithms. In addition to challenging front-end data association, thermal imagery presents an additional difficulty for long term relocalization and map reuse. The relative temperatures of objects in thermal imagery change dramatically from day to night. Feature descriptors typically used for relocalization in SLAM are unable to maintain consistency over these diurnal changes. We show that learned feature descriptors can be used within existing Bag of Word based localization schemes to dramatically improve place recognition across large temporal gaps in thermal imagery. In order to demonstrate the effectiveness of our trained vocabulary, we have developed a baseline SLAM system, integrating learned features and matching into a classical SLAM algorithm. Our system demonstrates good local tracking on challenging thermal imagery, and relocalization that overcomes dramatic day to night thermal appearance changes. Our code and datasets are available here: https://github.com/neufieldrobotics/IRSLAM_Baseline