# Sparse Depth Enhanced Direct Thermal-infrared SLAM Beyond the Visible Spectrum

arXiv: https://arxiv.org/abs/1902.10892 · HTML: https://mlsanity.com/paper/1902.10892/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Feb 2019.

## Built on by (most cited first, 22 of 22)

- [Complementary Random Masking for RGB-Thermal Semantic Segmentation](https://mlsanity.com/paper/2303.17386/) (2023)
- [Self-Supervised Depth and Ego-Motion Estimation for Monocular Thermal Video Using Multi-Spectral Consistency Loss](https://mlsanity.com/paper/2103.00760/) (2021)
- [Graph-based Thermal-Inertial SLAM with Probabilistic Neural Networks](https://mlsanity.com/paper/2104.07196/) (2021)
- [Fieldscale: Locality-Aware Field-based Adaptive Rescaling for Thermal Infrared Image](https://mlsanity.com/paper/2405.15395/) (2024)
- [Unbiased Estimator for Distorted Conics in Camera Calibration](https://mlsanity.com/paper/2403.04583/) (2024)
- [Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios](https://mlsanity.com/paper/2402.11826/) (2024)
- [ThermoHands: A Benchmark for 3D Hand Pose Estimation from Egocentric Thermal Images](https://mlsanity.com/paper/2403.09871/) (2024)
- [Towards Long Term SLAM on Thermal Imagery](https://mlsanity.com/paper/2403.19885/) (2024)
- [Deep Depth Estimation from Thermal Image: Dataset, Benchmark, and Challenges](https://mlsanity.com/paper/2503.22060/) (2025)
- [TherA: Thermal-Aware Visual-Language Prompting for Controllable RGB-to-Thermal Infrared Translation](https://mlsanity.com/paper/2602.19430/) (2026)
- [Thermal Chameleon: Task-Adaptive Tone-mapping for Radiometric Thermal-Infrared images](https://mlsanity.com/paper/2410.18340/) (2024)
- [Bridging Spectral-wise and Multi-spectral Depth Estimation via Geometry-guided Contrastive Learning](https://mlsanity.com/paper/2503.00793/) (2025)
- [Unsupervised Visible-light Images Guided Cross-Spectrum Depth Estimation from Dual-Modality Cameras](https://mlsanity.com/paper/2205.00257/) (2022)
- [BuFF: Burst Feature Finder for Light-Constrained 3D Reconstruction](https://mlsanity.com/paper/2209.09470/) (2022)
- [MrGS: Multi-modal Radiance Fields with 3D Gaussian Splatting for RGB-Thermal Novel View Synthesis](https://mlsanity.com/paper/2511.22997/) (2025)
- [DUALVISION: RGB-Infrared Multimodal Large Language Models for Robust Visual Reasoning](https://mlsanity.com/paper/2604.18829/) (2026)
- [ThermalLoc: A Vision Transformer-Based Approach for Robust Thermal Camera Relocalization in Large-Scale Environments](https://mlsanity.com/paper/2506.18268/) (2025)
- [Thermo-LIO: A Novel Multi-Sensor Integrated System for Structural Health Monitoring](https://mlsanity.com/paper/2601.08977/) (2026)
- [Multimodal Signal Processing For Thermo-Visible-Lidar Fusion In Real-time 3D Semantic Mapping](https://mlsanity.com/paper/2601.09578/) (2026)
- [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)
- [Thermal Image Refinement with Depth Estimation using Recurrent Networks for Monocular ORB-SLAM3](https://mlsanity.com/paper/2603.14998/) (2026)

## Abstract (arXiv)

In this paper, we propose a thermal-infrared simultaneous localization and mapping (SLAM) system enhanced by sparse depth measurements from Light Detection and Ranging (LiDAR). Thermal-infrared cameras are relatively robust against fog, smoke, and dynamic lighting conditions compared to RGB cameras operating under the visible spectrum. Due to the advantages of thermal-infrared cameras, exploiting them for motion estimation and mapping is highly appealing. However, operating a thermal-infrared camera directly in existing vision-based methods is difficult because of the modality difference. This paper proposes a method to use sparse depth measurement for 6-DOF motion estimation by directly tracking under 14- bit raw measurement of the thermal camera. In addition, we perform a refinement to improve the local accuracy and include a loop closure to maintain global consistency. The experimental results demonstrate that the system is not only robust under various lighting conditions such as day and night, but also overcomes the scale problem of monocular cameras. The video is available at https://youtu.be/oO7lT3uAzLc.