# AirSLAM: An Efficient and Illumination-Robust Point-Line Visual SLAM System

arXiv: https://arxiv.org/abs/2408.03520 · HTML: https://mlsanity.com/paper/2408.03520/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Aug 2024.

## Built on by (most cited first, 26 of 26)

- [Reconstructing 4D Spatial Intelligence: A Survey](https://mlsanity.com/paper/2507.21045/) (2025)
- [Bundle Adjustment in the Eager Mode](https://mlsanity.com/paper/2409.12190/) (2024)
- [PAPL-SLAM: Principal Axis-Anchored Monocular Point-Line SLAM](https://mlsanity.com/paper/2410.12324/) (2024)
- [UP-SLAM: Adaptively Structured Gaussian SLAM with Uncertainty Prediction in Dynamic Environments](https://mlsanity.com/paper/2505.22335/) (2025)
- [Video Understanding: From Geometry and Semantics to Unified Models](https://mlsanity.com/paper/2603.17840/) (2026)
- [LiteVLoc: Map-Lite Visual Localization for Image Goal Navigation](https://mlsanity.com/paper/2410.04419/) (2024)
- [NFL-BA: Near-Field Light Bundle Adjustment for SLAM in Dynamic Lighting](https://mlsanity.com/paper/2412.13176/) (2024)
- [ForestVO: Enhancing Visual Odometry in Forest Environments through ForestGlue](https://mlsanity.com/paper/2504.01261/) (2025)
- [DOA: A Degeneracy Optimization Agent with Adaptive Pose Compensation Capability based on Deep Reinforcement Learning](https://mlsanity.com/paper/2507.19742/) (2025)
- [UNO: Unified Self-Supervised Monocular Odometry for Platform-Agnostic Deployment](https://mlsanity.com/paper/2506.07013/) (2025)
- [ROVER: Robust Loop Closure Verification with Trajectory Prior in Repetitive Environments](https://mlsanity.com/paper/2508.13488/) (2025)
- [Enhancing Scene Coordinate Regression with Efficient Keypoint Detection and Sequential Information](https://mlsanity.com/paper/2412.06488/) (2024)
- [Oracle-Guided Masked Contrastive Reinforcement Learning for Visuomotor Policies](https://mlsanity.com/paper/2510.05692/) (2025)
- [UniVector: Unified Vector Extraction via Instance-Geometry Interaction](https://mlsanity.com/paper/2510.13234/) (2025)
- [KM-ViPE: Online Tightly Coupled Vision-Language-Geometry Fusion for Open-Vocabulary Semantic SLAM](https://mlsanity.com/paper/2512.01889/) (2025)
- [SuperEIO: Self-Supervised Event Feature Learning for Event Inertial Odometry](https://mlsanity.com/paper/2503.22963/) (2025)
- [Why Not Replace? Sustaining Long-Term Visual Localization via Handcrafted-Learned Feature Collaboration on CPU](https://mlsanity.com/paper/2505.18652/) (2025)
- [CSMapping: Scalable Crowdsourced Semantic Mapping and Topology Inference for Autonomous Driving](https://mlsanity.com/paper/2512.03510/) (2025)
- [Co-PLNet: A Collaborative Point-Line Network for Prompt-Guided Wireframe Parsing](https://mlsanity.com/paper/2601.18252/) (2026)
- [LST-SLAM: A Stereo Thermal SLAM System for Kilometer-Scale Dynamic Environments](https://mlsanity.com/paper/2602.20925/) (2026)
- [OTPL-VIO: Robust Visual-Inertial Odometry with Optimal Transport Line Association and Adaptive Uncertainty](https://mlsanity.com/paper/2603.09653/) (2026)
- [DINO-VO: Learning Where to Focus for Enhanced State Estimation](https://mlsanity.com/paper/2604.04055/) (2026)
- [DL-VINS-Factory: A Modular Framework for Learned Visual Front-Ends in Visual-Inertial SLAM](https://mlsanity.com/paper/2607.01757/) (2026)
- [Unified and Efficient Point-Line Local Features](https://mlsanity.com/paper/2608.19894/) (2026)
- [Stable and Scalable Bundle Adjustment of Holistic 3D Structures](https://mlsanity.com/paper/2609.04026/) (2026)
- [NSFlow: End-to-End Differentiable Neuro-Symbolic Optical Flow for Visual Odometry](https://mlsanity.com/paper/2609.06074/) (2026)

## Abstract (arXiv)

In this paper, we present an efficient visual SLAM system designed to tackle both short-term and long-term illumination challenges. Our system adopts a hybrid approach that combines deep learning techniques for feature detection and matching with traditional backend optimization methods. Specifically, we propose a unified convolutional neural network (CNN) that simultaneously extracts keypoints and structural lines. These features are then associated, matched, triangulated, and optimized in a coupled manner. Additionally, we introduce a lightweight relocalization pipeline that reuses the built map, where keypoints, lines, and a structure graph are used to match the query frame with the map. To enhance the applicability of the proposed system to real-world robots, we deploy and accelerate the feature detection and matching networks using C++ and NVIDIA TensorRT. Extensive experiments conducted on various datasets demonstrate that our system outperforms other state-of-the-art visual SLAM systems in illumination-challenging environments. Efficiency evaluations show that our system can run at a rate of 73Hz on a PC and 40Hz on an embedded platform. Our implementation is open-sourced: https://github.com/sair-lab/AirSLAM.