# UV-SLAM: Unconstrained Line-based SLAM Using Vanishing Points for Structural Mapping

arXiv: https://arxiv.org/abs/2112.13515 · HTML: https://mlsanity.com/paper/2112.13515/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Dec 2021.

## Built on by (most cited first, 23 of 23)

- [3D Line Mapping Revisited](https://mlsanity.com/paper/2303.17504/) (2023)
- [Robust Incremental Structure-from-Motion with Hybrid Features](https://mlsanity.com/paper/2409.19811/) (2024)
- [Enhancing Diffusion Models with 3D Perspective Geometry Constraints](https://mlsanity.com/paper/2312.00944/) (2023)
- [3D Neural Edge Reconstruction](https://mlsanity.com/paper/2405.19295/) (2024)
- [An N-Point Linear Solver for Line and Motion Estimation with Event Cameras](https://mlsanity.com/paper/2404.00842/) (2024)
- [Struct-MDC: Mesh-Refined Unsupervised Depth Completion Leveraging Structural Regularities from Visual SLAM](https://mlsanity.com/paper/2204.13877/) (2022)
- [Curve-Aware Gaussian Splatting for 3D Parametric Curve Reconstruction](https://mlsanity.com/paper/2506.21401/) (2025)
- [PAPL-SLAM: Principal Axis-Anchored Monocular Point-Line SLAM](https://mlsanity.com/paper/2410.12324/) (2024)
- [MambaGlue: Fast and Robust Local Feature Matching With Mamba](https://mlsanity.com/paper/2502.00462/) (2025)
- [LF-PGVIO: A Visual-Inertial-Odometry Framework for Large Field-of-View Cameras using Points and Geodesic Segments](https://mlsanity.com/paper/2306.06663/) (2023)
- [Tightly-Coupled LiDAR-Visual SLAM Based on Geometric Features for Mobile Agents](https://mlsanity.com/paper/2307.07763/) (2023)
- [Salient Sparse Visual Odometry With Pose-Only Supervision](https://mlsanity.com/paper/2404.04677/) (2024)
- [RSL-BA: Rolling Shutter Line Bundle Adjustment](https://mlsanity.com/paper/2408.05409/) (2024)
- [LightGlueStick: a Fast and Robust Glue for Joint Point-Line Matching](https://mlsanity.com/paper/2510.16438/) (2025)
- [Galibr: Targetless LiDAR-Camera Extrinsic Calibration Method via Ground Plane Initialization](https://mlsanity.com/paper/2406.11599/) (2024)
- [FR-SLAM: A SLAM Improvement Method Based on Floor Plan Registration](https://mlsanity.com/paper/2407.11299/) (2024)
- [Grab-3D: Detecting AI-Generated Videos from 3D Geometric Temporal Consistency](https://mlsanity.com/paper/2512.13665/) (2025)
- [JVLDLoc: a Joint Optimization of Visual-LiDAR Constraints and Direction Priors for Localization in Driving Scenario](https://mlsanity.com/paper/2208.09777/) (2022)
- [Brain-Inspired Visual Odometry: Balancing Speed and Interpretability through a System of Systems Approach](https://mlsanity.com/paper/2312.13162/) (2023)
- [POPL-KF: A Pose-Only Geometric Representation-Based Kalman Filter for Point-Line-Based Visual-Inertial Odometry](https://mlsanity.com/paper/2602.06425/) (2026)
- [GaussianFlow SLAM: Monocular Gaussian Splatting SLAM Guided by GaussianFlow](https://mlsanity.com/paper/2604.15612/) (2026)
- [Unified and Efficient Point-Line Local Features](https://mlsanity.com/paper/2608.19894/) (2026)
- [Robust Structureless Monocular Visual Inertial Initialization Exploiting Line Features and Vanishing Points](https://mlsanity.com/paper/2609.21186/) (2026)

## Abstract (arXiv)

In feature-based simultaneous localization and mapping (SLAM), line features complement the sparsity of point features, making it possible to map the surrounding environment structure. Existing approaches utilizing line features have primarily employed a measurement model that uses line re-projection. However, the direction vectors used in the 3D line mapping process cannot be corrected because the line measurement model employs only the lines' normal vectors in the Pl"ucker coordinate. As a result, problems like degeneracy that occur during the 3D line mapping process cannot be solved. To tackle the problem, this paper presents a UV-SLAM, which is an unconstrained line-based SLAM using vanishing points for structural mapping. This paper focuses on using structural regularities without any constraints, such as the Manhattan world assumption. For this, we use the vanishing points that can be obtained from the line features. The difference between the vanishing point observation calculated through line features in the image and the vanishing point estimation calculated through the direction vector is defined as a residual and added to the cost function of optimization-based SLAM. Furthermore, through Fisher information matrix rank analysis, we prove that vanishing point measurements guarantee a unique mapping solution. Finally, we demonstrate that the localization accuracy and mapping quality are improved compared to the state-of-the-art algorithms using public datasets.