# PL-SLAM: a Stereo SLAM System through the Combination of Points and Line Segments

arXiv: https://arxiv.org/abs/1705.09479 · HTML: https://mlsanity.com/paper/1705.09479/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 May 2017.

## Built on by (most cited first, 30 of 59)

- [CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching](https://mlsanity.com/paper/2104.04314/) (2021)
- [GlueStick: Robust Image Matching by Sticking Points and Lines Together](https://mlsanity.com/paper/2304.02008/) (2023)
- [DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients](https://mlsanity.com/paper/2212.07766/) (2022)
- [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)
- [Holistically-Attracted Wireframe Parsing: From Supervised to Self-Supervised Learning](https://mlsanity.com/paper/2210.12971/) (2022)
- [ELSED: Enhanced Line SEgment Drawing](https://mlsanity.com/paper/2108.03144/) (2021)
- [Digging Into Uncertainty-based Pseudo-label for Robust Stereo Matching](https://mlsanity.com/paper/2307.16509/) (2023)
- [ManhattanSLAM: Robust Planar Tracking and Mapping Leveraging Mixture of Manhattan Frames](https://mlsanity.com/paper/2103.15068/) (2021)
- [Structure PLP-SLAM: Efficient Sparse Mapping and Localization using Point, Line and Plane for Monocular, RGB-D and Stereo Cameras](https://mlsanity.com/paper/2207.06058/) (2022)
- [Line as a Visual Sentence: Context-aware Line Descriptor for Visual Localization](https://mlsanity.com/paper/2109.04753/) (2021)
- [Pose Refinement with Joint Optimization of Visual Points and Lines](https://mlsanity.com/paper/2110.03940/) (2021)
- [PnLCalib: Sports Field Registration via Points and Lines Optimization](https://mlsanity.com/paper/2404.08401/) (2024)
- [Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features](https://mlsanity.com/paper/2103.01501/) (2021)
- [PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation](https://mlsanity.com/paper/2110.05839/) (2021)
- [Visual SLAM: What are the Current Trends and What to Expect?](https://mlsanity.com/paper/2210.10491/) (2022)
- [AdaStereo: An Efficient Domain-Adaptive Stereo Matching Approach](https://mlsanity.com/paper/2112.04974/) (2021)
- [Vision-Based Environmental Perception for Autonomous Driving](https://mlsanity.com/paper/2212.11453/) (2022)
- [Detecting Line Segments in Motion-blurred Images with Events](https://mlsanity.com/paper/2211.07365/) (2022)
- [Handbook on Leveraging Lines for Two-View Relative Pose Estimation](https://mlsanity.com/paper/2309.16040/) (2023)
- [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)
- [Fast and Robust Certifiable Estimation of the Relative Pose Between Two Calibrated Cameras](https://mlsanity.com/paper/2101.08524/) (2021)
- [Simultaneous Localisation and Mapping with Quadric Surfaces](https://mlsanity.com/paper/2203.08040/) (2022)
- [RWT-SLAM: Robust Visual SLAM for Highly Weak-textured Environments](https://mlsanity.com/paper/2207.03539/) (2022)
- [E-Graph: Minimal Solution for Rigid Rotation with Extensibility Graphs](https://mlsanity.com/paper/2207.10008/) (2022)
- [GRAND-SLAM: Local Optimization for Globally Consistent Large-Scale Multi-Agent Gaussian SLAM](https://mlsanity.com/paper/2506.18885/) (2025)
- [Revisiting Binary Local Image Description for Resource Limited Devices](https://mlsanity.com/paper/2108.08380/) (2021)
- [DynPL-SVO: A Robust Stereo Visual Odometry for Dynamic Scenes](https://mlsanity.com/paper/2205.08207/) (2022)
- [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)

## Abstract (arXiv)

Traditional approaches to stereo visual SLAM rely on point features to estimate the camera trajectory and build a map of the environment. In low-textured environments, though, it is often difficult to find a sufficient number of reliable point features and, as a consequence, the performance of such algorithms degrades. This paper proposes PL-SLAM, a stereo visual SLAM system that combines both points and line segments to work robustly in a wider variety of scenarios, particularly in those where point features are scarce or not well-distributed in the image. PL-SLAM leverages both points and segments at all the instances of the process: visual odometry, keyframe selection, bundle adjustment, etc. We contribute also with a loop closure procedure through a novel bag-of-words approach that exploits the combined descriptive power of the two kinds of features. Additionally, the resulting map is richer and more diverse in 3D elements, which can be exploited to infer valuable, high-level scene structures like planes, empty spaces, ground plane, etc. (not addressed in this work). Our proposal has been tested with several popular datasets (such as KITTI and EuRoC), and is compared to state of the art methods like ORB-SLAM, revealing a more robust performance in most of the experiments, while still running in real-time. An open source version of the PL-SLAM C++ code will be released for the benefit of the community.