# PL-VINS: Real-Time Monocular Visual-Inertial SLAM with Point and Line Features

arXiv: https://arxiv.org/abs/2009.07462 · HTML: https://mlsanity.com/paper/2009.07462/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Sep 2020.

## Built on by (most cited first, 15 of 15)

- [DeepLSD: Line Segment Detection and Refinement with Deep Image Gradients](https://mlsanity.com/paper/2212.07766/) (2022)
- [PL-EVIO: Robust Monocular Event-based Visual Inertial Odometry with Point and Line Features](https://mlsanity.com/paper/2209.12160/) (2022)
- [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)
- [Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features](https://mlsanity.com/paper/2103.01501/) (2021)
- [RD-VIO: Robust Visual-Inertial Odometry for Mobile Augmented Reality in Dynamic Environments](https://mlsanity.com/paper/2310.15072/) (2023)
- [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)
- [LightGlueStick: a Fast and Robust Glue for Joint Point-Line Matching](https://mlsanity.com/paper/2510.16438/) (2025)
- [Perception-Aware Perching on Powerlines with Multirotors](https://mlsanity.com/paper/2202.06434/) (2022)
- [Sparse Optical Flow-Based Line Feature Tracking](https://mlsanity.com/paper/2204.03331/) (2022)
- [EVOLIN Benchmark: Evaluation of Line Detection and Association](https://mlsanity.com/paper/2303.05162/) (2023)
- [IDLS: Inverse Depth Line based Visual-Inertial SLAM](https://mlsanity.com/paper/2304.11748/) (2023)
- [A Simple yet Effective Subway Self-positioning Method based on Aerial-view Sleeper Detection](https://mlsanity.com/paper/2410.09492/) (2024)
- [POPL-KF: A Pose-Only Geometric Representation-Based Kalman Filter for Point-Line-Based Visual-Inertial Odometry](https://mlsanity.com/paper/2602.06425/) (2026)
- [OTPL-VIO: Robust Visual-Inertial Odometry with Optimal Transport Line Association and Adaptive Uncertainty](https://mlsanity.com/paper/2603.09653/) (2026)
- [Unified and Efficient Point-Line Local Features](https://mlsanity.com/paper/2608.19894/) (2026)

## Abstract (arXiv)

Leveraging line features to improve localization accuracy of point-based visual-inertial SLAM (VINS) is gaining interest as they provide additional constraints on scene structure. However, real-time performance when incorporating line features in VINS has not been addressed. This paper presents PL-VINS, a real-time optimization-based monocular VINS method with point and line features, developed based on the state-of-the-art point-based VINS-Mono citevins. We observe that current works use the LSD citelsd algorithm to extract line features; however, LSD is designed for scene shape representation instead of the pose estimation problem, which becomes the bottleneck for the real-time performance due to its high computational cost. In this paper, a modified LSD algorithm is presented by studying a hidden parameter tuning and length rejection strategy. The modified LSD can run at least three times as fast as LSD. Further, by representing space lines with the Pl"ucker coordinates, the residual error in line estimation is modeled in terms of the point-to-line distance, which is then minimized by iteratively updating the minimum four-parameter orthonormal representation of the Pl"ucker coordinates. Experiments in a public benchmark dataset show that the localization error of our method is 12-16% less than that of VINS-Mono at the same pose update frequency. %For the benefit of the community, The source code of our method is available at: https://github.com/cnqiangfu/PL-VINS.