# PL-CVIO: Point-Line Cooperative Visual-Inertial Odometry

arXiv: https://arxiv.org/abs/2311.05717 · HTML: https://mlsanity.com/paper/2311.05717/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Nov 2023.

## Built on by (most cited first, 5 of 5)

- [Representing 3D sparse map points and lines for camera relocalization](https://mlsanity.com/paper/2402.18011/) (2024)
- [NeRF-VIO: Map-Based Visual-Inertial Odometry with Initialization Leveraging Neural Radiance Fields](https://mlsanity.com/paper/2503.07952/) (2025)
- [VBGS-SLAM: Variational Bayesian Gaussian Splatting Simultaneous Localization and Mapping](https://mlsanity.com/paper/2604.02696/) (2026)
- [Improved 3D Point-Line Mapping Regression for Camera Relocalization](https://mlsanity.com/paper/2502.20814/) (2025)
- [An Efficient Approach for Muscle Segmentation and 3D Reconstruction Using Keypoint Tracking in MRI Scan](https://mlsanity.com/paper/2507.08690/) (2025)

## Abstract (arXiv)

Low-feature environments are one of the main Achilles' heels of geometric computer vision (CV) algorithms. In most human-built scenes often with low features, lines can be considered complements to points. In this paper, we present a multi-robot cooperative visual-inertial navigation system (VINS) using both point and line features. By utilizing the covariance intersection (CI) update within the multi-state constraint Kalman filter (MSCKF) framework, each robot exploits not only its own point and line measurements, but also constraints of common point and common line features observed by its neighbors. The line features are parameterized and updated by utilizing the Closest Point representation. The proposed algorithm is validated extensively in both Monte-Carlo simulations and a real-world dataset. The results show that the point-line cooperative visual-inertial odometry (PL-CVIO) outperforms the independent MSCKF and our previous work CVIO in both low-feature and rich-feature environments.