# Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and Lines

arXiv: https://arxiv.org/abs/1803.02403 · HTML: https://mlsanity.com/paper/1803.02403/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2018.

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

- [Avoiding Degeneracy for Monocular Visual SLAM with Point and Line Features](https://mlsanity.com/paper/2103.01501/) (2021)
- [Sparse Optical Flow-Based Line Feature Tracking](https://mlsanity.com/paper/2204.03331/) (2022)
- [Vision-based localization methods under GPS-denied conditions](https://mlsanity.com/paper/2211.11988/) (2022)
- [POPL-KF: A Pose-Only Geometric Representation-Based Kalman Filter for Point-Line-Based Visual-Inertial Odometry](https://mlsanity.com/paper/2602.06425/) (2026)
- [Know-Your-Scene (KYS)-SLAM: Hierarchical Semantic-Motion Priors for Feature Matching in Stereo Visual SLAM](https://mlsanity.com/paper/2609.27509/) (2026)

## Abstract (arXiv)

In this paper, we present the Trifo Visual Inertial Odometry (Trifo-VIO), a tightly-coupled filtering-based stereo VIO system using both points and lines. Line features help improve system robustness in challenging scenarios when point features cannot be reliably detected or tracked, e.g. low-texture environment or lighting change. In addition, we propose a novel lightweight filtering-based loop closing technique to reduce accumulated drift without global bundle adjustment or pose graph optimization. We formulate loop closure as EKF updates to optimally relocate the current sliding window maintained by the filter to past keyframes. We also present the Trifo Ironsides dataset, a new visual-inertial dataset, featuring high-quality synchronized stereo camera and IMU data from the Ironsides sensor [3] with various motion types and textures and millimeter-accuracy groundtruth. To validate the performance of the proposed system, we conduct extensive comparison with state-of-the-art approaches (OKVIS, VINS-MONO and S-MSCKF) using both the public EuRoC dataset and the Trifo Ironsides dataset.