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

TL;DR

Trifo-VIO is a robust stereo visual-inertial odometry system that combines points and lines for improved accuracy, introduces a lightweight loop closing method, and is validated on a new high-quality dataset and existing benchmarks.

Contribution

The paper introduces a novel filtering-based stereo VIO system utilizing points and lines, along with a lightweight loop closing technique and a new dataset for evaluation.

Findings

Outperforms state-of-the-art methods on EuRoC and Trifo Ironsides datasets.

Effectively reduces drift with the proposed loop closure method.

Demonstrates robustness in challenging environments with low texture and lighting changes.

Abstract

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.