Trifo-VIO: Robust and Efficient Stereo Visual Inertial Odometry using Points and Lines
This work addresses robustness in VIO for robotics and autonomous systems in low-texture or lighting-changing scenarios, but it is incremental as it builds on existing filtering-based approaches with added features.
The paper tackles the problem of robust visual-inertial odometry in challenging environments by proposing Trifo-VIO, which uses points and lines and a lightweight loop closing technique, achieving competitive accuracy with state-of-the-art methods on datasets like EuRoC and Trifo Ironsides.
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.