144803 20251113150205.0 doi 10.1109/TIV.2024.3418525 sideral 139594 ART-2025-139594 eng Liu, Changxiang PE-VINS: Accurate Monocular Visual-Inertial SLAM With Point-Edge Features 2025 Access copy available to the general public Unrestricted Visual-Inertial Navigation Systems (VINS) is a significant undertaking in computer vision, robotics, and autonomous driving. Currently, point-line VINS have attracted significant attention due to their increased robustness and accuracy compared to point-only VINS. However, their effectiveness relies on the existence of clear line structures within the scene. Point-line VINS may become inaccurate or fail when scenes contain scattered lines or other features like arcs. Moreover, extracting and matching line features can bring computational overheads due to complex geometric models. In order to address VINS challenges without the overheads related to lines, we propose a novel approach, denoted as PE-VINS, which adds edge features to point-based VINS. Our proposed employs edge features in scenes to establish extra correspondences between views and then enhance its accuracy and robustness. Our method identifies edge features using image gradients and selects the most informative ones in the front end. We leverage sparse optical flow to track selected edge features and triangulate them using the initial pose predicted by the Inertial Measurement Unit (IMU). In the back end, we present a novel edge feature residual formulation that differs from the traditional reprojection residual. We tightly couple the new edge residual with the reprojection and IMU preintegration residual to better refine camera poses. We test our PE-VINS on public datasets, and our results show that it outperforms existing point-line-based methods and achieves state-of-the-art VINS performance. The code will be released at https://github.com/BlueAkoasm/PE-VINS . info:eu-repo/semantics/openAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 14.3 2024 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE 6 / 204 = 0.029 2024 Q1 T1 TRANSPORTATION SCIENCE & TECHNOLOGY 4 / 77 = 0.052 2024 Q1 T1 ENGINEERING, ELECTRICAL & ELECTRONIC 7 / 366 = 0.019 2024 Q1 T1 2.821 2024 Artificial Intelligence 2024 Q1 Control and Optimization 2024 Q1 Automotive Engineering 2024 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion Yu, Hongshan Cheng, Panfei Sun, Wei Civera, Javier Universidad de Zaragoza (orcid)0000-0003-1368-1151 Chen, Xieyuanli 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. 10, 2 (2025), 808 - 818 IEEE trans. intell. veh. IEEE transactions on intelligent vehicles 2379-8858 14016338 http://zaguan.unizar.es/record/144803/files/texto_completo.pdf Postprint 3661713 http://zaguan.unizar.es/record/144803/files/texto_completo.jpg?subformat=icon icon Postprint oai:zaguan.unizar.es:144803 articulos driver 2025-11-13-15:00:48 ARTICLE