2025/09/13 by Kimia Khabiri, Parsa Hosseininejad, Khabiri, Kimia +7 · 1 citation
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Distributed #FOS: Computer and information sciences #Parallel #Robotics (cs.RO) #Robotics and Sensor-Based Localization #Visual Attention and Saliency Detection #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2509.10757
openalex publication_date 2025/09/13 · openalex created_date 2025/10/12 · openalex updated_date 2026/07/28
The tracking module of a visual-inertial SLAM system processes incoming image frames and IMU data to estimate the position of the frame in relation to the map. It is important for the tracking to complete in a timely manner for each frame to avoid poor localization or tracking loss. We therefore present a new approach which leverages GPU computing power to accelerate time-consuming components of tracking in order to improve its performance. These components include stereo feature matching and local map tracking. We implement our design inside the ORB-SLAM3 tracking process using CUDA. Our evaluation demonstrates an overall improvement in tracking performance of up to 2.8x on a desktop and Jetson Xavier NX board in stereo-inertial mode, using the well-known SLAM datasets EuRoC and TUM-VI.