用GPU加速视觉惯性SLAM的跟踪，速度提升2.8倍
FastTrack: GPU-Accelerated Tracking for Visual SLAM
- 利用CUDA在GPU上加速立体特征匹配与局部地图跟踪
- 在桌面和Jetson Xavier NX上实现最高2.8倍性能提升
- 适合需要实时定位的嵌入式视觉导航系统
视觉惯性SLAM系统的跟踪模块需处理图像帧与IMU数据，以估计当前帧相对于地图的位置。为避免定位失败或跟踪丢失，必须及时完成每帧的计算。本文提出一种新方法，利用GPU计算能力加速跟踪中耗时的操作，包括立体特征匹配和局部地图跟踪。我们在ORB-SLAM3的跟踪流程中集成该设计，采用CUDA实现。评估结果表明，在使用EuRoC和TUM-VI数据集的立体-惯性模式下，桌面端与Jetson Xavier NX板卡均实现最高2.8倍的整体跟踪性能提升。
原文摘要 · Abstract (English)
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.
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