使用数字孪生的无漂移视觉SLAM
Drift-free Visual SLAM using Digital Twins
AI总结：
针对传统VIO/VSLAM长期漂移、GPS在室内及城市不可靠的问题，提出将VIO/VSLAM生成的稀疏3D点云与数字孪生点对平面匹配的方法，提供6-DoF全局测量，实验证明其优于VIO-GPS系统且抗视角变化鲁棒性更强。
摘要：
城市环境中的全局一致定位对自动驾驶汽车、无人机等自主系统以及视障人士辅助技术至关重要。传统的视觉惯性里程计（Visual-Inertial Odometry, VIO）和视觉同时定位与地图构建（Visual Simultaneous Localization and Mapping, VSLAM）方法虽能满足局部位姿估计需求，但由于依赖局部传感器数据，长期运行时会产生漂移。GPS虽能抵消这种漂移，但在室内不可用，在城市区域也往往不可靠。另一种方案是通过视觉特征匹配将相机定位到已有的3D地图上，该方法可实现厘米级的精确定位，但受限于当前视图与地图之间的视觉相似性。本文提出了一种新方法，通过点对平面匹配将VIO/VSLAM系统生成的稀疏3D点云与数字孪生对齐，无需视觉数据关联，即可实现精确且全局一致的定位。所提方法提供了一个6自由度（6-DoF）全局测量值，可紧密集成到VIO/VSLAM系统中。在高保真GPS模拟器和无人机采集的真实世界数据上开展的实验表明，该方法优于当前最优的VIO-GPS系统，且相比当前最优的视觉SLAM系统，对视角变化具有更强的鲁棒性。
英文摘要：
Globally-consistent localization in urban environments is crucial for autonomous systems such as self-driving vehicles and drones, as well as assistive technologies for visually impaired people. Traditional Visual-Inertial Odometry (VIO) and Visual Simultaneous Localization and Mapping (VSLAM) methods, though adequate for local pose estimation, suffer from drift in the long term due to reliance on local sensor data. While GPS counteracts this drift, it is unavailable indoors and often unreliable in urban areas. An alternative is to localize the camera to an existing 3D map using visual-feature matching. This can provide centimeter-level accurate localization but is limited by the visual similarities between the current view and the map. This paper introduces a novel approach that achieves accurate and globally-consistent localization by aligning the sparse 3D point cloud generated by the VIO/VSLAM system to a digital twin using point-to-plane matching; no visual data association is needed. The proposed method provides a 6-DoF global measurement tightly integrated into the VIO/VSLAM system. Experiments run on a high-fidelity GPS simulator and real-world data collected from a drone demonstrate that our approach outperforms state-of-the-art VIO-GPS systems and offers superior robustness against viewpoint changes compared to the state-of-the-art Visual SLAM systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
