PLVS：一种结合点、线、体素建图与3D增量分割的SLAM系统
PLVS: A SLAM System with Points, Lines, Volumetric Mapping, and 3D Incremental Segmentation
AI总结：
本文提出实时SLAM系统PLVS，融合稀疏点线特征SLAM、并行体素建图与3D增量分割，支持多类相机，提出新型线重投影误差提升精度，经公开数据集验证且已开源。
摘要：
本文提出PLVS：一个融合稀疏SLAM、体素建图与3D无监督增量分割的实时系统。PLVS即点（Points）、线（Lines）、体素建图（Volumetric mapping）与分割（Segmentation）的缩写，支持RGB-D和双目相机，可可选配备IMU。其SLAM模块基于关键帧，提取并跟踪稀疏点与线段作为特征。体素建图与SLAM前端并行运行，通过融合从关键帧反投影的点云生成已探索环境的3D重建结果，PLVS集成并支持多种体素建图方法。我们提出一种新型重投影误差用于线段的光束法平差，该误差利用可用深度信息稳定线段端点的位置估计。PLVS框架中还为RGB-D相机实现并集成了一种基于几何的增量分割方法。我们在多个公开数据集上对PLVS框架进行了定性与定量评估。附录详述了所采用的双目线三角测量方法，并推导了我们用于线误差项的雅可比矩阵。该软件以开源形式发布。
英文摘要：
This document presents PLVS: a real-time system that leverages sparse SLAM, volumetric mapping, and 3D unsupervised incremental segmentation. PLVS stands for Points, Lines, Volumetric mapping, and Segmentation. It supports RGB-D and Stereo cameras, which may be optionally equipped with IMUs. The SLAM module is keyframe-based, and extracts and tracks sparse points and line segments as features. Volumetric mapping runs in parallel with respect to the SLAM front-end and generates a 3D reconstruction of the explored environment by fusing point clouds backprojected from keyframes. Different volumetric mapping methods are supported and integrated in PLVS. We use a novel reprojection error to bundle-adjust line segments. This error exploits available depth information to stabilize the position estimates of line segment endpoints. An incremental and geometric-based segmentation method is implemented and integrated for RGB-D cameras in the PLVS framework. We present qualitative and quantitative evaluations of the PLVS framework on some publicly available datasets. The appendix details the adopted stereo line triangulation method and provides a derivation of the Jacobians we used for line error terms. The software is available as open-source.
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