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.

翻译：本文提出PLVS系统：一种结合稀疏SLAM、体素建图与三维无监督增量分割的实时系统。PLVS代表点(Points)、线(Lines)、体素建图(Volumetric mapping)与分割(Segmentation)。该系统支持RGB-D相机与立体相机，并可选配惯性测量单元(IMU)。SLAM模块采用关键帧架构，提取并跟踪稀疏点与线段特征。体素建图与SLAM前端并行运行，通过融合关键帧反投影得到的点云，生成探索环境的3D重建。PLVS集成了多种体素建图方法。我们提出新型重投影误差用于线段光束法平差，该误差利用深度信息稳定线段端点位置估计。针对RGB-D相机，在PLVS框架中实现并集成了基于几何的增量式分割方法。本文在部分公开数据集上对PLVS框架进行了定性与定量评估。附录详述了采用的立体线段三角测量方法，并推导了线段误差项使用的雅可比矩阵。相关软件已开源发布。