This paper presents a visual SLAM system that uses both points and lines for robust camera localization, and simultaneously performs a piece-wise planar reconstruction (PPR) of the environment to provide a structural map in real-time. One of the biggest challenges in parallel tracking and mapping with a monocular camera is to keep the scale consistent when reconstructing the geometric primitives. This further introduces difficulties in graph optimization of the bundle adjustment (BA) step. We solve these problems by proposing several run-time optimizations on the reconstructed lines and planes. Our system is able to run with depth and stereo sensors in addition to the monocular setting. Our proposed SLAM tightly incorporates the semantic and geometric features to boost both frontend pose tracking and backend map optimization. We evaluate our system exhaustively on various datasets, and show that we outperform state-of-the-art methods in terms of trajectory precision. The code of PLP-SLAM has been made available in open-source for the research community (https://github.com/PeterFWS/Structure-PLP-SLAM).

翻译：本文提出一种视觉SLAM系统，它同时利用点和线实现鲁棒的相机定位，并对环境进行分段平面重建（PPR），实时生成结构化地图。使用单目相机进行并行跟踪与建图的最大挑战之一，是在重建几何图元时保持尺度一致性，这进一步给光束法平差（BA）步骤的图优化带来了困难。我们通过在重建的线和面上提出若干运行时优化来解决这些问题。除单目模式外，本系统还可与深度传感器和立体传感器协同运行。我们所提出的SLAM系统紧密融合语义特征与几何特征，以提升前端位姿跟踪和后端地图优化能力。我们在多种数据集上对系统进行了全面评估，结果表明我们在轨迹精度上优于现有最先进方法。PLP-SLAM的代码已开源供研究社区使用（https://github.com/PeterFWS/Structure-PLP-SLAM）。