论文标题
阶段链接：基于阶段的同时定位和移动结构光照明系统的映射
Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems
论文作者
论文摘要
结构化的光照射（SLI）系统已被用于通过相三角测量的可靠室内致密3D扫描。然而，360度重建需求3D点云登记的360度重建需求的移动SLI系统涉及较高的计算复杂性。在本文中，我们提出了一个基于相位的同时定位和映射（相lam）框架，以快速，准确的SLI传感器姿势估计和3D对象重建。这项工作的新颖性是三倍：（1）将重新投入模型从3D点到2D相数据，以低计算复杂性为相位注册； （2）开发局部优化器，以6 DOF变量的衍生雅各布矩阵实现SLI传感器姿势估计（探测率）； （3）开发一种压缩阶段比较方法，以实现高效环闭合检测。然后使用现有的全球姿势图优化技术利用整个相位链接管道。我们从虚幻仿真平台和现实世界中基于机器人的SLI系统构建数据集，以验证所提出的方法。实验结果表明，就姿势估计和3D重建的效率和准确性而言，所提出的阶段lam的表现优于其他最先进的方法。开源代码可在https://github.com/zhengxi-git/phase-slam上找到。
Structured Light Illumination (SLI) systems have been used for reliable indoor dense 3D scanning via phase triangulation. However, mobile SLI systems for 360 degree 3D reconstruction demand 3D point cloud registration, involving high computational complexity. In this paper, we propose a phase based Simultaneous Localization and Mapping (Phase-SLAM) framework for fast and accurate SLI sensor pose estimation and 3D object reconstruction. The novelty of this work is threefold: (1) developing a reprojection model from 3D points to 2D phase data towards phase registration with low computational complexity; (2) developing a local optimizer to achieve SLI sensor pose estimation (odometry) using the derived Jacobian matrix for the 6 DoF variables; (3) developing a compressive phase comparison method to achieve high-efficiency loop closure detection. The whole Phase-SLAM pipeline is then exploited using existing global pose graph optimization techniques. We build datasets from both the unreal simulation platform and a robotic arm based SLI system in real-world to verify the proposed approach. The experiment results demonstrate that the proposed Phase-SLAM outperforms other state-of-the-art methods in terms of the efficiency and accuracy of pose estimation and 3D reconstruction. The open-source code is available at https://github.com/ZHENGXi-git/Phase-SLAM.
