  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.
