Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems

Structured Light Illumination (SLI) systems have been used for reliable indoor dense 3D scanning via phase triangulation. However, mobile SLI 360 $^{\circ }$ reconstruction demand point cloud registration, involving high computational complexity. In this letter, we propose a based Simultaneous Localization and Mapping (Phase-SLAM) framework fast accurate sensor pose estimation object reconstruction. The novelty of work is threefold: (1) developing reprojection model from points to 2D data towards registration with low complexity; (2) local optimizer achieve (odometry) using the derived Jacobian matrix 6 DoF variables; (3) compressive comparison method high-efficiency loop closure detection. whole Phase-SLAM pipeline then exploited existing global graph optimization techniques. We build datasets both unreal simulation platform robotic arm system in real-world verify proposed approach. experiment results demonstrate that outperforms other state-of-the-art methods terms efficiency accuracy