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

Y Structured Light Illumination (SLI) systems have been used for reliable indoor dense 3D scanning via phase triangulation. However, mobile SLI systems for 360 degrees 3D reconstruction demand 3D point cloud registration, involving high computational complexity. In this letter, 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.

Automated summary

In this study, a phase-based SLAM framework is proposed for fast and accurate estimation of SLI sensor pose and 3D object reconstruction. By developing a reprojection model, a local optimizer, and a compressive phase comparison method, phase registration with low computational complexity and efficient loop closure detection are achieved. The experimental results demonstrate that the proposed Phase-SLAM outperforms other methods in terms of efficiency and accuracy.