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 systems for 360$^{\circ }$ 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.

I. Introduction

The SLI technology has been widely used for high-precision 3D scanning for many industrial applications with the camera-projector pair. There are usually two approaches for SLI systems to achieve 360 3D reconstruction: controlled motion based and free motion based [1]. The former uses a servo motor to rotate the object along a pre-defined trajectory for multiple view scanning; the latter estimates sensor motions through local and global point cloud registration, such as Iterative Closest Point (ICP) and associated variants [2], [3]. The free-motion approach is advantageous in its flexibility but incurs high computational complexity and demands a high storage capacity.