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

TL;DR

This paper introduces Phase-SLAM, a novel phase-based SLAM framework for mobile structured light systems that enhances 3D reconstruction accuracy and speed through efficient phase registration, pose estimation, and loop closure detection.

Contribution

It presents a low-complexity reprojection model, a local optimizer for pose estimation, and a high-efficiency loop closure method, advancing mobile SLI system capabilities.

Findings

Outperforms state-of-the-art in accuracy and efficiency

Validated on simulation and real-world datasets

Achieves fast 3D reconstruction with reliable pose estimation

Abstract

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.