SLAM2REF: Advancing Long-Term Mapping with 3D LiDAR and Reference Map Integration for Precise 6-DoF Trajectory Estimation and Map Extension

TL;DR

SLAM2REF is a novel framework that integrates 3D LiDAR, IMU data, and reference maps for precise long-term indoor localization and mapping, demonstrating robustness and accuracy in real-world scenarios.

Contribution

It introduces a new approach for automatic map alignment and extension using reference 3D maps and multi-session anchoring techniques, advancing long-term SLAM capabilities.

Findings

Outperforms current state-of-the-art methods in accuracy and robustness.

Effective in diverse indoor, GPS-denied environments.

Enhances digital map management for various applications.

Abstract

This paper presents a pioneering solution to the task of integrating mobile 3D LiDAR and inertial measurement unit (IMU) data with existing building information models or point clouds, which is crucial for achieving precise long-term localization and mapping in indoor, GPS-denied environments. Our proposed framework, SLAM2REF, introduces a novel approach for automatic alignment and map extension utilizing reference 3D maps. The methodology is supported by a sophisticated multi-session anchoring technique, which integrates novel descriptors and registration methodologies. Real-world experiments reveal the framework's remarkable robustness and accuracy, surpassing current state-of-the-art methods. Our open-source framework's significance lies in its contribution to resilient map data management, enhancing processes across diverse sectors such as construction site monitoring, emergency response, disaster management, and others, where fast-updated digital 3D maps contribute to better decision-making and productivity. Moreover, it offers advancements in localization and mapping research. Link to the repository: https://github.com/MigVega/SLAM2REF, Data: https://doi.org/10.14459/2024mp1743877.