SLAM2REF: Advancing Long-Term Mapping with 3D LiDAR and Reference Map Integration for Precise 6-DoF Trajectory Estimation and Map Extension (2408.15948v1)

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.

• The paper presents SLAM2REF, an innovative framework that integrates 3D LiDAR data with reference maps for accurate 6-DoF trajectory estimation.
• It employs multi-session SLAM techniques and advanced descriptors to correct motion distortion and extend maps in challenging indoor settings.
• Experimental results on the ConSLAM dataset demonstrate significant improvements in registration accuracy and robust change detection.

Leveraging 3D LiDAR in Long-Term Mapping: SLAM2REF's Approach

This paper introduces an advanced solution for the integration of 3D LiDAR data with reference maps to enhance long-term mapping capabilities. The framework, SLAM2REF, is designed for GPS-denied environments, primarily focusing on achieving precise 6-DoF trajectory estimation and map extension in indoor settings. This is achieved through multi-session SLAM techniques and novel descriptor methodologies.

Technological Advancements and Methodology

SLAM2REF effectively bridges the gap between real-time kinematic data and existing building information models or point clouds. This integration is pivotal for maintaining accurate, updated maps over extended periods, addressing a critical need in fields such as construction monitoring, emergency response, and disaster management.

Key components of the SLAM2REF framework include:

1. Reference Map Generation: An efficient method for converting large-scale 3D maps or BIM models into session data for rapid place recognition. This involves creating Occupancy Grid Maps (OGMs) from BIM or point cloud data, which are then used to simulate 3D LiDAR scans.
2. Query Session Creation: Motion distortion correction is a significant aspect where SLAM2REF stands out, utilizing advanced LiDAR-Inertial Odometry (LIO) systems to generate undistorted data from real-world input, thereby enhancing the accuracy of subsequent processing stages.
3. Multi-Session Anchoring and Optimization: The framework introduces a robust process for integrating inter-session loop closures using the Indoor Scan Context (ISC) and YawGICP. This approach ensures a cohesive alignment and correction of the map data, even in scenarios with significant discrepancies or deviations between scans and reference models.
4. Change Detection and Map Update: SLAM2REF can detect changes in the environment by comparing updated point clouds with reference maps, identifying positive and negative differences to visualize additions or removals in the 3D model.

Experimental Validation

The methodology was validated using the ConSLAM dataset, a comprehensive real-world dataset designed for SLAM in construction environments. The experiments demonstrate that SLAM2REF outperforms existing methods in terms of accuracy and robustness, making significant improvements in trajectory estimation and map accuracy through its advanced registration and optimization processes.

Future Implications and Conclusions

SLAM2REF offers substantial advancements in long-term mapping technologies, with potential applications extending well beyond construction into areas like urban planning and maintenance of complex infrastructure networks. By enabling precise alignment and continuous updating of maps, it paves the way for more autonomous operations in challenging environments.

The framework's open-source nature further allows for continuous development and integration with other technologies, potentially enhancing localization and navigation systems in various robotic applications. As the reliance on 3D mapping and sensor fusion increases, SLAM2REF represents a crucial step in improving the accuracy and utility of such systems in dynamically changing environments.