Real-time LiDAR SLAM System with Simultaneous Localization and Meshing
The paper "SLAMesh: Real-time LiDAR Simultaneous Localization and Meshing" presents a novel approach to LiDAR-based simultaneous localization and mapping (SLAM). This system is uniquely designed to integrate real-time mesh mapping with localization, aimed at addressing limitations of current point-cloud based SLAM systems.
Introduction and Motivation
Current LiDAR SLAM systems commonly employ point-cloud maps to represent 3D environments. These maps, despite seeming dense, are sparse upon closer inspection, requiring additional processing for applications like navigation. Mesh models, due to lower memory costs and dense mapping capabilities, have gained interest. Traditional approaches create mesh maps offline, separating meshing from localization, and failing to leverage the benefits that simultaneous processes could offer. This paper introduces the first CPU-only real-time LiDAR SLAM system that builds and utilizes mesh maps on-the-fly, integrating both localization and meshing.
Methodology
The SLAMesh system is distinguished by its use of a Gaussian process (GP) reconstruction for real-time mesh map development. This approach enables efficient building, registration, and updating of mesh maps directly in a continuous process. The system operates on several public datasets, demonstrating the capacity of SLAMesh to run at approximately 40Hz with superior localization and meshing accuracy.
Meshing Strategy
The approach tackles the inherent complexity of mesh updating through a reconstruction and connection method. Vertices in the mesh are distributed uniformly through a local surface reconstruction via GP, enabling the establishment of a mesh map in real-time. This approach ensures fast data querying and reduces the inaccuracies associated with discretization.
Point-to-Mesh Registration
SLAMesh employs a point-to-mesh registration method, capitalizing on mesh face normals to enhance localization accuracy. This method establishes correspondences based on vertex locations, facilitating a robust registration process through effective utilization of normal information from mesh faces.
Mesh Management
Efficient mesh management is key to SLAMesh’s real-time performance. The system limits its updates to 1-D predictions, thereby maintaining efficient data structures. By structuring data into independent cells, SLAMesh further reduces update complexities, benefiting from parallel processing through multithreading.
Results
The experimental evaluation establishes SLAMesh’s superiority across various metrics. On public datasets, the system demonstrates high-quality mesh maps and lower computational overhead compared to existing methods. These results are supported by memory efficiency and processing time, validating SLAMesh's applicability for large-scale environments.
The numerical results indicate SLAMesh's average odometry errors at 0.676% in translation and 0.291 deg/100m in rotation, outperforming traditional point-cloud, NDT, and surfel-based methods. The mesh maps created by SLAMesh preserve detailed structures, achieving better precision and recall metrics, establishing their utility in both robotics and mapping applications.
Implications and Future Developments
The integration of real-time meshing with localization paves the way for SLAM systems that are more efficient and accurate, potentially transforming autonomous navigation and 3D reconstruction tasks. The methodology offers scalable solutions with reduced memory requirements, which are crucial for resource-constrained robotic platforms.
Future research may explore extending SLAMesh capabilities to various sensor modalities, enhancing robustness in dynamic environments, and further reducing computational loads through optimized parallel processing schemes. The open-source nature of the SLAMesh system also invites contributions, fostering ongoing innovations in real-time SLAM and meshing applications.