Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM

Map-centric SLAM utilizes elasticity as a means of loop closure. This approach reduces the cost of loop closure while still providing large-scale fusion-based dense maps, when compared to trajectory-centric SLAM approaches. In this article, we present a novel framework, named ElasticLiDAR++, for multimodal map-centric SLAM. Having the advantages of a map-centric approach, our method exhibits new features to overcome the shortcomings of existing systems associated with multimodal (LiDAR-inertial-visual) sensor fusion and LiDAR motion distortion. This is accomplished through the use of a local continuous-time trajectory representation. Also, our surface resolution preserving matching algorithm and normal-inverse-Wishart-based surfel fusion model enables nonredundant yet dense mapping. Furthermore, we present a robust metric loop closure model to make the approach stable regardless of where the loop closure occurs. Finally, we demonstrate our approach through both simulation and real data experiments using multiple sensor payload configurations and environments to illustrate its utility and robustness.

Automated summary

The article introduces a novel map-centric SLAM framework, ElasticLiDAR++, which overcomes the challenges of multimodal sensor fusion and LiDAR motion distortion. Using a local continuous-time trajectory representation, the method achieves nonredundant yet dense mapping through a surface resolution preserving matching algorithm and surfel fusion model.