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