  Map-centric SLAM utilizes elasticity as a means of loop closure. This
approach reduces the cost of loop closure while still provides large-scale
fusion-based dense maps, when compared to the trajectory-centric SLAM
approaches. In this paper, we present a novel framework for 3D LiDAR-based
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 multi-modal sensor fusion and LiDAR motion distortion. This is
accomplished through the use of a local Continuous-Time (CT) trajectory
representation. Also, our surface resolution preservative matching algorithm
and Wishart-based surfel fusion model enables non-redundant 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.
