Elasticity Meets Continuous-Time: Map-Centric Dense 3D LiDAR SLAM
This work addresses SLAM for robotics and autonomous systems, offering incremental improvements in map-centric approaches.
The paper tackles the problem of 3D LiDAR SLAM by introducing a map-centric framework that uses elasticity for loop closure, achieving reduced loop closure costs and large-scale dense maps, with demonstrations in simulation and real data showing utility and robustness.
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.