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

TL;DR

This paper introduces a novel map-centric 3D LiDAR SLAM framework that leverages continuous-time trajectories, surfel fusion, and robust loop closure to achieve dense, large-scale mapping with improved stability and sensor fusion capabilities.

Contribution

It presents a new map-centric SLAM method with continuous-time trajectories, surfel fusion, and a robust loop closure model, addressing limitations of existing systems.

Findings

Effective dense mapping in large-scale environments

Robust loop closure improves stability

Handles multi-modal sensor fusion and motion distortion

Abstract

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.