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The concept of continuous-time trajectory representation has broughtincreased accuracy and efficiency to multi-modal sensor fusion in modern SLAM.However, regardless of these advantages, its offline property caused by therequirement of global batch optimization is critically hindering its relevancefor real-time and life-long applications. In this paper, we present a densemap-centric SLAM method based on a continuous-time trajectory to cope with thisproblem. The proposed system locally functions in a similar fashion toconventional Continuous-Time SLAM (CT-SLAM). However, it removes the need forglobal trajectory optimization by introducing map deformation. Thecomputational complexity of the proposed approach for loop closure does notdepend on the operation time, but only on the size of the space it exploredbefore the loop closure. It is therefore more suitable for long term operationcompared to the conventional CT-SLAM. Furthermore, the proposed method reducesuncertainty in the reconstructed dense map by using probabilistic surfaceelement (surfel) fusion. We demonstrate that the proposed method producesglobally consistent maps without global batch trajectory optimization, andeffectively reduces LiDAR noise by surfel fusion.

Elastic LiDAR Fusion: Dense Map-Centric Continuous-Time SLAM