  The concept of continuous-time trajectory representation has brought
increased accuracy and efficiency to multi-modal sensor fusion in modern SLAM.
However, regardless of these advantages, its offline property caused by the
requirement of global batch optimization is critically hindering its relevance
for real-time and life-long applications. In this paper, we present a dense
map-centric SLAM method based on a continuous-time trajectory to cope with this
problem. The proposed system locally functions in a similar fashion to
conventional Continuous-Time SLAM (CT-SLAM). However, it removes the need for
global trajectory optimization by introducing map deformation. The
computational complexity of the proposed approach for loop closure does not
depend on the operation time, but only on the size of the space it explored
before the loop closure. It is therefore more suitable for long term operation
compared to the conventional CT-SLAM. Furthermore, the proposed method reduces
uncertainty in the reconstructed dense map by using probabilistic surface
element (surfel) fusion. We demonstrate that the proposed method produces
globally consistent maps without global batch trajectory optimization, and
effectively reduces LiDAR noise by surfel fusion.
