  Online dense mapping of urban scenes forms a fundamental cornerstone for
scene understanding and navigation of autonomous vehicles. Recent advancements
in mapping methods are mainly based on NeRF, whose rendering speed is too slow
to meet online requirements. 3D Gaussian Splatting (3DGS), with its rendering
speed hundreds of times faster than NeRF, holds greater potential in online
dense mapping. However, integrating 3DGS into a street-view dense mapping
framework still faces two challenges, including incomplete reconstruction due
to the absence of geometric information beyond the LiDAR coverage area and
extensive computation for reconstruction in large urban scenes. To this end, we
propose HGS-Mapping, an online dense mapping framework in unbounded large-scale
scenes. To attain complete construction, our framework introduces Hybrid
Gaussian Representation, which models different parts of the entire scene using
Gaussians with distinct properties. Furthermore, we employ a hybrid Gaussian
initialization mechanism and an adaptive update method to achieve high-fidelity
and rapid reconstruction. To the best of our knowledge, we are the first to
integrate Gaussian representation into online dense mapping of urban scenes.
Our approach achieves SOTA reconstruction accuracy while only employing 66%
number of Gaussians, leading to 20% faster reconstruction speed.
