  Accurate and consistent construction of point clouds from LiDAR scanning data
is fundamental for 3D modeling applications. Current solutions, such as
multiview point cloud registration and LiDAR bundle adjustment, predominantly
depend on the local plane assumption, which may be inadequate in complex
environments lacking of planar geometries or substantial initial pose errors.
To mitigate this problem, this paper presents a LiDAR bundle adjustment with
progressive spatial smoothing, which is suitable for complex environments and
exhibits improved convergence capabilities. The proposed method consists of a
spatial smoothing module and a pose adjustment module, which combines the
benefits of local consistency and global accuracy. With the spatial smoothing
module, we can obtain robust and rich surface constraints employing smoothing
kernels across various scales. Then the pose adjustment module corrects all
poses utilizing the novel surface constraints. Ultimately, the proposed method
simultaneously achieves fine poses and parametric surfaces that can be directly
employed for high-quality point cloud reconstruction. The effectiveness and
robustness of our proposed approach have been validated on both simulation and
real-world datasets. The experimental results demonstrate that the proposed
method outperforms the existing methods and achieves better accuracy in complex
environments with low planar structures.
