  In the context of 3D mapping, larger and larger point clouds are acquired
with LIDAR sensors. The Iterative Closest Point (ICP) algorithm is used to
align these point clouds. However, its complexity is directly dependent of the
number of points to process. Several strategies exist to address this problem
by reducing the number of points. However, they tend to underperform with
non-uniform density, large sensor noise, spurious measurements, and large-scale
point clouds, which is the case in mobile robotics. This paper presents a novel
sampling algorithm for registration in ICP algorithm based on spectral
decomposition analysis and called Spectral Decomposition Filter (SpDF). It
preserves geometric information along the topology of point clouds and is able
to scale to large environments with non-uniform density. The effectiveness of
our method is validated and illustrated by quantitative and qualitative
experiments on various environments.
