Geometry Preserving Sampling Method Based on Spectral Decomposition for Large-Scale Environments
Résumé
In the context of 3D mapping, larger and larger point clouds are acquired with lidar
sensors. Although pleasing to the eye, dense maps are not necessarily tailored for
practical applications. For instance, in a surface inspection scenario, keeping geometric
information such as the edges of objects is essential to detect cracks, whereas very
dense areas of very little information such as the ground could hinder the main goal
of the application. 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 based on spectral
decomposition analysis to derive local density measures for each geometric primitive. The
proposed method, called Spectral Decomposition Filter (SpDF), identifies and preserves
geometric information along the topology of point clouds and is able to scale to large
environments with a non-uniform density. Finally, qualitative and quantitative experiments
verify the feasibility of our method and present a large-scale evaluation of SpDF with other
seven point cloud sampling algorithms, in the context of the 3D registration problem
using the Iterative Closest Point (ICP) algorithm on real-world datasets. Results show
that a compression ratio up to 97 % can be achieved when accepting a registration
error within the range accuracy of the sensor, here for large scale environments of less
than 2 c
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