Inproceedings,
Smoothed Normal Distribution Transform for Efficient Point Cloud Registration During Space Rendezvous
Proceedings of the 18th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications -- Volume 5: VISAPP,, page 919--930. INSTICC, SciTePress, (2023)
DOI: 10.5220/0011616300003417
Abstract
Next to the iterative closest point (ICP) algorithm,
the normal distribution transform (NDT) algorithm is
be- coming a second standard for 3D point cloud
registration in mobile robotics. Both methods are
effective, however they require a sufficiently good
initialization to successfully converge. In
particular, the discontinuities in the NDT cost
function can lead to difficulties when performing
the optimization. In addition, when the size of the
point clouds increases, performing the registration
in real-time becomes challenging. This work in-
troduces a Gaussian smoothing technique of the NDT
map, which can be done prior to the registration
process. A kd-tree adaptation of the typical octree
representation of NDT maps is also proposed. The
performance of the modified smoothed NDT (S-NDT)
algorithm for pairwise scan registration is assessed
on two large-scale outdoor datasets, and compared to
the performance of a state-of-the-art ICP
implementation. S-NDT is around four times faster
and as robust as ICP while reaching similar
precision. The algorithm is thereafter applied to
the problem of LiDAR tracking of a spacecraft in
close-range in the context of space rendezvous,
demonstrating the performance and applicability to
real-time applications.
