Lidar sensors enable precise pose estimation of an
uncooperative spacecraft in close range. In this
context, the iterative closest point (ICP) is
usually employed as a tracking method. However, when
the size of the point clouds increases, the required
computation time of the ICP can become a limiting
factor. The normal distribution transform (NDT) is
an alternative algorithm which can be more efficient
than the ICP, but suffers from robustness issues. In
addition, lidar sensors are also subject to motion
blur effects when tracking a spacecraft tumbling
with a high angular velocity, leading to a loss of
precision in the relative pose estimation. This work
introduces a smoothed formulation of the NDT to
improve the algorithm’s robustness while
maintaining its efficiency. Additionally, two
strategies are investigated to mitigate the effects
of motion blur. The first consists in un-distorting
the point cloud, while the second is a
continuous-time formulation of the
NDT. Hardware-in-the-loop tests at the European
Proximity Operations Simulator demonstrate the
capability of the proposed methods to precisely
track an uncooperative spacecraft under realistic
conditions within tens of milliseconds, even when
the spacecraft tumbles with a significant angular
rate.
%0 Journal Article
%1 REMSEN2023
%A Renaut, L.
%A Frei, H.
%A Nüchter, A.
%D 2023
%J Remote Sensing
%K author myown
%N 9
%R 10.3390/rs15092286
%T Lidar Pose Tracking of a Tumbling Spacecraft Using the Smoothed Normal Distribution Transform
%U https://robotik.informatik.uni-wuerzburg.de/telematics/download/remotesensing2023.pdf
%V 15
%X Lidar sensors enable precise pose estimation of an
uncooperative spacecraft in close range. In this
context, the iterative closest point (ICP) is
usually employed as a tracking method. However, when
the size of the point clouds increases, the required
computation time of the ICP can become a limiting
factor. The normal distribution transform (NDT) is
an alternative algorithm which can be more efficient
than the ICP, but suffers from robustness issues. In
addition, lidar sensors are also subject to motion
blur effects when tracking a spacecraft tumbling
with a high angular velocity, leading to a loss of
precision in the relative pose estimation. This work
introduces a smoothed formulation of the NDT to
improve the algorithm’s robustness while
maintaining its efficiency. Additionally, two
strategies are investigated to mitigate the effects
of motion blur. The first consists in un-distorting
the point cloud, while the second is a
continuous-time formulation of the
NDT. Hardware-in-the-loop tests at the European
Proximity Operations Simulator demonstrate the
capability of the proposed methods to precisely
track an uncooperative spacecraft under realistic
conditions within tens of milliseconds, even when
the spacecraft tumbles with a significant angular
rate.
@article{REMSEN2023,
abstract = {Lidar sensors enable precise pose estimation of an
uncooperative spacecraft in close range. In this
context, the iterative closest point (ICP) is
usually employed as a tracking method. However, when
the size of the point clouds increases, the required
computation time of the ICP can become a limiting
factor. The normal distribution transform (NDT) is
an alternative algorithm which can be more efficient
than the ICP, but suffers from robustness issues. In
addition, lidar sensors are also subject to motion
blur effects when tracking a spacecraft tumbling
with a high angular velocity, leading to a loss of
precision in the relative pose estimation. This work
introduces a smoothed formulation of the NDT to
improve the algorithm’s robustness while
maintaining its efficiency. Additionally, two
strategies are investigated to mitigate the effects
of motion blur. The first consists in un-distorting
the point cloud, while the second is a
continuous-time formulation of the
NDT. Hardware-in-the-loop tests at the European
Proximity Operations Simulator demonstrate the
capability of the proposed methods to precisely
track an uncooperative spacecraft under realistic
conditions within tens of milliseconds, even when
the spacecraft tumbles with a significant angular
rate.},
added-at = {2023-04-26T16:32:16.000+0200},
article-number = {2286},
author = {Renaut, L. and Frei, H. and N{\"u}chter, A.},
biburl = {https://www.bibsonomy.org/bibtex/2ea7eeef971e872ea97a0cbca8f6ed9e6/nuechter76},
doi = {10.3390/rs15092286},
interhash = {f36c7906b63050031b5c99335aceef32},
intrahash = {ea7eeef971e872ea97a0cbca8f6ed9e6},
issn = {2072-4292},
journal = {Remote Sensing},
keywords = {author myown},
number = 9,
timestamp = {2024-07-30T17:19:57.000+0200},
title = {Lidar Pose Tracking of a Tumbling Spacecraft Using the Smoothed Normal Distribution Transform},
url = {https://robotik.informatik.uni-wuerzburg.de/telematics/download/remotesensing2023.pdf},
volume = 15,
year = 2023
}