View in EDS

Space debris pose and motion identification using 4D LiDAR.

Saved in:
Bibliographic Details
Title: Space debris pose and motion identification using 4D LiDAR.
Authors: Li, Jun Yang1 (AUTHOR) junyang.li@mail.utoronto.ca, Wolfe, Sean1 (AUTHOR) sean.wolfe@mail.utoronto.ca, Emami, M. Reza1 (AUTHOR) reza.emami@utoronto.ca
Source: Advances in Space Research. Aug2026, Vol. 78 Issue 4, p3807-3829. 23p.
Subjects: Space debris, Pose estimation (Computer vision), Monte Carlo method, Optical sensors, Orbital rendezvous (Space flight), LIDAR, Kalman filtering, Motion analysis
Abstract: • Complete pose and motion estimation for non-cooperative space debris is proposed. • A consensus-based algorithm enables a single optical sensor to determine debris states. • A commercial off-the-shelf 4D LiDAR is verified for close-range rendezvous. • Monte Carlo simulations demonstrate robustness and computational efficiency. Determining the pose and motion of an uncooperative object, such as space debris, during close-range rendezvous requires a real-time, comprehensive perception suite. However, resource constraints, especially on miniaturized platforms, may prohibit the use of conventional optical payloads with computationally intensive algorithms. In this paper, a framework for the application of a 4D LiDAR, providing both range and velocity measurements, is presented. The proposed pose and motion estimation framework consists of a concord-based iterated sigmapoint Kalman filter. Two distinct oriented bounding boxes are constructed, with their principal axes aligned with vectors obtained from the eigenvectors using principal component analysis and plane identification by random sample consensus, respectively. While individually, neither bounding box can accurately match the ground truth pose of the debris, the agreement between the two bounding box orientations as an input measurement to the sigmapoint Kalman filter allows the filter to reject poor measurements and perform orientation estimation. Through Monte Carlo simulations, it is shown that the filter is capable of leveraging both the speed and range measurements of the 4D LiDAR, in order to reliably and accurately determine the full pose and motion of a space debris in real-time. [ABSTRACT FROM AUTHOR]
Copyright of Advances in Space Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
FullText Text:
  Availability: 0
Header DbId: egs
DbLabel: Engineering Source
An: 195550901
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Space debris pose and motion identification using 4D LiDAR.
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Li%2C+Jun+Yang%22">Li, Jun Yang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> junyang.li@mail.utoronto.ca</i><br /><searchLink fieldCode="AR" term="%22Wolfe%2C+Sean%22">Wolfe, Sean</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> sean.wolfe@mail.utoronto.ca</i><br /><searchLink fieldCode="AR" term="%22Emami%2C+M%2E+Reza%22">Emami, M. Reza</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> reza.emami@utoronto.ca</i>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="JN" term="%22Advances+in+Space+Research%22">Advances in Space Research</searchLink>. Aug2026, Vol. 78 Issue 4, p3807-3829. 23p.
– Name: Subject
  Label: Subjects
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Space+debris%22">Space debris</searchLink><br /><searchLink fieldCode="DE" term="%22Pose+estimation+%28Computer+vision%29%22">Pose estimation (Computer vision)</searchLink><br /><searchLink fieldCode="DE" term="%22Monte+Carlo+method%22">Monte Carlo method</searchLink><br /><searchLink fieldCode="DE" term="%22Optical+sensors%22">Optical sensors</searchLink><br /><searchLink fieldCode="DE" term="%22Orbital+rendezvous+%28Space+flight%29%22">Orbital rendezvous (Space flight)</searchLink><br /><searchLink fieldCode="DE" term="%22LIDAR%22">LIDAR</searchLink><br /><searchLink fieldCode="DE" term="%22Kalman+filtering%22">Kalman filtering</searchLink><br /><searchLink fieldCode="DE" term="%22Motion+analysis%22">Motion analysis</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: • Complete pose and motion estimation for non-cooperative space debris is proposed. • A consensus-based algorithm enables a single optical sensor to determine debris states. • A commercial off-the-shelf 4D LiDAR is verified for close-range rendezvous. • Monte Carlo simulations demonstrate robustness and computational efficiency. Determining the pose and motion of an uncooperative object, such as space debris, during close-range rendezvous requires a real-time, comprehensive perception suite. However, resource constraints, especially on miniaturized platforms, may prohibit the use of conventional optical payloads with computationally intensive algorithms. In this paper, a framework for the application of a 4D LiDAR, providing both range and velocity measurements, is presented. The proposed pose and motion estimation framework consists of a concord-based iterated sigmapoint Kalman filter. Two distinct oriented bounding boxes are constructed, with their principal axes aligned with vectors obtained from the eigenvectors using principal component analysis and plane identification by random sample consensus, respectively. While individually, neither bounding box can accurately match the ground truth pose of the debris, the agreement between the two bounding box orientations as an input measurement to the sigmapoint Kalman filter allows the filter to reject poor measurements and perform orientation estimation. Through Monte Carlo simulations, it is shown that the filter is capable of leveraging both the speed and range measurements of the 4D LiDAR, in order to reliably and accurately determine the full pose and motion of a space debris in real-time. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Advances in Space Research is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=195550901
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1016/j.asr.2026.06.010
    Languages:
      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 23
        StartPage: 3807
    Subjects:
      – SubjectFull: Space debris
        Type: general
      – SubjectFull: Pose estimation (Computer vision)
        Type: general
      – SubjectFull: Monte Carlo method
        Type: general
      – SubjectFull: Optical sensors
        Type: general
      – SubjectFull: Orbital rendezvous (Space flight)
        Type: general
      – SubjectFull: LIDAR
        Type: general
      – SubjectFull: Kalman filtering
        Type: general
      – SubjectFull: Motion analysis
        Type: general
    Titles:
      – TitleFull: Space debris pose and motion identification using 4D LiDAR.
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Li, Jun Yang
      – PersonEntity:
          Name:
            NameFull: Wolfe, Sean
      – PersonEntity:
          Name:
            NameFull: Emami, M. Reza
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 15
              M: 08
              Text: Aug2026
              Type: published
              Y: 2026
          Identifiers:
            – Type: issn-print
              Value: 02731177
          Numbering:
            – Type: volume
              Value: 78
            – Type: issue
              Value: 4
          Titles:
            – TitleFull: Advances in Space Research
              Type: main
ResultId 1