Multi-cue 3D model-based object tracking

Geoffrey Taylor, Lindsay Kleeman

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

Abstract

Once an object has been located and classified using the techniques in the previous chapters, the system must continue to update the estimated pose for several reasons. Clearly, the initial pose will quickly become invalid if the object is under internal or external dynamic influences. However, even if the object is static, the motion of the active cameras may dynamically bias the estimated pose through modelling errors. Even a small pose bias is sufficient to destabilize a planned grasp and cause the manipulation to fail. Tracking is therefore an important component in a robust grasping and manipulation framework. If the range sensing and segmentation methods described in Chapters 3 and 4 could be performed with sufficient speed, the tracking could be implemented by continuously repeating this process. Unfortunately, the current measurement rate (up to one minute per range scan) renders this approach unsuitable for real-time tracking. However, the textured polygonal models and initial pose information from range data segmentation present an ideal basis for 3D modelbased tracking. To close the visual feedback loop, this chapter now addresses the problem of continuously updating the pose of modelled objects.

Original languageEnglish
Title of host publicationRobotic Manipulation
Subtitle of host publication3D Object Recognition, Tracking and Hand-Eye Coordination
Pages85-113
Number of pages29
DOIs
Publication statusPublished - 27 Sep 2006

Publication series

NameSpringer Tracts in Advanced Robotics
Volume26
ISSN (Print)1610-7438
ISSN (Electronic)1610-742X

Cite this

Taylor, G., & Kleeman, L. (2006). Multi-cue 3D model-based object tracking. In Robotic Manipulation: 3D Object Recognition, Tracking and Hand-Eye Coordination (pp. 85-113). (Springer Tracts in Advanced Robotics; Vol. 26). https://doi.org/10.1007/11540151_5
Taylor, Geoffrey ; Kleeman, Lindsay. / Multi-cue 3D model-based object tracking. Robotic Manipulation: 3D Object Recognition, Tracking and Hand-Eye Coordination. 2006. pp. 85-113 (Springer Tracts in Advanced Robotics).
@inbook{dbee7310e674466384377f9dad7f2286,
title = "Multi-cue 3D model-based object tracking",
abstract = "Once an object has been located and classified using the techniques in the previous chapters, the system must continue to update the estimated pose for several reasons. Clearly, the initial pose will quickly become invalid if the object is under internal or external dynamic influences. However, even if the object is static, the motion of the active cameras may dynamically bias the estimated pose through modelling errors. Even a small pose bias is sufficient to destabilize a planned grasp and cause the manipulation to fail. Tracking is therefore an important component in a robust grasping and manipulation framework. If the range sensing and segmentation methods described in Chapters 3 and 4 could be performed with sufficient speed, the tracking could be implemented by continuously repeating this process. Unfortunately, the current measurement rate (up to one minute per range scan) renders this approach unsuitable for real-time tracking. However, the textured polygonal models and initial pose information from range data segmentation present an ideal basis for 3D modelbased tracking. To close the visual feedback loop, this chapter now addresses the problem of continuously updating the pose of modelled objects.",
author = "Geoffrey Taylor and Lindsay Kleeman",
year = "2006",
month = "9",
day = "27",
doi = "10.1007/11540151_5",
language = "English",
isbn = "3540334548",
series = "Springer Tracts in Advanced Robotics",
pages = "85--113",
booktitle = "Robotic Manipulation",

}

Taylor, G & Kleeman, L 2006, Multi-cue 3D model-based object tracking. in Robotic Manipulation: 3D Object Recognition, Tracking and Hand-Eye Coordination. Springer Tracts in Advanced Robotics, vol. 26, pp. 85-113. https://doi.org/10.1007/11540151_5

Multi-cue 3D model-based object tracking. / Taylor, Geoffrey; Kleeman, Lindsay.

Robotic Manipulation: 3D Object Recognition, Tracking and Hand-Eye Coordination. 2006. p. 85-113 (Springer Tracts in Advanced Robotics; Vol. 26).

Research output: Chapter in Book/Report/Conference proceedingChapter (Book)Researchpeer-review

TY - CHAP

T1 - Multi-cue 3D model-based object tracking

AU - Taylor, Geoffrey

AU - Kleeman, Lindsay

PY - 2006/9/27

Y1 - 2006/9/27

N2 - Once an object has been located and classified using the techniques in the previous chapters, the system must continue to update the estimated pose for several reasons. Clearly, the initial pose will quickly become invalid if the object is under internal or external dynamic influences. However, even if the object is static, the motion of the active cameras may dynamically bias the estimated pose through modelling errors. Even a small pose bias is sufficient to destabilize a planned grasp and cause the manipulation to fail. Tracking is therefore an important component in a robust grasping and manipulation framework. If the range sensing and segmentation methods described in Chapters 3 and 4 could be performed with sufficient speed, the tracking could be implemented by continuously repeating this process. Unfortunately, the current measurement rate (up to one minute per range scan) renders this approach unsuitable for real-time tracking. However, the textured polygonal models and initial pose information from range data segmentation present an ideal basis for 3D modelbased tracking. To close the visual feedback loop, this chapter now addresses the problem of continuously updating the pose of modelled objects.

AB - Once an object has been located and classified using the techniques in the previous chapters, the system must continue to update the estimated pose for several reasons. Clearly, the initial pose will quickly become invalid if the object is under internal or external dynamic influences. However, even if the object is static, the motion of the active cameras may dynamically bias the estimated pose through modelling errors. Even a small pose bias is sufficient to destabilize a planned grasp and cause the manipulation to fail. Tracking is therefore an important component in a robust grasping and manipulation framework. If the range sensing and segmentation methods described in Chapters 3 and 4 could be performed with sufficient speed, the tracking could be implemented by continuously repeating this process. Unfortunately, the current measurement rate (up to one minute per range scan) renders this approach unsuitable for real-time tracking. However, the textured polygonal models and initial pose information from range data segmentation present an ideal basis for 3D modelbased tracking. To close the visual feedback loop, this chapter now addresses the problem of continuously updating the pose of modelled objects.

UR - http://www.scopus.com/inward/record.url?scp=33748904071&partnerID=8YFLogxK

U2 - 10.1007/11540151_5

DO - 10.1007/11540151_5

M3 - Chapter (Book)

SN - 3540334548

SN - 9783540334545

T3 - Springer Tracts in Advanced Robotics

SP - 85

EP - 113

BT - Robotic Manipulation

ER -

Taylor G, Kleeman L. Multi-cue 3D model-based object tracking. In Robotic Manipulation: 3D Object Recognition, Tracking and Hand-Eye Coordination. 2006. p. 85-113. (Springer Tracts in Advanced Robotics). https://doi.org/10.1007/11540151_5