Accurate keyframe selection and keypoint tracking for robust visual odometry.
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| Title: | Accurate keyframe selection and keypoint tracking for robust visual odometry. |
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| Authors: | Fanfani, Marco1 marco.fanfani@unifi.it, Bellavia, Fabio1, Colombo, Carlo1 |
| Source: | Machine Vision & Applications. Aug2016, Vol. 27 Issue 6, p833-844. 12p. |
| Subjects: | Visual odometry, Mathematical models of human behavior, Human-computer interaction, Patient monitoring, Computer vision |
| Abstract: | This paper presents a novel stereo visual odometry (VO) framework based on structure from motion, where a robust keypoint tracking and matching is combined with an effective keyframe selection strategy. In order to track and find correct feature correspondences a robust loop chain matching scheme on two consecutive stereo pairs is introduced. Keyframe selection is based on the proportion of features with high temporal disparity. This criterion relies on the observation that the error in the pose estimation propagates from the uncertainty of 3D points-higher for distant points, that have low 2D motion. Comparative results based on three VO datasets show that the proposed solution is remarkably effective and robust even for very long path lengths. [ABSTRACT FROM AUTHOR] |
| Copyright of Machine Vision & Applications is the property of Springer Nature 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 | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 117171350 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Accurate keyframe selection and keypoint tracking for robust visual odometry. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Fanfani%2C+Marco%22">Fanfani, Marco</searchLink><relatesTo>1</relatesTo><i> marco.fanfani@unifi.it</i><br /><searchLink fieldCode="AR" term="%22Bellavia%2C+Fabio%22">Bellavia, Fabio</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Colombo%2C+Carlo%22">Colombo, Carlo</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Machine+Vision+%26+Applications%22">Machine Vision & Applications</searchLink>. Aug2016, Vol. 27 Issue 6, p833-844. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Visual+odometry%22">Visual odometry</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models+of+human+behavior%22">Mathematical models of human behavior</searchLink><br /><searchLink fieldCode="DE" term="%22Human-computer+interaction%22">Human-computer interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Patient+monitoring%22">Patient monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+vision%22">Computer vision</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: This paper presents a novel stereo visual odometry (VO) framework based on structure from motion, where a robust keypoint tracking and matching is combined with an effective keyframe selection strategy. In order to track and find correct feature correspondences a robust loop chain matching scheme on two consecutive stereo pairs is introduced. Keyframe selection is based on the proportion of features with high temporal disparity. This criterion relies on the observation that the error in the pose estimation propagates from the uncertainty of 3D points-higher for distant points, that have low 2D motion. Comparative results based on three VO datasets show that the proposed solution is remarkably effective and robust even for very long path lengths. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Machine Vision & Applications is the property of Springer Nature 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.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00138-016-0793-3 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 833 Subjects: – SubjectFull: Visual odometry Type: general – SubjectFull: Mathematical models of human behavior Type: general – SubjectFull: Human-computer interaction Type: general – SubjectFull: Patient monitoring Type: general – SubjectFull: Computer vision Type: general Titles: – TitleFull: Accurate keyframe selection and keypoint tracking for robust visual odometry. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Fanfani, Marco – PersonEntity: Name: NameFull: Bellavia, Fabio – PersonEntity: Name: NameFull: Colombo, Carlo IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 08 Text: Aug2016 Type: published Y: 2016 Identifiers: – Type: issn-print Value: 09328092 Numbering: – Type: volume Value: 27 – Type: issue Value: 6 Titles: – TitleFull: Machine Vision & Applications Type: main |
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