An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation
Abstract Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-co...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Senthooran, Ilankaikone [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2018 |
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| Schlagwörter: |
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| Anmerkung: |
© Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| Übergeordnetes Werk: |
Enthalten in: Autonomous robots - Springer US, 1994, 43(2018), 5 vom: 20. Aug., Seite 1257-1270 |
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| Übergeordnetes Werk: |
volume:43 ; year:2018 ; number:5 ; day:20 ; month:08 ; pages:1257-1270 |
| Links: |
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| DOI / URN: |
10.1007/s10514-018-9801-y |
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OLC2052752994 |
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| 520 | |a Abstract Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times. | ||
| 650 | 4 | |a Pose estimation | |
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| 700 | 1 | |a Chung, Hoam |4 aut | |
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10.1007/s10514-018-9801-y doi (DE-627)OLC2052752994 (DE-He213)s10514-018-9801-y-p DE-627 ger DE-627 rakwb eng 620 VZ Senthooran, Ilankaikone verfasserin (orcid)0000-0001-6207-3780 aut An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times. Pose estimation Visual odometry RANSAC RGB-D MAV Limited processing Murshed, Manzur aut Barca, Jan Carlo aut Kamruzzaman, Joarder aut Chung, Hoam aut Enthalten in Autonomous robots Springer US, 1994 43(2018), 5 vom: 20. Aug., Seite 1257-1270 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:43 year:2018 number:5 day:20 month:08 pages:1257-1270 https://doi.org/10.1007/s10514-018-9801-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 AR 43 2018 5 20 08 1257-1270 |
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10.1007/s10514-018-9801-y doi (DE-627)OLC2052752994 (DE-He213)s10514-018-9801-y-p DE-627 ger DE-627 rakwb eng 620 VZ Senthooran, Ilankaikone verfasserin (orcid)0000-0001-6207-3780 aut An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times. Pose estimation Visual odometry RANSAC RGB-D MAV Limited processing Murshed, Manzur aut Barca, Jan Carlo aut Kamruzzaman, Joarder aut Chung, Hoam aut Enthalten in Autonomous robots Springer US, 1994 43(2018), 5 vom: 20. Aug., Seite 1257-1270 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:43 year:2018 number:5 day:20 month:08 pages:1257-1270 https://doi.org/10.1007/s10514-018-9801-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 AR 43 2018 5 20 08 1257-1270 |
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10.1007/s10514-018-9801-y doi (DE-627)OLC2052752994 (DE-He213)s10514-018-9801-y-p DE-627 ger DE-627 rakwb eng 620 VZ Senthooran, Ilankaikone verfasserin (orcid)0000-0001-6207-3780 aut An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times. Pose estimation Visual odometry RANSAC RGB-D MAV Limited processing Murshed, Manzur aut Barca, Jan Carlo aut Kamruzzaman, Joarder aut Chung, Hoam aut Enthalten in Autonomous robots Springer US, 1994 43(2018), 5 vom: 20. Aug., Seite 1257-1270 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:43 year:2018 number:5 day:20 month:08 pages:1257-1270 https://doi.org/10.1007/s10514-018-9801-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 AR 43 2018 5 20 08 1257-1270 |
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10.1007/s10514-018-9801-y doi (DE-627)OLC2052752994 (DE-He213)s10514-018-9801-y-p DE-627 ger DE-627 rakwb eng 620 VZ Senthooran, Ilankaikone verfasserin (orcid)0000-0001-6207-3780 aut An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times. Pose estimation Visual odometry RANSAC RGB-D MAV Limited processing Murshed, Manzur aut Barca, Jan Carlo aut Kamruzzaman, Joarder aut Chung, Hoam aut Enthalten in Autonomous robots Springer US, 1994 43(2018), 5 vom: 20. Aug., Seite 1257-1270 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:43 year:2018 number:5 day:20 month:08 pages:1257-1270 https://doi.org/10.1007/s10514-018-9801-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 AR 43 2018 5 20 08 1257-1270 |
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10.1007/s10514-018-9801-y doi (DE-627)OLC2052752994 (DE-He213)s10514-018-9801-y-p DE-627 ger DE-627 rakwb eng 620 VZ Senthooran, Ilankaikone verfasserin (orcid)0000-0001-6207-3780 aut An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation 2018 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer Science+Business Media, LLC, part of Springer Nature 2018 Abstract Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times. Pose estimation Visual odometry RANSAC RGB-D MAV Limited processing Murshed, Manzur aut Barca, Jan Carlo aut Kamruzzaman, Joarder aut Chung, Hoam aut Enthalten in Autonomous robots Springer US, 1994 43(2018), 5 vom: 20. Aug., Seite 1257-1270 (DE-627)186689446 (DE-600)1252189-9 (DE-576)053002199 0929-5593 nnns volume:43 year:2018 number:5 day:20 month:08 pages:1257-1270 https://doi.org/10.1007/s10514-018-9801-y lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC GBV_ILN_70 AR 43 2018 5 20 08 1257-1270 |
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An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation |
| abstract |
Abstract Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstractGer |
Abstract Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
| abstract_unstemmed |
Abstract Achieving autonomous flight in GPS-denied environments begins with pose estimation in three-dimensional space, and this is much more challenging in an MAV in a swarm robotic system due to limited computational resources. In vision-based pose estimation, outlier detection is the most time-consuming step. This usually involves a RANSAC procedure using the reprojection-error method for hypothesis evaluation. Realignment-based hypothesis evaluation method is observed to be more accurate, but the considerably slower speed makes it unsuitable for robots with limited resources. We use sufficient statistics of least-squares minimisation to speed up this process. The additive nature of these sufficient statistics makes it possible to compute pose estimates in each evaluation by reusing previously computed statistics. Thus estimates need not be calculated from scratch each time. The proposed method is tested on standard RANSAC, Preemptive RANSAC and R-RANSAC using benchmark datasets. The results show that the use of sufficient statistics speeds up the outlier detection process with realignment hypothesis evaluation for all RANSAC variants, achieving an execution speed of up to 6.72 times. © Springer Science+Business Media, LLC, part of Springer Nature 2018 |
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| title_short |
An efficient RANSAC hypothesis evaluation using sufficient statistics for RGB-D pose estimation |
| url |
https://doi.org/10.1007/s10514-018-9801-y |
| remote_bool |
false |
| author2 |
Murshed, Manzur Barca, Jan Carlo Kamruzzaman, Joarder Chung, Hoam |
| author2Str |
Murshed, Manzur Barca, Jan Carlo Kamruzzaman, Joarder Chung, Hoam |
| ppnlink |
186689446 |
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false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s10514-018-9801-y |
| up_date |
2024-07-03T16:24:23.366Z |
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