Probabilistic combination of noisy points and planes for RGB-D Odometry

Pedro F. Proença; Yang Gao

doi:10.1007/978-3-319-64107-2_27

Probabilistic combination of noisy points and planes for RGB-D Odometry

Pedro F. Proença^*
, Yang Gao

^*Corresponding author for this work

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

7 Citations (Scopus)

Abstract

This work proposes a visual odometry method that combines points and plane primitives, extracted from a noisy depth camera. Depth measurement uncertainty is modelled and propagated through the extraction of geometric primitives to the frame-to-frame motion estimation, where pose is optimized by weighting the residuals of 3D point and planes matches, according to their uncertainties. Results on an RGB-D dataset show that the combination of points and planes, through the proposed method, is able to perform well in poorly textured environments, where point-based odometry is bound to fail.

Original language	English
Title of host publication	Towards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings
Editors	Yang Gao, Saber Fallah, Yaochu Jin, Constantina Lekakou
Publisher	Springer Verlag
Pages	340-350
Number of pages	11
ISBN (Electronic)	9783319641072
ISBN (Print)	9783319641065
DOIs	https://doi.org/10.1007/978-3-319-64107-2_27
Publication status	Published - 20 Jul 2017
Externally published	Yes
Event	18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017 - Guildford, United Kingdom Duration: 19 Jul 2017 → 21 Jul 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10454 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017
Country/Territory	United Kingdom
City	Guildford
Period	19/07/17 → 21/07/17

Bibliographical note

Publisher Copyright:
© Springer International Publishing AG 2017.

Keywords

Visual odometry
Depth cameras
Uncertainty propagation
Probabilistic plane fitting

Access to Document

10.1007/978-3-319-64107-2_27

Cite this

Proença, P. F., & Gao, Y. (2017). Probabilistic combination of noisy points and planes for RGB-D Odometry. In Y. Gao, S. Fallah, Y. Jin, & C. Lekakou (Eds.), Towards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings (pp. 340-350). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10454 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-64107-2_27

Proença, Pedro F. ; Gao, Yang. / Probabilistic combination of noisy points and planes for RGB-D Odometry. Towards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings. editor / Yang Gao ; Saber Fallah ; Yaochu Jin ; Constantina Lekakou. Springer Verlag, 2017. pp. 340-350 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "This work proposes a visual odometry method that combines points and plane primitives, extracted from a noisy depth camera. Depth measurement uncertainty is modelled and propagated through the extraction of geometric primitives to the frame-to-frame motion estimation, where pose is optimized by weighting the residuals of 3D point and planes matches, according to their uncertainties. Results on an RGB-D dataset show that the combination of points and planes, through the proposed method, is able to perform well in poorly textured environments, where point-based odometry is bound to fail.",

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Proença, PF & Gao, Y 2017, Probabilistic combination of noisy points and planes for RGB-D Odometry. in Y Gao, S Fallah, Y Jin & C Lekakou (eds), Towards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10454 LNAI, Springer Verlag, pp. 340-350, 18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017, Guildford, United Kingdom, 19/07/17. https://doi.org/10.1007/978-3-319-64107-2_27

Probabilistic combination of noisy points and planes for RGB-D Odometry. / Proença, Pedro F.; Gao, Yang.
Towards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings. ed. / Yang Gao; Saber Fallah; Yaochu Jin; Constantina Lekakou. Springer Verlag, 2017. p. 340-350 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10454 LNAI).

Research output: Chapter in Book/Conference Proceeding/Report › Conference Paper published in a book › peer-review

TY - GEN

T1 - Probabilistic combination of noisy points and planes for RGB-D Odometry

AU - Proença, Pedro F.

AU - Gao, Yang

N1 - Publisher Copyright: © Springer International Publishing AG 2017.

PY - 2017/7/20

Y1 - 2017/7/20

N2 - This work proposes a visual odometry method that combines points and plane primitives, extracted from a noisy depth camera. Depth measurement uncertainty is modelled and propagated through the extraction of geometric primitives to the frame-to-frame motion estimation, where pose is optimized by weighting the residuals of 3D point and planes matches, according to their uncertainties. Results on an RGB-D dataset show that the combination of points and planes, through the proposed method, is able to perform well in poorly textured environments, where point-based odometry is bound to fail.

AB - This work proposes a visual odometry method that combines points and plane primitives, extracted from a noisy depth camera. Depth measurement uncertainty is modelled and propagated through the extraction of geometric primitives to the frame-to-frame motion estimation, where pose is optimized by weighting the residuals of 3D point and planes matches, according to their uncertainties. Results on an RGB-D dataset show that the combination of points and planes, through the proposed method, is able to perform well in poorly textured environments, where point-based odometry is bound to fail.

KW - Visual odometry

KW - Depth cameras

KW - Uncertainty propagation

KW - Probabilistic plane fitting

UR - https://www.webofscience.com/wos/woscc/full-record/WOS:000453217100027

UR - https://openalex.org/W2614149807

U2 - 10.1007/978-3-319-64107-2_27

DO - 10.1007/978-3-319-64107-2_27

M3 - Conference Paper published in a book

SN - 9783319641065

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 340

EP - 350

BT - Towards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings

A2 - Gao, Yang

A2 - Fallah, Saber

A2 - Jin, Yaochu

A2 - Lekakou, Constantina

PB - Springer Verlag

T2 - 18th Annual Conference on Towards Autonomous Robotic Systems, TAROS 2017

Y2 - 19 July 2017 through 21 July 2017

ER -

Proença PF, Gao Y. Probabilistic combination of noisy points and planes for RGB-D Odometry. In Gao Y, Fallah S, Jin Y, Lekakou C, editors, Towards Autonomous Robotic Systems - 18th Annual Conference, TAROS 2017, Proceedings. Springer Verlag. 2017. p. 340-350. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-64107-2_27

Probabilistic combination of noisy points and planes for RGB-D Odometry

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Bibliographical note

Keywords

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