O-POCO: Online point cloud compression mapping for visual odometry and SLAM

Luis Angel Contreras Toledo; Walterio Mayol-Cuevas

doi:10.1109/ICRA.2017.7989523

O-POCO: Online point cloud compression mapping for visual odometry and SLAM

Luis Angel Contreras Toledo, Walterio Mayol-Cuevas

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

12 Citations (Scopus)

706 Downloads (Pure)

Abstract

This paper presents O-POCO, a visual odometry and SLAM system that makes online decisions regarding what to map and what to ignore. It takes a point cloud from classical SfM and aims to sample it on-line by selecting map features useful for future 6D relocalisation. We use the camera's traveled trajectory to compartamentalize the point cloud, along with visual and spatial information to sample and compress the map. We propose and evaluate a number of different information layers such as the descriptor information's relative entropy, map-feature occupancy grid, and the point cloud's geometry error. We compare our proposed system against both SfM, and online and offline ORB-SLAM using publicly available datasets in addition to our own. Results show that our online compression strategy is capable of outperforming the baseline even for conditions when the number of features per key-frame used for mapping is four times less.

Original language	English
Title of host publication	ICRA 2017 - IEEE International Conference on Robotics and Automation
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	4509-4514
Number of pages	6
ISBN (Electronic)	9781509046331
ISBN (Print)	9781509046348
DOIs	https://doi.org/10.1109/ICRA.2017.7989523
Publication status	Published - 24 Jul 2017
Event	2017 IEEE International Conference on Robotics and Automation, ICRA 2017 - Singapore, Singapore Duration: 29 May 2017 → 3 Jun 2017

Conference

Conference	2017 IEEE International Conference on Robotics and Automation, ICRA 2017
Country/Territory	Singapore
City	Singapore
Period	29/05/17 → 3/06/17

Access to Document

10.1109/ICRA.2017.7989523

Full-text PDF (accepted author manuscript)
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at http://ieeexplore.ieee.org/document/7989523/. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 5.98 MB

Handle.net

Persistent link

Cite this

@inproceedings{36c328b97db64a2ca8048762b3496601,

title = "O-POCO: Online point cloud compression mapping for visual odometry and SLAM",

abstract = "This paper presents O-POCO, a visual odometry and SLAM system that makes online decisions regarding what to map and what to ignore. It takes a point cloud from classical SfM and aims to sample it on-line by selecting map features useful for future 6D relocalisation. We use the camera's traveled trajectory to compartamentalize the point cloud, along with visual and spatial information to sample and compress the map. We propose and evaluate a number of different information layers such as the descriptor information's relative entropy, map-feature occupancy grid, and the point cloud's geometry error. We compare our proposed system against both SfM, and online and offline ORB-SLAM using publicly available datasets in addition to our own. Results show that our online compression strategy is capable of outperforming the baseline even for conditions when the number of features per key-frame used for mapping is four times less.",

author = "{Contreras Toledo}, {Luis Angel} and Walterio Mayol-Cuevas",

year = "2017",

month = jul,

day = "24",

doi = "10.1109/ICRA.2017.7989523",

language = "English",

isbn = "9781509046348",

pages = "4509--4514",

booktitle = "ICRA 2017 - IEEE International Conference on Robotics and Automation",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2017 IEEE International Conference on Robotics and Automation, ICRA 2017 ; Conference date: 29-05-2017 Through 03-06-2017",

}

Contreras Toledo, LA & Mayol-Cuevas, W 2017, O-POCO: Online point cloud compression mapping for visual odometry and SLAM. in ICRA 2017 - IEEE International Conference on Robotics and Automation., 7989523, Institute of Electrical and Electronics Engineers (IEEE), pp. 4509-4514, 2017 IEEE International Conference on Robotics and Automation, ICRA 2017, Singapore, Singapore, 29/05/17. https://doi.org/10.1109/ICRA.2017.7989523

O-POCO: Online point cloud compression mapping for visual odometry and SLAM. / Contreras Toledo, Luis Angel; Mayol-Cuevas, Walterio.
ICRA 2017 - IEEE International Conference on Robotics and Automation. Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 4509-4514 7989523.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

TY - GEN

T1 - O-POCO

T2 - 2017 IEEE International Conference on Robotics and Automation, ICRA 2017

AU - Contreras Toledo, Luis Angel

AU - Mayol-Cuevas, Walterio

PY - 2017/7/24

Y1 - 2017/7/24

N2 - This paper presents O-POCO, a visual odometry and SLAM system that makes online decisions regarding what to map and what to ignore. It takes a point cloud from classical SfM and aims to sample it on-line by selecting map features useful for future 6D relocalisation. We use the camera's traveled trajectory to compartamentalize the point cloud, along with visual and spatial information to sample and compress the map. We propose and evaluate a number of different information layers such as the descriptor information's relative entropy, map-feature occupancy grid, and the point cloud's geometry error. We compare our proposed system against both SfM, and online and offline ORB-SLAM using publicly available datasets in addition to our own. Results show that our online compression strategy is capable of outperforming the baseline even for conditions when the number of features per key-frame used for mapping is four times less.

AB - This paper presents O-POCO, a visual odometry and SLAM system that makes online decisions regarding what to map and what to ignore. It takes a point cloud from classical SfM and aims to sample it on-line by selecting map features useful for future 6D relocalisation. We use the camera's traveled trajectory to compartamentalize the point cloud, along with visual and spatial information to sample and compress the map. We propose and evaluate a number of different information layers such as the descriptor information's relative entropy, map-feature occupancy grid, and the point cloud's geometry error. We compare our proposed system against both SfM, and online and offline ORB-SLAM using publicly available datasets in addition to our own. Results show that our online compression strategy is capable of outperforming the baseline even for conditions when the number of features per key-frame used for mapping is four times less.

UR - http://www.scopus.com/inward/record.url?scp=85028006759&partnerID=8YFLogxK

U2 - 10.1109/ICRA.2017.7989523

DO - 10.1109/ICRA.2017.7989523

M3 - Conference Contribution (Conference Proceeding)

AN - SCOPUS:85028006759

SN - 9781509046348

SP - 4509

EP - 4514

BT - ICRA 2017 - IEEE International Conference on Robotics and Automation

PB - Institute of Electrical and Electronics Engineers (IEEE)

Y2 - 29 May 2017 through 3 June 2017

ER -

O-POCO: Online point cloud compression mapping for visual odometry and SLAM

Abstract

Conference

Access to Document

Handle.net

Other files and links

Fingerprint

Cite this