SLAMBench 3.0: Systematic Automated Reproducible Evaluation of SLAM Systems for Robot Vision Challenges and Scene Understanding

Mihai Bujanca, Paul Gafton, Sajad Saeedi, Andy Nisbet, Bruno Bodin, Michael F. P. O'Boyle, Andrew J. Davison, Paul H. J. Kelly, Graham Riley, Barry Lennox, Mikel Luján, Steve Furber

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

As the SLAM research area matures and the number of SLAM systems available increases, the need for frameworks that can objectively evaluate them against prior work grows. This new version of SLAMBench moves beyond traditional visual SLAM, and provides new support for scene understanding and non-rigid environments (dynamic SLAM). More concretely for dynamic SLAM, SLAMBench 3.0 includes the first publicly available implementation of DynamicFusion, along with an evaluation infrastructure. In addition, we include two SLAM systems (one dense, one sparse) augmented with convolutional neural networks for scene understanding, together with datasets and appropriate metrics. Through a series of use-cases, we demonstrate the newly incorporated algorithms, visulation aids and metrics (6 new metrics, 4 newdatasets and 5 new algorithms).
Original languageEnglish
Title of host publication2019 International Conference on Robotics and Automation (ICRA)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages6351-6358
Number of pages8
ISBN (Electronic)978-1-5386-6027-0
ISBN (Print)978-1-5386-8176-3
DOIs
Publication statusPublished - 12 Aug 2019
Event2019 IEEE International Conference on Robotics and Automation (ICRA) - Montreal, Canada
Duration: 20 May 201924 May 2019
https://www.icra2019.org/

Publication series

Name
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
ISSN (Print)1050-4729
ISSN (Electronic)2577-087X

Conference

Conference2019 IEEE International Conference on Robotics and Automation (ICRA)
Abbreviated titleICRA 2019
CountryCanada
CityMontreal
Period20/05/1924/05/19
Internet address

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