SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM

Bruno Bodin, Harry Wagstaff, Sajad Saecdi, Luigi Nardi, Emanuele Vespa, John Mawer, Andy Nisbet, Mikel Lujan, Steve Furber, Andrew J. Davison, Paul H.J. Kelly, Michael F.P. O'Boyle

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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SLAM is becoming a key component of robotics and augmented reality (AR) systems. While a large number of SLAM algorithms have been presented, there has been little effort to unify the interface of such algorithms, or to perform a holistic comparison of their capabilities. This is a problem since different SLAM applications can have different functional and non-functional requirements. For example, a mobile phone-based AR application has a tight energy budget, while a UAV navigation system usually requires high accuracy. SLAMBench2 is a benchmarking framework to evaluate existing and future SLAM systems, both open and close source, over an extensible list of datasets, while using a comparable and clearly specified list of performance metrics. A wide variety of existing SLAM algorithms and datasets is supported, e.g. ElasticFusion, InfiniTAM, ORB-SLAM2, OKVIS, and integrating new ones is straightforward and clearly specified by the framework. SLAMBench2 is a publicly-available software framework which represents a starting point for quantitative, comparable and val-idatable experimental research to investigate trade-offs across SLAM systems.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Robotics and Automation, ICRA 2018
Number of pages8
ISBN (Electronic)9781538630815
Publication statusPublished - 10 Sept 2018
Event2018 IEEE International Conference on Robotics and Automation - Brisbane, Australia
Duration: 21 May 201825 May 2018


Conference2018 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2018


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