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

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

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


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.

Bibliographical metadata

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


Conference2018 IEEE International Conference on Robotics and Automation
Abbreviated titleICRA 2018