SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM
Abstract
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 phonebased 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 validatable experimental research to investigate trade-offs across SLAM systems.
Cite
@article{arxiv.1808.06820,
title = {SLAMBench2: Multi-Objective Head-to-Head Benchmarking for Visual SLAM},
author = {Bruno Bodin and Harry Wagstaff and Sajad Saeedi and Luigi Nardi and Emanuele Vespa and John H Mayer and Andy Nisbet and Mikel Luján and Steve Furber and Andrew J Davison and Paul H. J. Kelly and Michael O'Boyle},
journal= {arXiv preprint arXiv:1808.06820},
year = {2018}
}