In this section
Below is a list of all relevant publications authored by Robotics Forum members.
@inproceedings{Bodin:2018:10.1109/ICRA.2018.8460558,
author = {Bodin, B and Wagstaff, H and Saeedi, S and Nardi, L and Vespa, E and Mawer, J and Nisbet, A and Lujan, M and Furber, S and Davison, AJ and Kelly, PHJ and O'Boyle, MFP},
doi = {10.1109/ICRA.2018.8460558},
pages = {3637--3644},
publisher = {IEEE},
title = {SLAMBench2: multi-objective head-to-head benchmarking for visual SLAM},
url = {http://dx.doi.org/10.1109/ICRA.2018.8460558},
year = {2018}
}
TY - CPAPER
AB - 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.
AU - Bodin,B
AU - Wagstaff,H
AU - Saeedi,S
AU - Nardi,L
AU - Vespa,E
AU - Mawer,J
AU - Nisbet,A
AU - Lujan,M
AU - Furber,S
AU - Davison,AJ
AU - Kelly,PHJ
AU - O'Boyle,MFP
DO - 10.1109/ICRA.2018.8460558
EP - 3644
PB - IEEE
PY - 2018///
SN - 1050-4729
SP - 3637
TI - SLAMBench2: multi-objective head-to-head benchmarking for visual SLAM
UR - http://dx.doi.org/10.1109/ICRA.2018.8460558
UR - https://ieeexplore.ieee.org/document/8460558
UR - http://hdl.handle.net/10044/1/64308
ER -
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