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