  Simultaneous localization and mapping (SLAM) is one of the key components of
a control system that aims to ensure autonomous navigation of a mobile robot in
unknown environments. In a variety of practical cases a robot might need to
travel long distances in order to accomplish its mission. This requires
long-term work of SLAM methods and building large maps. Consequently the
computational burden (including high memory consumption for map storage)
becomes a bottleneck. Indeed, state-of-the-art SLAM algorithms include specific
techniques and optimizations to tackle this challenge, still their performance
in long-term scenarios needs proper assessment. To this end, we perform an
empirical evaluation of two widespread state-of-the-art RGB-D SLAM methods,
suitable for long-term navigation, i.e. RTAB-Map and Voxgraph. We evaluate them
in a large simulated indoor environment, consisting of corridors and halls,
while varying the odometer noise for a more realistic setup. We provide both
qualitative and quantitative analysis of both methods uncovering their
strengths and weaknesses. We find that both methods build a high-quality map
with low odometry noise but tend to fail with high odometry noise. Voxgraph has
lower relative trajectory estimation error and memory consumption than
RTAB-Map, while its absolute error is higher.
