  Simultaneous localization and mapping (SLAM) algorithms are essential for the
autonomous navigation of mobile robots. With the increasing demand for
autonomous systems, it is crucial to evaluate and compare the performance of
these algorithms in real-world environments. In this paper, we provide an
evaluation strategy and real-world datasets to test and evaluate SLAM
algorithms in complex and challenging indoor environments. Further, we analysed
state-of-the-art (SOTA) SLAM algorithms based on various metrics such as
absolute trajectory error, scale drift, and map accuracy and consistency. Our
results demonstrate that SOTA SLAM algorithms often fail in challenging
environments, with dynamic objects, transparent and reflecting surfaces. We
also found that successful loop closures had a significant impact on the
algorithm's performance. These findings highlight the need for further research
to improve the robustness of the algorithms in real-world scenarios.
