  Simultaneous localization and mapping (SLAM) is an essential component of
robotic systems. In this work we perform a feasibility study of RGB-D SLAM for
the task of indoor robot navigation. Recent visual SLAM methods, e.g. ORBSLAM2
\cite{mur2017orb}, demonstrate really impressive accuracy, but the experiments
in the papers are usually conducted on just a few sequences, that makes it
difficult to reason about the robustness of the methods. Another problem is
that all available RGB-D datasets contain the trajectories with very complex
camera motions. In this work we extensively evaluate ORBSLAM2 to better
understand the state-of-the-art. First, we conduct experiments on the popular
publicly available datasets for RGB-D SLAM across the conventional metrics. We
perform statistical analysis of the results and find correlations between the
metrics and the attributes of the trajectories. Then, we introduce a new large
and diverse HomeRobot dataset where we model the motions of a simple home
robot. Our dataset is created using physically-based rendering with realistic
lighting and contains the scenes composed by human designers. It includes
thousands of sequences, that is two orders of magnitude greater than in
previous works. We find that while in many cases the accuracy of SLAM is very
good, the robustness is still an issue.

Computer Vision and Pattern Recognition;Robotics