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Simultaneous localisation 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 [9], 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.

We present a novel dataset for training and benchmarking semantic SLAM methods. The dataset consists of 200 long sequences, each one containing 3000-5000 data frames. We generate the sequences using realistic home layouts. For that we sample trajectories that simulate motions of a simple home robot, and then render the frames along the trajectories. Each data frame contains a) RGB images generated using physically-based rendering, b) simulated depth measurements, c) simulated IMU readings and d) ground truth occupancy grid of a house. Our dataset serves a wider range of purposes compared to existing datasets and is the first large-scale benchmark focused on the mapping component of SLAM. The dataset is split into train/validation/test parts sampled from different sets of virtual houses. We present benchmarking results for both classical geometry-based [1], [2] and recent learningbased [3] SLAM algorithms, a baseline mapping method [4], semantic segmentation [5] and panoptic segmentation [6]. The dataset and source code for reproducing our experiments will be publicly available at the time of publication.

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