  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
forboth classical geometry-based and recent learning-based SLAM algorithms, a
baseline mapping method, semantic segmentation and panoptic segmentation.
