DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping And Navigation We present a novel dataset for training and benchmarking semantic SLAM methods. The dataset consists of 200 long sequences, each one containing 30005000 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 physicallybased 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 largescale benchmark focused on the mapping component of SLAM. The dataset is split into trainvalidationtest parts sampled from different sets of virtual houses. We present benchmarking results for both classical geometrybased 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.

Learning Topometric Semantic Maps from Occupancy Grids Todayxe2x80x99s mobile robots are expected to operate in complex environments they share with humans. To allow intuitive humanrobot collaboration, robots require a humanlike understanding of their surroundings in terms of semantically classified instances. In this paper, we propose a new approach for deriving such instancebased semantic maps purely from occupancy grids. We employ a combination of deep learning techniques to detect, segment and extract door hypotheses from a randomsized map. The extraction is followed by a postprocessing chain to further increase the accuracy of our approach, as well as place categorization for the three classes room, door and corridor. All detected and classified entities are described as instances specified in a common coordinate system, while a topological map is derived to capture their spatial links. To train our two neural networks used for detection and map segmentation, we contribute a simulator that automatically creates and annotates the required training data. We further provide insight into which features are learned to detect doorways, and how the simulated training data can be augmented to train networks for the direct application on realworld grid maps. We evaluate our approach on several publicly available realworld data sets. Even though the used networks are solely trained on simulated data, our approach demonstrates high robustness and effectiveness in various realworld indoor environments.