DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping and Navigationстатья

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Дата последнего поиска статьи во внешних источниках: 7 июля 2020 г.

Работа с статьей

[1] Discoman: Dataset of indoor scenes for odometry, mapping and navigation / P. Kirsanov, A. Gaskarov, F. Konokhov et al. // 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). — 2019. — P. 2470–2477. 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. [ DOI ]

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