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

张宁 DISCOMAN: Dataset of Indoor SCenes for Odometry, Mapping and Navigation

Pavel Kirsanov, Airat Gaskarov, Filipp Konokhov, Konstantin Sofiiuk, Anna Vorontsova
https://arxiv.org/abs/1909.12146
Development of a Navigation Algorithm for Optimal Path Planning for Autonomous Electric Vehicles
https://ieeexplore.ieee.org/document/8968036

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.

我们提供了一个用于训练和基准化语义SLAM方法的新颖数据集，该数据集由200个长序列组成，每个序列包含3000-5000个数据帧。 我们使用现实的首页布局生成序列。为此，我们对模拟简单家用机器人运动的轨迹进行采样，然后沿这些轨迹渲染框架。 每个数据帧包含：a）使用基于物理的渲染生成的RGB图像，b）模拟的深度测量，c）模拟的IMU读数，以及d）房屋的地面实况占用格网。 与现有数据集相比，我们的数据集具有更广泛的用途，并且是针对SLAM映射组件的第一个大型基准测试。数据集分为从不同的虚拟房屋集合中采样的训练/验证/测试部分。 我们介绍了基于经典几何[1]，[2]和最近基于学习[3]的SLAM算法，基准线映射方法[4]，语义分割[5]和全景分割[6]的基准测试结果。 用于复制我们的实验的数据集和源代码将在发布时公开提供。