Robot@Home2, a robotic dataset of home environments

The Robot-at-Home dataset (Robot@Home, paper here) is a collection of raw and processed data from five domestic settings compiled by a mobile robot equipped with 4 RGB-D cameras and a 2D laser scanner. Its main purpose is to serve as a testbed for semantic mapping algorithms through the categorization of objects and/or rooms. This dataset is unique in three aspects: The provided data were captured with a rig of 4 RGB-D sensors with an overall field of view of 180°H. and 58°V., and with a 2D laser scanner. It comprises diverse and numerous data: sequences of RGB-D images and laser scans from the rooms of five apartments (87,000+ observations were collected), topological information about the connectivity of these rooms, and 3D reconstructions and 2D geometric maps of the visited rooms. The provided ground truth is dense, including per-point annotations of the categories of the objects and rooms appearing in the reconstructed scenarios, and per-pixel annotations of each RGB-D image within the recorded sequences During the data collection, a total of 36 rooms were completely inspected, so the dataset is rich in contextual information of objects and rooms. This is a valuable feature, missing in most of the state-of-the-art datasets, which can be exploited by, for instance, semantic mapping systems that leverage relationships like pillows are usually on beds or ovens are not in bathrooms. Robot@Home2 Robot@Home2, is an enhanced version aimed at improving usability and functionality for developing and testing mobile robotics and computer vision algorithms. It consists of three main components. Firstly, a relational database that states the contextual information and data links, compatible with Standard Query Language. Secondly,a Python package for managing the database, including downloading, querying, and interfacing functions. Finally, learning resources in the form of Jupyter notebooks, runnable locally or on the Google Colab platform, enabling users to explore the dataset without local installations. These freely available tools are expected to enhance the ease of exploiting the Robot@Home dataset and accelerate research in computer vision and robotics. If you use Robot@Home2, please cite the following paper: Gregorio Ambrosio-Cestero, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez, The Robot@Home2 dataset: A new release with improved usability tools, in SoftwareX, Volume 23, 2023, 101490, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2023.101490. @article{ambrosio2023robotathome2, title = {The Robot@Home2 dataset: A new release with improved usability tools}, author = {Gregorio Ambrosio-Cestero and Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez}, journal = {SoftwareX}, volume = {23}, pages = {101490}, year = {2023}, issn = {2352-7110}, doi = {https://doi.org/10.1016/j.softx.2023.101490}, url = {https://www.sciencedirect.com/science/article/pii/S2352711023001863}, keywords = {Dataset, Mobile robotics, Relational database, Python, Jupyter, Google Colab} } Version history v1.0.1 Fixed minor bugs. v1.0.2 Fixed some inconsistencies in some directory names. Fixes were necessary to automate the generation of the next version. v2.0.0 SQL based dataset. Robot@Home v1.0.2 has been packed into a sqlite database along with RGB-D and scene files which have been assembled into a hierarchical structured directory free of redundancies. Path tables are also provided to reference files in both v1.0.2 and v2.0.0 directory hierarchies. This version has been automatically generated from version 1.0.2 through the toolbox. v2.0.1 A forgotten foreign key pair have been added. v.2.0.2 The views have been consolidated as tables which allows a considerable improvement in access time. v.2.0.3 The previous version does not include the database. In this version the database has been uploaded.家用机器人数据集（Robot@Home，详见相关论文）是由搭载4台RGB-D相机（RGB-D Camera）与1台2D激光扫描仪（2D Laser Scanner）的移动机器人在5个家庭环境中采集的原始与处理后数据集合。其核心用途是作为语义建图算法的测试基准，用于对物体和/或房间进行分类标注。 该数据集具备三大独特优势：其一，采集所用的4台RGB-D传感器组合的总视场角为水平180°、垂直58°，并搭配2D激光扫描仪；其二，数据类型丰富多样且体量庞大：涵盖5套公寓房间的RGB-D图像序列与激光扫描数据（累计采集超过87000条观测数据）、这些房间的拓扑连接信息，以及所遍历房间的三维重建结果与二维几何地图；其三，提供的真值标注（Ground Truth）十分稠密，包含重建场景中出现的物体与房间类别的逐点标注，以及录制序列中每张RGB-D图像的逐像素标注。 数据采集阶段共完成36个房间的全面巡检，因此该数据集富含物体与房间的上下文信息——这是多数主流数据集所缺失的宝贵特性，可被诸如利用“枕头通常放置于床上”“烤箱不会出现在浴室”这类关联关系的语义建图系统所利用。 Robot@Home2是该数据集的增强版本，旨在提升移动机器人与计算机视觉算法开发测试的易用性与功能性。其主要包含三大组成部分：一是支持结构化查询语言（Structured Query Language, SQL）的关系型数据库，用于存储上下文信息与数据关联关系；二是用于管理数据库的Python工具包，涵盖下载、查询与接口调用等功能；三是以Jupyter笔记本（Jupyter Notebook）形式提供的学习资源，可在本地或Google Colab平台运行，使用户无需本地安装即可探索该数据集。 这些免费开放的工具旨在降低Robot@Home数据集的使用门槛，加速计算机视觉与机器人领域的研究进展。若您使用Robot@Home2，请引用以下论文： Gregorio Ambrosio-Cestero, Jose-Raul Ruiz-Sarmiento, Javier Gonzalez-Jimenez. 《Robot@Home2数据集：一款搭载易用性增强工具的新版数据集》[J]. SoftwareX, 2023, 23: 101490. ISSN 2352-7110. DOI: https://doi.org/10.1016/j.softx.2023.101490. 以下为对应的BibTeX引用条目： @article{ambrosio2023robotathome2, title = {The Robot@Home2 dataset: A new release with improved usability tools}, author = {Gregorio Ambrosio-Cestero and Jose-Raul Ruiz-Sarmiento and Javier Gonzalez-Jimenez}, journal = {SoftwareX}, volume = {23}, pages = {101490}, year = {2023}, issn = {2352-7110}, doi = {https://doi.org/10.1016/j.softx.2023.101490}, url = {https://www.sciencedirect.com/science/article/pii/S2352711023001863}, keywords = {Dataset, Mobile robotics, Relational database, Python, Jupyter, Google Colab} } 版本更新记录 v1.0.1 修复了少量细微漏洞。 v1.0.2 修正了部分目录名称存在的不一致问题，此次修正确保了下一版本的自动化生成流程可正常执行。 v2.0.0 基于结构化查询语言的数据集版本。原Robot@Home v1.0.2数据已打包整合至SQLite数据库（SQLite Database）中，RGB-D与场景文件也被整理为无冗余的层级化目录结构。同时提供了路径对照表，可分别指向v1.0.2与v2.0.0版本的目录层级文件。该版本通过工具箱从v1.0.2版本自动生成。 v2.0.1 补充了一组此前遗漏的外键对。 v2.0.2 将视图整合为数据表，大幅提升了数据访问速度。 v2.0.3 此前版本未包含数据库文件，本版本已上传完整数据库文件。