RISEDB

A novel public dataset for developing and benchmarking indoor localization systems. We have selected and 3D mapped a set of representative indoor environments including a large office building, a conference room, a workshop, an exhibition area and a restaurant. to collect long sequences of spherical and stereo images, together with all the sensor readings coming from a consumer smartphone and locate them inside the map with centimetre accuracy. The dataset addresses many of the limitations of existing indoor localization datasets regarding the scale and diversity of the mapped buildings; the number of acquired sequences under varying conditions; the accuracy of the ground-truth trajectory; the availability of a detailed 3D model and the availability of different sensor types. It enables the benchmarking of existing and the development of new indoor localization approaches, in particular for deep learning based systems that require large amounts of labeled training data.一款用于开发与基准测试室内定位系统的新型公开数据集。本研究团队选取并完成了多组代表性室内场景的三维建模，涵盖大型办公楼、会议室、车间、展区及餐厅；同步采集了长序列球面全景图像与立体图像，同时记录消费级智能手机输出的全部传感器读数，并以厘米级精度将采集数据匹配至对应三维地图中。本数据集解决了现有室内定位数据集的多项局限，涉及建模建筑的规模与多样性、不同采集条件下的有效序列数量、真值轨迹的精度、详细三维模型的可获取性以及多类型传感器数据的支持性等维度。其可为现有室内定位方法的基准测试与新型定位方案的研发提供有力支撑，尤其适用于依赖海量标注训练数据的深度学习（deep learning）相关系统。