2026-10-14T18:22:06Z https://www.scidb.cn/oai

10.57760/sciencedb.ai.00003 2025-04-18T09:42:41Z

2025-04-18 CID-SIMS Dataset doi:10.57760/sciencedb.ai.00003 en To advance research in leveraging semantic information and multi-sensor data to enhance the performances of SLAM and 3D reconstruction in complex indoor scenes, we propose a novel and complex indoor dataset named CID-SIMS, where semantic annotated RGBD images, inertial measurement unit (IMU) measurements and wheel odometer data are provided from a ground wheeled robot viewpoint. The dataset consists of 22 challenging sequences captured in 9 different scenes including office building and apartment environments. Notably, our dataset achieves two significant breakthroughs. Firstly, semantic information and multi-sensor data are provided meanwhile for the first time. Secondly, GeoSLAM is utilized for the first time to generate ground truth trajectories and 3D point clouds within 2 cm accuracy. With spatial-temporal synchronous ground truth trajectories and 3D point clouds, our dataset is capable of evaluating SLAM and 3D reconstruction algorithms in a unified global coordinate system. Dataset; Ground wheeled robots; Semantic segmentation; Multi-sensor data; SLAM; 3D reconstruction Yidi Zhang Ning An Chenhui Shi Shuo Wang Hao Wei Pengju Zhang Xinrui Meng Zengpeng Sun Jinke Wang Wenliang Liang Fulin Tang Yihong Wu PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset http://www.doi.org/https://doi.org/10.1177/02783649231222507 Science Data Bank