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Simultaneous localization and mapping (SLAM) and 3D reconstruction have numerous applications for indoor ground wheeled robots such as floor sweeping and food delivery. 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 nine 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 two-centimeter 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. We evaluate state-of-the-art SLAM and 3D reconstruction approaches on our dataset, demonstrating that our benchmark is applicable. The dataset is publicly available on https://cid-sims.github.io. [ABSTRACT FROM AUTHOR]

Fe2O3/ZnO/Ag ternary composite photocatalytic material was prepared by simple hydrothermal method, and its structure and photocatalytic properties were studied. The experimental results show that Fe2O3/ZnO/Ag exhibits better photocatalytic performance. After two hours of UV irradiation, the degradation rates of orange II and methyl orange reached 91.9% and 75.9%, respectively. The design and preparation of the photocatalyst provide a theoretical basis for the practical application of photocatalytic technology. [ABSTRACT FROM AUTHOR]