  Automatic underground parking has attracted considerable attention as the
scope of autonomous driving expands. The auto-vehicle is supposed to obtain the
environmental information, track its location, and build a reliable map of the
scenario. Mainstream solutions consist of well-trained neural networks and
simultaneous localization and mapping (SLAM) methods, which need numerous
carefully labeled images and multiple sensor estimations. However, there is a
lack of underground parking scenario datasets with multiple sensors and
well-labeled images that support both SLAM tasks and perception tasks, such as
semantic segmentation and parking slot detection. In this paper, we present
SUPS, a simulated dataset for underground automatic parking, which supports
multiple tasks with multiple sensors and multiple semantic labels aligned with
successive images according to timestamps. We intend to cover the defect of
existing datasets with the variability of environments and the diversity and
accessibility of sensors in the virtual scene. Specifically, the dataset
records frames from four surrounding fisheye cameras, two forward pinhole
cameras, a depth camera, and data from LiDAR, inertial measurement unit (IMU),
GNSS. Pixel-level semantic labels are provided for objects, especially ground
signs such as arrows, parking lines, lanes, and speed bumps. Perception, 3D
reconstruction, depth estimation, and SLAM, and other relative tasks are
supported by our dataset. We also evaluate the state-of-the-art SLAM algorithms
and perception models on our dataset. Finally, we open source our virtual 3D
scene built based on Unity Engine and release our dataset at
https://github.com/jarvishou829/SUPS.
