  Unmanned surface vehicles (USVs) have great value with their ability to
execute hazardous and time-consuming missions over water surfaces. Recently,
USVs for inland waterways have attracted increasing attention for their
potential application in autonomous monitoring, transportation, and cleaning.
However, unlike sailing in open water, the challenges posed by scenes of inland
waterways, such as the complex distribution of obstacles, the global
positioning system (GPS) signal denial environment, the reflection of bank-side
structures, and the fog over the water surface, all impede USV application in
inland waterways. To address these problems and stimulate relevant research, we
introduce USVInland, a multisensor dataset for USVs in inland waterways. The
collection of USVInland spans a trajectory of more than 26 km in diverse
real-world scenes of inland waterways using various modalities, including
lidar, stereo cameras, millimeter-wave radar, GPS, and inertial measurement
units (IMUs). Based on the requirements and challenges in the perception and
navigation of USVs for inland waterways, we build benchmarks for simultaneous
localization and mapping (SLAM), stereo matching, and water segmentation. We
evaluate common algorithms for the above tasks to determine the influence of
unique inland waterway scenes on algorithm performance. Our dataset and the
development tools are available online at
https://www.orca-tech.cn/datasets.html.
