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.

In recent years, more attention has been paid to water surface environment protection. Current water surface waste cleaning mainly relies on manual operations, which are low-efficiency and dangerous. Therefore, in this paper, we design a fully autonomous water surface cleaning robot, SMURF, which achieves high-efficiency water surface cleaning without human operation and adapts to be used in various types of real-world water bodies. In addition, we propose a novel coverage path planning method on water surfaces and an improved nonlinear model predictive controller. The real-world experiment shows that SMURF works well in different kinds of water bodies and achieves much higher efficiency than traditional water surface cleaning methods.

Unmanned surface vehicles (USVs) have wide applications in marine inspection and monitoring, terrain mapping, and water surface cleaning. Accurate and robust environment perception ability is essential for achieving autonomy in USVs. Small object detection on water surfaces is an important environment perception task, typically achieved by visual detection using cameras. However, existing vision-based small object detection methods suffer from performance degradation in complex water surface environments. Therefore, in this paper, we propose a millimeter-wave (mmWave) radar-aided vision detection method that enables automatic data association and fusion between mmWave radar point clouds and images. Through testing on real-world data, the proposed method demonstrates significant performance improvement over vision-based object detection methods without introducing more computational costs, making it suitable for real-time application on USVs. Furthermore, the image–radar data association model in the proposed method can serve as a plug-and-play module for other object detection methods.