An Enhanced Navigation System With Predictive Motion Planning for Unmanned Surface Vehicles in GNSS‐Attenuated Dynamic Urban Waterways

ABSTRACT Unmanned surface vehicles (USVs) applied in urban waterways may suffer from inaccurate localization due to the Global Navigation Satellite System (GNSS) attenuation, and be susceptible to collision threats from vessels of human‐induced violations and piloting errors. This paper proposes an enhanced navigation framework capable of stable continuous localization, dynamic obstacle perception, and collision‐free motion planning. A tightly coupled LiDAR‐Visual‐Inertial Odometry via Smoothing and Mapping (LVI–SAM) is selected as the fundamental framework of localization and mapping subsystem. An incrementally mapping data structure is incorporated to improve the computation efficiency and accuracy of the LiDAR odometry optimization process. To mitigate the long‐term accumulating odometry drift, valid GNSS measurements are introduced to provide absolute reference in the factor graph optimization framework, which can achieve optimum state estimation by maximum a posteriori given all the noisy measurements from multiple sensors. Furthermore, a dynamic occupancy grid map framework, based on sequential Monte Carlo and probability hypothesis density method, is developed to enhance situational awareness of USVs for risk anticipation of dynamic obstacles and facilitate predictive avoidance. Extensive real‐world experiments have been carried out to demonstrate that the proposed autonomous navigation system is capable of robust and accurate localization over long‐term urban waterway navigation, and dynamic obstacle avoidance through a safer predictive strategy.