Lidar-Based Stable Navigable Region Detection for Unmanned Surface Vehicles

Detection of navigable regions for the unmanned surface vehicles (USVs) sailing on the narrow rivers is very important. Existing detection methods mostly depend on cameras, which is sensitive to the environment and cannot provide reliable navigable regions for sailing. In this article, we propose a scheme to directly process 3-D LiDAR data to achieve accurate and stable navigable regions' detection. In our scheme, a deep learning-based method is used to semantically segment the river objects, and then, a Kalman Filter (KF)-based multiobjects tracking method is deployed to accurately track the bank objects. Finally, the navigable regions are modeled by a wave frontier detection (WFD)-based method. In order to validate the performance of the proposed detection scheme, field experiments are carried out in a narrow and complicated river to prove the performance of the proposed scheme, and comparison experiments and ablation studies are conducted to show the impacts of deep segmentation and KF-based tracking methods. Experimental results show that our scheme outperforms other methods in accuracy up to 10% and smoothness up to 47%.