# Riverbank Following Planner (RBFP) for USVs Based on Point Cloud Data
## 2 Related Work
### River Shoreline and Water-Slore-Line Inspection Review  
There are three methods for detecting the boundary between the riverbank and water using sensors such as LiDAR and cameras.  
The first method involves the linear fitting of edge pixels using the linear detection of edge points. [PERSON] et al. [27] used a vision sensor mounted on the float to collect picture information and calculate water-shore-line. However, strong light and waves can affect camera data stability, requiring some time to adapt to interference before collecting information. [PERSON] et al. [28] suggested real-time estimation of a region of interest (ROI)during sea antenna detection, which can calculate the horizon and re-project the result onto the original image. [PERSON] et al. [29] proposed a new method using multi-scale cross-modal line features, integrating multi-scale filtering, Hough transforms, and intensity change methods to detect water-shore-line, and the results are superior to other methods. [PERSON] et al. [30] proposed a method based on color visual data from an omnidirectional camera. Although their method solved many segmentation issues and provided a single camera 360\({}^{\circ}\) monitoring, the authors acknowledged that the effective range was limited due to the wide observation angle.  
The second method involves image segmentation. [PERSON] et al. [31] proposed online detection through constraint and unsupervised segmentation of video data collected by the USV to extract the boundary line. But this method may not accurately detect partially or completely submerged objects because it relies on the boundary above the water surface as the detection line. [PERSON] [32] proposed a method to build visually uniform areas through cohesive clustering. However, the algorithm needs to merge areas, and even tiny images in online scenes can take a long time to complete the analysis. Combining the above two methods can increase the accuracy of coastline image extraction, but image data are vulnerable to climate (rain, fog) and disturbance of wind and water flow caused by image acquisition equipment.  
The third method involves scanning the boundary between the water surface and riverbank with LiDAR, which is unaffected by weather and can determine the three-dimensional coordinates of objects and return point cloud images [33]. [PERSON] et al. [34] used the means shift algorithm to extract and segment the coastline integrated with the LiDAR data and satellite images. [PERSON] et al. [35] proposed using LiDAR data to segment land and water, form and enhance land and water objects, and track boundaries as coastline features. [PERSON] et al. [36] proposed a method for coastline extraction by using LiDAR point cloud data and aerial orthographic film. The boundary of the LiDAR points after classification is determined by the improved convex hull algorithm. The coastline is defined as the result of the separation boundary between water points and non-water points of the LiDAR. On the human-made banks, the accuracy is about 0.5 m, and on the natural shoreline, the accuracy is about 1.5 m. Due to the change in water surface caused by tidal action, the shoreline detection will also change. [PERSON] et al. [37] proposed a new algorithm to extract coastlines by dividing LiDAR point clouds by boundary points, which is especially suitable for shallow and muddy environments. The average error of the points of this algorithm is reduced from 1m to 0.5 m, and its standard deviation (0.1656) and variance (0.0274) are lower than the contour tracking method (0.2116 and 0.0448, respectively).  
Based on the reported results, LiDAR is more accurate and robust than the other two methods. However, the shoreline can be affected by tidal changes in the water surface.