# Bidirectional Sliding Window for Boundary Recognition of Pavement Construction Area Using GPS-RTK
## 1 Introduction  
In the field of robotic navigation, the road boundary recognition is mainly achieved using vision-based passive sensors [1; 2] and lidar-based active sensors [3; 4; 5]. Vision-based sensors generally recognize road boundaries based on the physical and geometric characteristics of the roads, and lidar sensors generally determine the characteristics according to road boundary point cloud data to identify road boundaries. The literature shows that the fusion of the two algorithms can achieve a certain accuracy. However, the unconstructed road surface cannot provide sufficient physical, geometric features or point cloud data for identification by vision-based and lidar-based sensors.  
Identification of the road boundary is also critical in the field of geographic information and plays a vital role in mapping, public management and road data updates. Many documents propose using GPS trajectories to extract and update road data, which generally involves using clustering [6; 7; 8], kernel density estimation [9; 10], trajectory merging [11; 12] and geometric modelling [13; 14]. However, these methods extract road boundaries by analyzing and processing a large number of GPS trajectories, which are generally of meter-level accuracy [15]. According to actual construction requirements, the length of Pavement Construction Area (PCA) boundaries is generally only about 50 m and the width is only 10 to 20 m, but the positioning needs to be accurate to 1 decimeter. In addition, three typesof construction joint--including stepped seams, miter seams and flat seams--are generally adopted during road construction, and both sides of the road may be straight or curved [16]. Figure 1 shows these six cases respectively.  
To meet the needs of unmanned pavement construction, we generally design several paths parallel to one side of the road and the articulated road roller is required to track these planned paths in turn, as shown in Figure 2. Therefore, the designed construction boundaries, marked by the red curves in Figure 2, should accord with the curvature radius of the road. In general, the curvature radius of the road is 60 to 300 m. In this work, a curvature radius of 70 to 300 m was set for simulation experiments.  
Figure 1: Three types of straight polygon construction joints: (**a**) Stepped seams; (**b**) Miter seams; (**c**) Flat seams, and three types of curved polygon construction joints: (**d**) Stepped seams; (**e**) Miter seams; (**f**) Flat seams.  
Figure 2: The articulated road roller tracks the planned paths in sequence form 1 to 8, where 1, 4 and 7 indicate that the roller is moving forward; 2, 5 and 8 indicate that the roller is driving in reverse; and 3 and 6 indicate that the roller is turning to the next path.  
Boundary Recognition of Pavement Construction Area Using Global Positioning System--Real Time Kinematic (GPS-RTK)  
In this section, GPS-RTK positioning technology is firstly introduced. Then, we describe how the latitude and longitude WGS-84 coordinate system data were extracted and converted into Gaussian plane coordinate format. Next, we describe how BSW was used to identify the Possible Irrelevant Points (PIP), and finally, we show how curve fitting with Relevant Points (RP) was applied to recognize the boundaries of the PCA. A flowchart describing PCA boundary recognition is shown in Figure 3.