## 4 Semantic Information Extraction
### IPM-Based Hybrid Semantic Edge Extraction  
The original synthesized surround view image often contains a large number of ground markings, which can be fed into SLAM systems as high-quality road sign information. However, the information of the synthesized surround view image is also disturbed by a large number of ground spots. At the same time, the reverse-perspective projection transformation during the synthesizing process will distort objects with a certain height on the ground. Therefore, an edge segmentation module needs to be designed to reject the above interference edges, so that the high-quality effective edges on the ground can be retained. The process of hybrid semantic edge extraction is shown in Figure 3.  
Primary edge extraction: The edges on the input surround view synthesized image can be extracted by traditional edge detection methods, such as the Canny edge detector and LSD detector. On the one hand, the edge of the ground mark and the projected edge of the object can be well extracted; on the other hand, traditional edge detectors will also detect those invalid edges, such as the edges of surrounding robots, pillars, and light spots, so the original edges need to be processed to some extent. Due to the segmentation of the passable space, edges that are located on objects above the ground level can be easily removed. However, there is still a considerable part of the spot edge that cannot be removed in this way, and the part of the passable space boundary that is affected by distortion requires additional processing; otherwise, it will not be able to be entered into the subsequent SLAM system and achieve the ideal positioning and mapping results. IPM-based edge segmentation: Considering that the distortion edges are mainly centered on the photocenter of each camera and distributed in the direction of the rays, an intuitive idea is to build a polar coordinate system with its photocenter as the origin for each camera and count the number of edge points in each direction. However, the segmentation method based on ray accumulation has problems such as erroneous removal of dense small edges, such as the edge of zebra crossings, threshold coupling of angle parameters and the number of edge points in the fan, and inaccurate results. In order to consider the geometric distribution of edges, further attempts can be made to detect line segments in the edge image. Specifically, traditional linear detectors, such as those based on the Hough transform, can extract segments of a length from the original edge image, then calculate the distance between the line in which each segment is located and the camera center of its field of view. Finally, those segments that are small enough away are marked as distorted edge areas. However, the segmentation method based on segment detection has problems such as the erroneous exclusion of some unconnected edge points, and the distance between the line segment and the optical center will also affect the selection of the final distance threshold. To further consider the fine structure of the edge, different consecutive edges can first be distinguished before edge segmentation. Then, for each edge, the Douglas-Peucker algorithm is used to approximate the edges. The line segments obtained by approximating the contour of each edge are connected to the center of the camera photocenter in the field  
Figure 3: The procedure of hybrid semantic edges extraction. of view, and then, the angle between the line and the line segment is calculated to remove those parts whose angles are less than a certain threshold. The main advantage of the segmentation method based on polyline approximation is that it simplifies the operation of segment detection and limits segment approximation to the interior of each edge. At the same time, for edges at different distances, the way of evaluating the angle is more stable and consistent than the previous way of evaluating the distance, especially for those edges that are very far from the robot.