## 2 Related Work
### Off-Road Point Cloud Semantic Segmentation  
The above methods for structured road provide many valuable references for off-road segmentation. The biggest difference between the off-road environments and the structured road scene is that its drivable area has no lane lines, no obvious road boundaries, and even no regular shape. Such off-road is very different from structured road, so it is difficult to directly apply the 3D point cloud semantic segmentation algorithm based on structured road scenes. At present, the semantic segmentation algorithms of off-road scenes are mainly divided into three categories: feature engineering based on point clouds, weakly supervised learning, and transfer learning  
The feature engineering algorithm based on point clouds performs road segmentation by extracting the geometric features of roads in off-road scenes. On one hand, [PERSON] et al. [21] focus on identifying negative obstacles on the road. First, three LiDARs are installed directly above and on both sides of the vehicle. Then a mathematical model of the LiDAR scan line is established, and an adaptive filtering algorithm is proposed to identify negative obstacles based on this model. Finally, the operation results of the three LiDARs are fused to detect the drivable area and negative obstacles of the off-road. On the other hand, [PERSON] et al. [22] project the LiDAR point cloud into a two-dimensional image plane and generate a histogram from it. Water, positive obstacles, and drivable areas in off-road scenes are detected from the histogram. Finally, the result is back-projected into the LiDAR coordinate system. Although the feature engineering method has achieved good results in specific off-road scenes, it has significant constraints, merely possessing the capacity to classify a few specific elements in the scene, and failing to adapt to various off-road scenes.  
[PERSON] et al. [23] projected the original point cloud onto the image plane through a bird's-eye view, and then used the GPS information of the moving vehicle to obtain the driving trajectory. On the projected image, the region growth algorithm is performed on the driving trajectory to automatically generate the label of the drivable region, and combined with a small amount of manually labeled data as the training dataset, a good segmentation result is finally achieved. Meanwhile, the workload of manual annotation is greatly reduced. [PERSON] et al. [24] use an existing CNN framework to pre-train on a dataset of urban structured road scenes. They then use a small dataset of off-road scenes to re-determine the segmentation classes for transfer learning. While achieving good results, it can effectively reduce the labeling of off-road scene LiDAR point cloud data.  
To sum up, the main problem in designing semantic segmentation algorithms for off-road scenes is the lack of datasets. Existing algorithms mainly use geometric features or combine specific algorithms with a small amount of data to perform semantic segmentation. However, lower accuracy is still a big problem. Therefore, on one hand, the research should focus on how to obtain a large amount of high-quality data. Relying on computer simulation technology, typical off-road scenes can be built to obtain a large number of accurately labeled datasets. On the other hand, more targeted algorithms should be designed according to the characteristics of off-road scenes. The above two aspects have important engineering value and academic significance for improving the semantic segmentation accuracy of off-road scenes.