Laser pulse launching by LiDAR sensor has strong penetrability and sun shine as well as extreme weather has little influence on it, because of which, it can genuine acquire the three-dimensional information on the ground. It is an ideal data source for crop canopy structure information extraction. In this paper, based on the airborne laser radar data, as the goal was to extract the corresponding feature ground point. Using the TerraSolid software to classify the whole points, the points were divided into different classification, such as ground, vegetation, wire power and wire line. Meanwhile, RANdom SAmple Consensus (RANSAC) was applied to fit the plane segmentation model based on the Point cloud library (PCL), which optimized the obstacles extraction results. The TerraSolid software classification results, PCL plane segmentation fitting results with initial classification of point cloud for confusion matrix were obtained, respectively. Confusion matrix for precision evaluation was concluded. Correlation analysis was carried out on two kinds of precision evaluation. Research results show that it is better for TerraSolid to deal with block rather than the whole point cloud data. The results of TerraSolid and PCL are similar for the same point cloud. Its operation is fast and efficient but poor for the visibility. We can combine both advantages in extracting obstacles. This study basically achieved the anticipated goal of flying obstacles extraction, to provide security for the unmanned aerial vehicle (UAV) flight and help with the flight path planning.