Research On Visual Analysis Method For Surface Terrain Of Small Body

In recent years,deep space exploration has become a hotspot of space scientific research activities,including Russia,the United States,Japan and other countries and regions are actively engaged in deep space exploration activities.At the same time,due to the large investment of capital,long period of exploration and high level of science and technology in deep space exploration,people are pursuing higher and higher returns.Previously,the way of sending the probe to the target celestial body and returning data such as images has been unable to meet people's needs.It is hoped that the probe will be able to land on the surface of the celestial body,obtain rock and soil samples and bring them back to Earth for research.This requires that the probe can achieve a soft landing on the surface of the celestial body.The first step of landing is to select the landing site based on the observation of the target celestial body.The choice of landing site needs to acquire the surface terrain of celestial body and the motion law of the celestial body,so as to determine the suitable landing area.And undoubtedly,rock is an important measurement point.Accurate identification of rock's location and size has great reference value for landing site selection.Aiming at the small body exploration activities,the purpose of this paper is to automatically analyse areas on the surface of small body suitable for landing.On the premise of obtaining the high resolution three-dimensional point cloud model of the small body,terrain characteristic description and terrain classification of the surface terrain of the small body are carried out to identify the areas suitable for landing on the topographic requirements.Then rock detection is performed on the identified areas that may be suitable for landing.These together provide a reference for landing site selection in terms of terrain and obstacles.The main research work of this paper are as follows:(1)Firstly,we need to complete the transformation from three-dimensional point cloud model to elevation map,and take the idea of fitting spheroid or plane to calculate the distance between three-dimensional point and datum plane as elevation value.Ensure enough details to prepare data for later research.(2)Several local areas are divided on the elevation map,and terrain characteristic description is carried out by using roughness and slope.Convolutional neural network training classifier is used to classify the terrain artificially divided into several classes,and the local areas with relatively flat terrain are identified as candidate landing areas.(3)On the basis of the before work,aiming at the identified local elevation map with flat terrain,the threshold is used to intercept the points at the topographic salient,and clustering algorithm is used to determine the number and location of rocks,and the approximate size of rocks is obtained by adjusting the range of rocks with the local slope map.Finally,the location and approximate range of rocks are shown on the elevation map,and the rock detection of the elevation map of the candidate landing area is completed.Finally,experiments are carried out on the local terrain generated by computer simulation and the 3-D point cloud model data reconstructed from the real scene.The experimental results show that the proposed method can effectively transform the three-dimensional point cloud model into elevation map,accurately identify the relatively flat terrain area,and accurately detect rocks in the flat area.