# LIDAR Point Cloud Registration for Sensing and Reconstruction of Unstructured Terrain
## 1 Introduction  
In recent years, autonomous navigation technology has been gradually deployed on outdoor vehicles operated in unstructured terrain. Different from structured terrain, unstructured terrain has an uneven surface and complex conditions, which brings a great challenge to the safety and stability of autonomous vehicles. Therefore, various sensors such as visual image sensors [1,2] and LIDAR [3,4] have been deployed on autonomous vehicles for sensing unstructured terrain to achieve safe operation. Although the visual image sensors only produce 2D images, sufficient terrain information can be obtained by using some approaches, such as calculating a height map from an image to obtain 3D information [5] or using the super-resolution reconstruction method to obtain a spatial resolution-enhanced image [6]. Compared with visual image sensors, LIDAR has the advantage of being free from the effect of light and weather and can obtain the three-dimensional terrain information conveniently. Therefore, it has been more and more widely used in autonomous vehicles for navigation purpose. When LIDAR is used to scan unstructured terrain, the obtained point cloud is uneven in density and noisy due to the influence of the terrain, the land cover [7] and the laser scanning mechanism [8]. Moreover, the point cloud is incomplete because of the self-occlusion of objects and the installing angle of LIDAR. Without sufficient terrain information, the autonomous vehicles will fall into bad performance and even roll over. Therefore, in order to provide sufficient terrain information to autonomous vehicles, it is necessary to register multiple scanning point clouds to obtainan accurate terrain point cloud map. Traditionally, dead reckoning is combined with other sensors for registration [9; 10]. Although this method is fast, it can easily lead to a large error in the registration result due to the imprecision of sensor, the sampling difference and the tire slip. Therefore, we propose a new two-step registration method to conduct accurate registration after this initial registration. The ICP (Iterative Closest Point) algorithm [11] has been widely used for accurate registration; however, it has the disadvantages of low efficiency and of easily falling into local minima. Therefore, many improved ICP algorithms have been proposed to optimize a part of the iterative process and then to improve the traditional ICP algorithm, including nearest point search [12; 13; 14; 15], feature description [16; 17], correspondence building [18; 19], and convergence judgment [20]. Among them, feature description can effectively conduct the registration of point clouds with uneven-density and high-noise and therefore improve the performance of the traditional ICP algorithm. In recent years, many scholars have studied the feature description of point cloud. [PERSON] et al. [21] conducted registration by extracting the line and plane features of the point cloud, which can reduce the influence of noise, but requires that the point cloud has obvious line and plane features. [PERSON] et al. [22] and [PERSON] et al. [23] proposed new feature descriptors with a certain anti-noise ability, but the calculation was complex and time-consuming. [PERSON] et al. [24] extracted key points based on curvature features without full consideration of the neighborhood of the point, which bring about many feature-similar points when used for the registration of complicated point clouds. The limitations of these methods make them difficult to be used for the fast point cloud reconstruction of unstructured terrains, thus a new feature descriptor is required. In this paper, we propose a two-step registration method consisting of initial registration and accurate registration. After initial registration, accurate registration is conducted by using an improved ICP based on the neighborhood normal vector, point cloud density and curvature to extract key points and build the new feature descriptor.