# MIM_SLAM: A Multi-Level ICP Matching Method for Mobile Robot in Large-Scale and Sparse Scenes
## 1 Introduction  
In recent years, the simultaneous localization and mapping (SLAM) based on a 3D LIDAR sensor have become an important topic in robotics due to the rise of autonomous driving technology. Meanwhile, the building of a map has become the basis for an autonomous mobile robot to complete tasks such as inspection and autonomous navigation [1] in some harsh environments. The research on SLAM can be traced back to [PERSON] et al. [2] of Stanford University in the 1980s. They published a seminal paper on SLAM, and later generations have done considerable work on it. Among them, the algorithms such as Extended Kalman Filters [3], Extended Information Filters [4], and Rao-Blackwellized Particle Filters [5] based on a filtering idea all adopt the Bayesian state estimation theory to estimate the posterior probability of the system's state. However, the essence of this kind of algorithm is only optimizing the local state of the system. As the distance traveled by the robot increases, it is difficult to establish a high consistency map. Another kind of algorithm is the graph optimization that was first proposed by [PERSON] and [PERSON] [6]. The essence of this algorithm is maintaining all the observation and space constraints between observations, then using the maximum likelihood method to estimate the pose of the robot. Better mapping results can be obtained in large-scale scenes using the global optimization method. Although the SLAM problem has been thoroughly researched, there are still many problems to be solved, including the fact that computational complexity will increase from two-dimensions to three-dimensions, and how to ensure a consistent map with the increase of the size of mapping and reliable data association with less information in sparse environments. Combining these issues, [PERSON] [7] utilizes a rotating 2D LIDAR sensor to achieve high-precision map construction in a wide mine, but it cannot be applied online. [PERSON] [8] proposes a data association method in sparse environments, but it is only suitable for solving the localization problem under known maps. [PERSON] [9] proposes a sparse local submap joining filter (SLSJF) for map-building in large-scale environments. However, this depends on environmental features. [PERSON] [10] and [PERSON] [11] propose a real-time SLAM framework, and the experiments in some scenarios have achieved good results, but the results of the mapping are not tested for large-scale and sparse environments. [PERSON] [12] estimates the pose of the robot by extracting the feature points in the scene, but it is difficult to establish a closed-loop map as the map size expands. [PERSON] [13] proposes a multi-layer matching SLAM in large-scale and sparse environments, but it only solves the data association in a 2D scene. The algorithm will be ineffective when the robot drives on a non-flat road. Therefore, this paper is dedicated to solving the following two problems:  
In sparse environments, due to the scarce environmental information perceived by a 3D LIDAR sensor and the non-flat road, data association is easy to fall into a local minimum. [PERSON] [14] proposes a LiDAR SLAM method in natural terrains which fuses multiple sensors including two 3D LiDAR. Therefore, the robustness of data associated with a lower accumulated error should be strengthened. In large-scale environments, it is difficult to obtain a reasonable pose estimation when the robot returns to a region it has previously explored and then generates an inconsistent map. [PERSON] [15] addresses the laser-based loop closure problem by fusing visual information. [PERSON] [16] can achieve real-time mapping in indoor scenes, and it may fail in the closing loop due to the incremental computation in large-scale outdoor scenes. How to reduce the computational complexity of graph optimization is also a problem that needs to be improved. To solve these problems, we propose a real-time incremental SLAM algorithm called the MIM_SLAM based on a multi-level ICP matching method. This paper is organized as follows.