## 1 Introduction
Though the initial estimated pose can be optimized in the second stage (back-end stage), the accumulated errors of DA may give rise to big problems for the results of SLAM, and the accuracy and the stability will be badly affected, sometimes even failing to obtain the results. This is often the case in low-texture environments for a 2D scanner, for example, the mobile robot moves along the long corridor in an indoor environment, and due to the measuring limitation of the scanner, there may be no obvious changes between two consecutive scanning outputs, making the DA process difficult. Additionally, when there are moving objects in a dynamic environment, the scanned points located on the dynamic target are taken as noise data and badly impact the DA process. Both cases bring great challenges for the DA task. If a large error is generated in the early stage of the DA process, the following steps, including the LCD and the back-end optimization, will be affected. The closed-loop detection is actually a scan-to-map matching process, and can help to limit the error accumulation generated in the DA process. However, it needs a good initial pose estimate. Unfortunately, a good initial pose is hard to obtain in a low-texture or dynamic environment. From this point of view, the DA and LCD are still an open challenging problem. To solve the aforementioned problems, some approaches based on sensor fusion are put forward for data association. With the particle filter (PF) framework [18; 19], the GMapping approach [10] introduces the adaptive resampling technique, calculating the particle distribution not only relying on the current observation of the LiDAR, but also the odometry information; the uncertainty of the robot pose is reduced and the particle dissipation problem is minimized. But no LCD is included in their method. The IMU sensor in the Hector SLAM method [7] can provide an initial pose estimation to solve the least squares problems. By making use of the extended Kalman filter (EKF) framework, Hector SLAM uses the rigid transformation from the current point cloud to the existing map as input to update the pose. [PERSON] [20] integrated a 6 DOF IMU with a 2D LiDAR to explore the rescue scene and to find a safe route. [PERSON] [21] uses IMU data to project the single-line LiDAR data to the horizontal plane to achieve the purpose of drawing the tree distribution map in the forest. However, in this method, IMU is only used to assist in mapping, and is not involved in positioning, so is not suitable for low-texture environments. [PERSON] [22] and others connect a single-line LiDAR with an inertial measurement unit. The system can provide real-time, complex 3D indoor environment tracking. However, the implementation of the method is based on the assumption that all vertical walls are orthogonal to each other. All the above methods are based on the assumption of a static environment, however, this comes into conflict with the real applications because there are always other moving objects around, such as people moving around, or even other moving robots. Dynamic environments will inevitably affect the reliability of data association, which results in inaccurate maps. Currently, there are two ways to deal with the dynamic environment. One way is integrating the moving object detecting and tracking with the traditional SLAM method, finally building a dynamic map and static map to provide comprehensive information of the whole environment. Another way is to detect and eliminate the observed information caused by dynamic objects. [PERSON] [23] decomposes the task into the SLAM problem in the static environment and the navigation problem of the dynamic environment in the known map. [PERSON] [24] proposes a mapping strategy for occupancy grid map in the dynamic environment. [PERSON] [25] and others identify the moving human body in the process of positioning, thus improving the robustness of pose estimation.