# Robust Localization of the Mobile Robot Driven by Lidar Measurement and Matching for Ongoing Scene
## 1 Introduction  
The localization with robustness of mobile robot is a characteristic that describes the ability to maintain its original or expected state. With the continuous improvement of the construction industry, the mobile robot technology is being widely used in the construction industry [1]. More kinds of robots, such as the masonry robot and 3D printing robot, are endowed with mobility to accomplish the structure manufacturing, handling and assembly tasks [2; 3; 4]. The localization issue becomes more critical for them. The construction scene is an empty and unstructured environment, the measured uncertainty and noise of sensors cannot be ignored. Therefore, focusing on the robust localization of mobile robot in construction scene can improve the monitoring efficiency of construction and bring a great potential for automation and intelligent prefabrication [5; 6]. For the localization of mobile robot in the empty scenes, the artificial landmarks are usually set to achieve it by scholars. A global localization system that based on artificial beacons was developed by visual sensors for indoor environments [7]. For improving the adaptation of sensors and computing power, an artificial beacon was designed to calculate poses of mobile robot by trigonometric functions [8]. Also, the accuracy of localization for mobile robot was affected by numbers of sensors and errors of their placements. A new three object triangulation algorithm that solved the limitation of the specific beacon ordering, beacon placement, and the working range of the triangle was proposed, which showed a fast calculation efficiency [9]. Similarly, an active beacon angle measurement system that was proposed to realize the robust localization of mobile robot by measuring angles of identified beacons [10]. An error model for a robot's pose based on triangulation from three landmarks was proposed in [11], and the uncertainty of landmark's location was represented by Gaussian distribution to calculate therobot pose error. In addition, the photoelectric artificial landmarks were pre-marked in a large-scale space, and a multi angle intersection algorithm was established to show an accurate measurement in three-dimensional position and orientation of mobile robot [12]. From the above works, the localization of indoor scenes for mobile robot can be achieved by the triangulation or trilateral measurement methods with artificial landmarks. However, the effects of sensor noise and measured errors of landmarks on the mobile robot's localization are ignored, so the robust localization can be improved further by filtering or optimization. The filtering method is a key tool to improve the localization accuracy of mobile robot. It can effectively solve the ambiguity and kidnapping problems in the process of localization. Among them, the commonly filtering methods that used in robot localization are Particle Filtering (PF), Kalman Filtering (KF), and their variants. For the consistency of vehicle localization, a novel global localization system based on PF was presented in [13; 14], which improved the convergence relationship and accelerated to the convergence process of particles. A triangulation algorithm that based on combination of Unbiased Finite Impulse Response (UFIR) and KF was introduced to improve localization accuracy of mobile robot [15]. [PERSON] et al. [16] fused external measurement sensor and odometer information through Extended Kalman Filter (EKF), and adopted triangulation method to improve localization accuracy of mobile robot. Also, a localization scheme based on Invariant Extended Kalman Filter (IEKF) for mobile robot was developed by the Kinect point cloud [17]. For further improvement of localization accuracy and stability of mobile robot, the measured noise of Lidar to artificial landmarks was considered [15; 18], and the localization method based on an Adaptive Unscented Kalman Filter (AUKF) was used to improve the localization of mobile robot [18].