# Robust Localization of the Mobile Robot Driven by Lidar Measurement and Matching for Ongoing Scene
## 4 Experiments  
In Section 4, the localization of mobile robot in the ongoing scene is analyzed in detail. Also, the robustness of localization method that based on a mixture of localization by artificial landmarks and matching based on all features of the scene or generated features will be verified through the experiments. Under the 1:1 scale of construction drawings to the construction site, the paper has set nine key working locations for mobile robot to move with clockwise. Then, the artificial landmarks of the ongoing scene and the generated features are extracted to estimate the locations of mobile robot. As shown in Figure 6, the red building indicates the generated features in the ongoing scene, the green indicates ongoing buildings, and the gray indicates unbuilt buildings. According to the working process of mobile robot, the scene is divided into three working conditions. Working condition 1: Four artificial landmarks are evenly distributed in the ongoing scene, and there are no geometric features of generated buildings. The mobile robot can observe all artificial landmarks through Lidar, just as the initial working location 1, the initial location of mobile robot is realized by extracting the intensity information of reflectors. In the scene of Figure 6, the 4 artificial reflectors that recognized by Lidar are represented in black. Working condition 2: Four artificial landmarks are evenly distributed in the ongoing scene, and the geometric features of buildings are generated gradually. At this time, the mobile robot can observe part of artificial landmarks through Lidar from working locations 2 to 5. As a result, the mobile robot can achieve its localization by both extracting the features of reflectors and generated buildings. From Figure 6, the black reflectors represent artificial landmarks that recognized by Lidar, and the red reflectors represent unidentified ones. Working condition 3: Four artificial landmarks are evenly distributed in the ongoing scene, and there are gradually geometric features of the generated buildings. The Lidar mounted on the mobile robot cannot observe the artificial landmarks, just as the working location 5. All artificial reflectors placed in the scene are blocked by the generated buildings, which prevents mobile robot from extracting the features of reflectors. Therefore, the localization of mobile robot is only obtained by the matching of generated buildings in the ongoing scene. In order to verify a better robust localization of mobile robot in the ongoing scene, this paper proves the superiority of the localization method proposed in the paper from the workflow of (a) to (b) (Figure 7). Firstly, a 1:1 construction scale based on construction drawings is set, and the length and width of the construction site is 1666 mm \(\times\) 1100 mm  
of mobile robot, and then six working locations are also selected in the paper according to the clockwise construction sequence of the buildings. Finally, the paper selects the white plate as the ongoing features of the scene, and adopts a mobile robot equipped with Lidar to test the localization algorithm. The specific construction process is shown in Figure 8a-l. According to the process of construction that shown in Figure 8, the localization in 6 designated positions of mobile robot is tested. The specific locations for mobile robot are (a or b), (c or d), (e or f), (g or h), (i or j), and (k or l). In addition, the characteristics of the scene are different under the working conditions. Under the initial location b where the generated building is ongoing, the paper can extract the intensity information of reflectors to determine the initial position of mobile robot. Under the locations of c, e, g, and i for working condition 2 and 3, the paper adopts adaptive weighting hybrid localization method based on artificial landmarks and ongoing features to perform the robust localization.