# Robust Localization of the Mobile Robot Driven by Lidar Measurement and Matching for Ongoing Scene
## 1 Introduction
The above researches have improved the localization accuracy or computational efficiency of mobile robot through the filter framework. However, the fusion mechanism of different sensors is complex and has the disadvantages of unsynchronization and uncertainty. In addition, the localization methods that mentioned above are not directly applied well in the scene, where the recognizable landmarks are blocked by the new ongoing features. So, the scan matching technology can be adopted as an optimization method to improve robust localization of mobile robot. Simultaneous localization and mapping is called SLAM for short, that is, in an unknown environment, mobile robot can use sensors such as Lidar or camera to perceive external information to achieve localization and mapping. With the rapid development of intelligent robot technology, SLAM technology has been widely used in robotics and unmanned driving fields [19; 20; 21]. It can greatly improve the localization performance of mobile robot in the construction scene. One of the key tools for robust localization of mobile robot is the scan matching. A typical method for scan matching is the Iterative Closest Point (ICP) proposed by [PERSON] etc. [22]. It was used to register two laser scans to determine the relative transformation with the low complexity. However, the ICP method had also the disadvantages. It was given the accurate initial values to avoid falling into local optimal values [22; 23]. For the robust localization of mobile robot, a novel robust localization method for mobile robot was proposed in [24], and the particle loss was also solved based on the particle swarm optimization strategy. A scan matching algorithm based on the combination of 2D laser and odometer was adopted to reduce the localization error of mobile robot in an indoor environment [25]. Also, an Anchor Point Relation (APR) pattern matching algorithm was adopted to realize the real-time search between the most matching lasers and the known reference lasers, which improved the localization robustness of mobile robot [26]. To reduce the influence of noise, the localization accuracy of mobile robot was improved through the weighted scanning matching method [27]. A matrix matching method [28] and the method that based on the Radial distance and Incident Angle (RIA) [29] were adopted, which estimated efficiently the transformation of two locations. In addition, some other scan matching methods were also studied. A polar coordinate scan matching method (PSM) based on point-to-point was proposed to achieve a rapid data association, and the matching results demonstrated high accuracy of localization for mobile robot [30]. Normal Distributions Transform (NDT) [31] and a grid-based 2D map matching with multiple Normal Distributions (NDs) were presented, to improve the global matching efficiency and robustness of localization [32]. Without the need of landmarks, an optimization method that based online segment matching was proposed to reduce the matching error of two positions for mobile robot [33]. Also, Monte Carlo, Kullback-Leibler Distance (KLD) sampling was adopted to estimate the pose of mobile robot [34]. The traditional localization method of mobile robot is to use laser scanners, vision systems, sonar and other sensors to identify the artificial or natural features of the scene to achieve the localization. However, due to the absence of natural landmarks or the difficulty in extracting features, the localization method that mentioned above cannot be well applied into the construction scenes. Therefore, in this paper, we propose a novel localization algorithm for mobile robot by combining features from original scene and ongoing buildings. Without considering the ground inclination, the artificial landmarks are set in the scene firstly. Then, according to the sequence of construction processing, the features of artificial landmarks and ongoing buildings at different locations are extracted.