# A Distributed Indoor Mapping Method Based on Control-Network-Aided SLAM: Scheme and Analysis
## 1 Introduction  
With the rapid development of the geographic information industry, demand for location-based services (LBS) is growing rapidly. Fast-updated online maps (e.g., Google Maps, Baidu Maps, GaoDe Maps) have made outdoor LBS, such as positioning, navigation, and first-aid applications, more convenient and faster [1]. However, for indoor environments where people spend most of their time [2], the lack of indoor environment maps is very serious. Even Google Indoor Maps, which may represent the industry state of the art [3], covers only a small part of indoor environments. The lack of indoor maps severely limits the development of indoor LBS, so an efficient, low-cost, high-precision indoor mapping technology is urgently needed to solve this problem [4]. After decades of development, mobile mapping has been proven to be an effective outdoor mapping method. Mobile mapping systems can complete outdoor environment mapping quickly and accurately with the assistance of GNSS (Global Navigation Satellite System), INS (Inertial Navigation System), or other sensors. However, in indoor environments where GNSS is denied due to satellite signal occlusion, it is more difficult to obtain the position and orientation for mapping [4; 5; 6; 7]. The traditional mapping methods, such as utilizing a stationary total station to measure the feature points and get accurate indoor sparse mapping results, are time-consuming and cannot meet the need for fast updating of indoor maps. Another conventional method is obtaining indoor maps by utilizing static laser scanning, but its cost and efficiency cannot meet the requirements of low cost and fast updating of indoor mapping [4]. In recent years, Simultaneous Localization and Mapping (SLAM) has been proven to be an efficient and promising technology for solving the indoor mapping problem. SLAM can construct a consistent map by localizing a robot relative to the map at the same time, which has been investigated in the community of robotics for more than two decades. Light Detection and Ranging (LiDAR)-based SLAM is one of the most stable and popular methods for mapping, positioning, and navigation in indoor environments because LiDAR can provide high-precision range information over long distances compared with other sensors, which can improve the positioning and mapping precision [8]. There have been many studies on LiDAR SLAM utilizing 2D or 3D LiDAR, such as Hector SLAM [9], Gmapping [10], Karto SLAM [11], and Cartographer [12]. The framework of SLAM can be divided into filter-based and graph-based types. The filter-based SLAM frameworks include Extended Kalman Filter (EKF) SLAM [13; 14; 15] and Particle Filter (PF) SLAM [16]. Graph-based SLAM has gradually become the mainstream SLAM framework by modeling the SLAM problem as a constrained sparse graph with a front end and back end. It is a state-of-the-art technique with respect to calculation speed, location accuracy, and mapping accuracy [17]. Although SLAM technology has been successfully applied in the field of indoor mapping to produce high-precision indoor maps, the positioning and mapping accuracy will gradually degrade with the expansion of the mapping area [18], and it is difficult for SLAM to correctly detect loop closure to eliminate the cumulative error in large-scale indoor environments [19]. Moreover, the computational pressure will increase rapidly with the expansion of the mapping area, especially for filter-based SLAM [20; 21]. Distributed indoor mapping methods based on multirobot cooperation SLAM systems can decompose a large-scale mapping problem into multiple small-area mapping problems, which can greatly improve the efficiency and reduce computational pressure. The most important challenge in cooperation SLAM is how to stitch the maps from multiple tasks or multiple robots together by determining the relative positioning between multiple mapping results [22; 23]; because SLAM is a relative positioning process, the coordinate system of the mapping result is usually determined by the first frame of data.