## 1 Introduction  
Simultaneous localization and mapping (SLAM) has been rapidly developing, and it is being widely used in the simultaneous estimation of the pose of a moving platform and the building of a map of the surrounding environment [1; 2; 3]. Consequently, SLAM has become a key prerequisite for unmanned systems to operate autonomously in unknown environments. In known environments [4; 5], the localization part of SLAM can provide a six-degree-of-freedom sensor pose estimation, and thus plays an essential role in positioning and navigation applications such as navigation and planetary exploration missions, especially in environments where the global navigation satellite system (GNSS) is unavailable. Moreover, recent developments in SLAM have been driven by advances in sensor technology (e.g., RGB and depth (RGB-D) cameras and mobile Light Detection and Ranging (LiDAR scanners) [6; 7; 8], as well as by the potential large market opportunities in emerging applications such as mobile augmented reality, self-driving vehicles, unmanned aerial vehicles, and home robots. SLAM has been extensively researched over the past 30 years by the robotics and computer vision community, and thus a comprehensive review on this topic is out of the scope of this paper; instead, we focus on the most relevant literature applicable to this research. First, excellently robust and quick feature extraction techniques such as SIFT [9], SURF [10], BRISK [11], and ORB [12] that are to be used in the SLAM framework are developed. PTAM [13] incorporated parallel computing in small-scale Augmented Reality (AR) tracking and mapping to improve efficiency, and was the first multithreaded approach to separate localization and mapping into two different threads. To further improve the accuracy and robustness, many SLAM algorithms utilize loop closure detection using the bag-of-words model [14]. Once a loop closure is successfully identified, the cumulative tracking error can be significantly reduced. In recent years, in indoor environments without GPS coverage, visual SLAM has become an important method for self-localization and environmental sensing [15]. Visual SLAM uses a monocular camera [16; 17], stereo cameras [18; 19], and RGB-D cameras [20] to construct various forms of SLAM systems.  
With people increasingly spending most time indoors, the necessity for indoor positioning and mapping of large scenes has been increasing. However, without prior data, the cumulative error in both mapping and camera pose increases with the tracking distance. This leads to low mapping precision, and thus new technologies that rely on high-precision indoor positioning and mapping, such as mobile augmented reality, self-driving vehicles, path planning, and obstacle avoidance in service robots, are almost rendered unusable. To address this problem, this study is focused on eliminating the cumulative errors by fully utilizing the information acquired by the RGB and depth (RGB-D) sensors by incorporating high-precision prior LiDAR data. In addition, a multithreaded segment-based map double window optimization strategy is adopted to construct a consistent and accurate map in real-time and deep learning is utilized in indoor complex environments. Therefore, this research finally improves the robustness and flexibility of the RGB-D SLAM algorithm.