## 2 Related Works  
With lightweight and low-cost RGB-D sensors, such as Microsoft Kinetic, becoming increasingly popular, RGB-D SLAM has attracted significant attention from researchers. For example, a pose graph optimization framework is proposed to realize real-time and highly accurate SLAM [21; 22]. In addition, the conventional bundle adjustment framework is extended to incorporate the RGB-D data with inertial measurement unit to optimize mapping and pose estimation in an integrated manner [23; 24; 25]. The inductively coupled plasma (ICP) method is used to estimate the motion between continuous frames containing dense point cloud, and is effectively applied to RGB-D SLAM [26; 27]. it is clear that ORB SLAM2 [28] is one of the best SLAM methods which includes most state-of-the art techniques developed in recent years, and supports single, stereo, and RGB-D cameras. [PERSON], [PERSON] presents a framework for incremental 3D cuboid modeling combined with RGB-D SLAM [29]. To improve the positioning and mapping accuracy and the robustness of the system in various scenarios, various RGB-D SLAM methods are proposed correspondingly. To eliminate the influence of dynamic objects, [PERSON], [PERSON] presents a dynamic scene SLAM [30] which improves the dynamic object detection using multiview geometry and deep learning, along with background inpainting. Because the line features are more stable than point features, many researchers use line features to estimate camera poses. Sun, Q. matches the plane features between two successive RGB-D scans and the scans are used to estimate the camera pose [31] only if they can provide sufficient constraints. [PERSON], Y provides Canny-VO [32], which efficiently tracks all Canny edge features extracted from the images, to estimate the camera pose. A real-time RGB-D SLAM with points and lines [33] is proposed. To reduce cumulative errors, a vertex-to-edge weighted closed-form algorithm [34] is used to reduce the camera drift error for dense RGB-D SLAM, which results in significantly low trajectory error than several state-of-the-art methods; in addition, this research has received great attention for the improving real-time performance. The back-end optimization problem is decoupled into linear components (feature position) and nonlinear components (camera poses) [35], and as a result complexity is significantly reduced without compromising accuracy; in addition, this algorithm can achieve globally consistent pose estimation in real-time via CPU computing. Thus, it is clear that, after various improvements, all of the above-mentioned RGB-D SLAM algorithms generally achieve better results in indoor environments involving small scenes. In environments involving large scenes, if prior data is not obtained, large cumulative errors are often caused by complex environments, such as by changes in illumination, motion blur, and feature mismatches. Although the global drift error can be reduced by loop closure, no loop is available in many scenarios, and computation of large loops is time-consuming. To address this problem, we intend to utilize the priori LiDAR data as the control information to guide the RGB-D SLAM operation in large indoor environments; the priori data can neglect the cumulative error problem irrespective of the tracking distance. Recently, multisource data fusion SLAM has been developed for complex indoor environments, which combines the LiDAR data and vision data; this technology mainly involves real-time fusion and postprocessing fusion. A lot of research has been conducted on real-time data acquisition fusion, by which the data acquired by the laser sensor and the camera are directly fused online for positioning and mapping. A real-time method is presented, namely V-LOAM [36], which combines visual odometry in high frequency and LiDAR odometry in low frequency to provide localization and mapping without global map optimization.