## 2 Related Works
Sarvrood, Y.B combines stereo visual odometry, LiDAR odometry, and reduced inertial measurement unit (IMU) to construct a reliable and relatively accurate odometry-only system [37]. Although both the above-mentioned methods function well, because the LiDAR data is usually obtained in real time without preprocessing, large cumulative errors may still exist after long-distance tracking. For postprocessed fusion, the LiDAR data can be utilized to obtain a priori map to enhance visual localization; however, little research exists on this technique. Fast and accurate feature landmark-based camera parameter estimation is achieved by adopting tentative camera parameter estimation and considering the local 3-D structure of a landscape using the dense depth information obtained by a laser range sensor [38]; however, only positioning, and not map construction, is obtained by this method. A mobile platform tracks the pose of a monocular camera with respect to a given 3-D LiDAR map, achieving excellent results under changing photometric appearance of the environment [39], because it relies only on matching geometry; however, the initialization time is long. The normalized mutual information between real camera measurements and synthetic views is utilized to the maximum extent, based on a 3D prior ground-map generated by a survey vehicle equipped with 3D LiDAR scanners to localize a single monocular camera [40]. This method does not fully utilize the scene information of the LiDAR data map, but utilizes only that of the ground part; therefore, it has reduced robustness in special environments. A UAV platform is utilized to obtain prior LiDAR data and a stereo camera is used to map large-scale outdoor scenes without automatic initialization and real-time performance [41]. To address the real-time map optimization problem, a multithreaded segment-based map double window optimization strategy is adopted in the method proposed in this paper, to ensure consistency and accuracy of map construction, as well as high real-time performance. The algorithm used for initial registration in the above-mentioned methods is time consuming or requires manual operation, which greatly limits its application, whereas the proposed method uses the deep learning neural network to perform automatic initialization and motion recovery, which improves the flexibility and robustness of the algorithm in difficult scenarios. In recent years, deep learning has shown great advancements and very high performance in various computer research fields because of their similar cognitive characteristics as humans. The combination of deep learning and geometric SLAM has been receiving increasing attention from researchers. Deep learning is used for closed-loop detection [42; 43], and the results indicate that it is more suitable than conventional methods based on visual low-level features. Researches have been focused not only on addressing the conventional SLAM problems, geometric mapping, and localization, but also on semantic SLAM, which has gradually become a trend in the field of SLAM. The object detection module, realized by the deep-learning method and localization module, is integrated seamlessly into the RGB-D SLAM framework to build semantic maps with object-level entities [44]. A robust semantic visual SLAM named DS-SLAM [45], aimed at dynamic environments, is proposed. The absolute trajectory accuracy of this method can be improved by one order of magnitude compared with that of the ORB-SLAM2. A review of literature shows that visual and RGB-D SLAM generally use the natural landscape to construct a 3-D environment and simultaneously estimate the camera pose; however, under highly variable illumination conditions and low-textured environments, without priori data, errors accumulate in long distance tracking, causing tracking failures, which may result in kidnapped-robot problem. This limitation often hinders the practicality of SLAM technology in real-life applications.