目的 目前已有的单目视觉SLAM（simultaneous localization and mapping）系统每次开始运行时都将初始帧而不是绝对位置设置为参考帧，不能在一个固定的坐标系中获得位姿，导致无法重用已有的建图信息，而且在复杂场景中相机容易跟踪失败，需要当前帧与已有的关键帧非常相似时才能重定位并继续建图。针对这个问题，提出一种具有重新初始化、地图重用与地图恢复能力的视觉SLAM系统。方法 首先，加载先验地图，通过ORB（oriented brief）特征匹配SLAM系统当前帧与先验地图关键帧，并结合重定位方法完成SLAM系统的初始化。接着，为了避免丢失地图，建立一种应对SLAM系统跟踪失败的地图保存机制，保存跟踪成功地图，并提出一种自适应快速重新初始化算法，引入灭点检测，自动选择最佳重新初始化策略，保证SLAM系统继续跟踪与建图，建立的地图称为恢复地图。最后，对于跟踪成功地图与恢复地图，采用改进的回环方法获得它们之间的转换关系，并提出一种地图恢复法，减少跟踪成功地图与恢复地图尺度不一带来的误差，确保得到的全局一致地图更加准确。结果 在经过加噪处理的KITTI数据集上进行地图恢复融合的测试，实验结果表明，在KITTI00、KITTI02、KITTI05数据集下，本文提出的SLAM系统比ORB-SLAM2系统分别可以多获得39.25%、47.75%、32.46%的地图信息。在EuRoC数据集上的运行结果表明，本文提出的单目视觉SLAM系统不仅在建图精度方面与ORB-SLAM2效果相当，还在跟踪稳定性方面有显著提升。结论 本文提出的SLAM系统可以在跟踪失败的情况下有效恢复地图；此外，还可以高效重用SLAM系统已有的建图结果，固定SLAM地图坐标系，提升SLAM系统运行稳定性。

Objective Simultaneous localization and mapping (SLAM) has been an important research topic in the last two decades in the computer vision and robotics communities. SLAM aims to build a map of an unknown environment and localize the sensor in the map with real-time operation. Among the existing research results, many state-of-the-art monocular visual SLAM schemes are used. In most existing traditional approaches, the system would set the initial frame instead of the absolute position as the reference frame when it begins running, and it could not acquire the pose in a fixed coordinate system, resulting in the failure of re-using the existing mapping information. Additionally, the present monocular visual SLAM schemes are prone to tracking failure in case of complicated scenes such as cluttered background, motion blur, and defocus, and the existing schemes process it by place recognition, which is a key module of a SLAM system to close loops and relocalize the camera. However, the main drawback of the solution is that it requires the current frame to be highly similar to the existing key frame to ensure the success of relocalization, generally causing inconvenience in certain real application scenarios. For example, an advanced mobile robot is required to return to the place where the location information is lost after tracking failure so that the system could continue tracking and mapping. However, the map information could not be recovered from the failure of system tracking to the success of relocalization, thereby resulting in considerable map information loss. Therefore, to address the limitations, such as loss of map information and requirement for high similarity between a current frame and an existing frame while relocalizing, we propose a prior-information-based visual SLAM system with the capacity of re-initialization, map re-using, and map recovery in complex scenes. Method In this study, the first step is loading the prior map, matching the current frame with the key frame of the prior map in the SLAM system by applying ORB features to obtain matched frames, and finishing the initialization of SLAM system in combination of relocalization, which is conducive to ensuring the consistency of the SLAM coordinate system. Second, a map-saving mechanism is built to save the map of successful tracking before tracing failure occurs to avoid losing the map information. The traditional SLAM always conducts relocalization after tracking failure, and the map could not be generated before the relocalization succeeds accordingly, further resulting in the loss of map information. Considering the aforementioned issues, we address tracking failure by re-initializing rather than relocalizing to establish a new map called the recovery map. To improve the probability of successful initialization and the ability to recover map information, we investigate a self-adaptation fast re-initialization algorithm with the introduction of vanishing point detection. Initially, the scene vanishing points are detected by M-estimator sample consensus (MSAC) method. If vanishing points are present, the fast initialization method would be used. Otherwise, a simple initialization method would be applied; in other words, the proposed algorithm reduces the initialization requirements of the traditional SLAM system. The optimal re-initialization strategy could be selected automatically to ensure that the SLAM system continues tracking and mapping. Finally, for the successful tracking map and the recovery map, the improved loop method is used to obtain the transformation relationship between them. Once the overlapping maps are detected, the relative position and pose relationships between overlapping maps is calculated. Furthermore, the recovery map is transformed into a coordinate system that is consistent with the coordinate system of the matched map in memory. Second, scale information is used to fuse overlapping map points, and a map recovery method is proposed to reduce the errors caused by different scales between the successful tracking map and the recovered map, thereby solving the discrepancy between two maps caused by different scales to obtain accurate global consistency. Result We have compared the proposed system with the state-of-the-art system ORB-SLAM2 on two public datasets, namely, KITTI and EuroC, and a dataset is recorded in complex scenarios. The evaluation metrics contain the number of key frames, integrity rate of key frames, and recovery rate of key frames; moreover, we provide several point cloud maps and trajectory maps of the two systems for comparison. The experimental results show that the proposed system not only performs comparably in accuracy with ORB-SLAM2, but also significantly outperforms state-of-the-art existing methods in various real-world settings in tracking and mapping robustness. The comparative experiment consists of three parts, and the final experiment results are used to verify the effectiveness of the proposed system. In the first part, the KITTI dataset is added with noise, causing the traditional SLAM system to fail in tracking. The map restoration ability of the proposed system on KITTI00, KITTI02, and KITTI05 increased by 39.25%, 47.75%, and 32.46%, respectively. The experimental results show that the advantages of the proposed system are used to address tracking failure effectively and build a complete map, and the proposed systems are better than the state-of-the-art system ORB-SLAM2. In the second part, compared with the results in EuRoC, the proposed system has the same mapping accuracy on V1_01_easy dataset and V1_02_medium dataset as that of ORB-SLAM2. ORB-SLAM2 could not run steadily on the V1_03_difficult dataset, whereas the proposed system could, thereby also indicating that tracking stability has been significantly improved. The third part is the test in the real application scenario. The running dataset is obtained during walking more than one closed track in the underground garage with a camera. The underground garage has a small unlit room. The experimental results show that the proposed system still performs well in real application scenarios with environmental and motion complexity, and they are more complete than the map obtained by ORB-SLAM2. Conclusion Experimental results indicate that the proposed system can effectively recover maps in case of tracking failure. Furthermore, the proposed system can efficiently reuse the existing mapping results of the SLAM system, fix the map coordinate system, and significantly improve the robustness of the system.