# A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
###### Abstract
VINS-Mono (visual-inertial system) proposed by the Flight Robot Laboratory [15] of Hong Kong University of Science and Technology is a tightly coupled monocular visual-inertial SLAM system, which has a more perfect system framework, including five parts: observation preprocessing, initialization, joint optimization of local visual-inertia, global graph optimization and loop closure detection. VINS-MONO uses the Lucas-Kanade (LK) optical flow method to track inter-frame feature points, and accelerates the tracking process.Not long ago, [PERSON] et al. [16] also proposed Kimera, and used technologies such as PGO and loop closure detection, 3D grid reconstruction and semantic labeling. Although it is not stable at present, it shows great potential. [PERSON] et al. [17] proposed ORB-SLAM3, which is one of the most accurate and robust algorithms at present. ORB-SLAM3 is the first open source algorithm that can support pure visual, visual-inertia and multi-map reuse. It not only supports the pinhole camera model, but also supports the fisheye camera model and custom camera model. It only needs to take the projection, back projection and Jacobian equation as input. ORB-SLAM3 establishes three kinds of data association: short-term, medium-term and long-term data association, which enables the algorithm to adapt to various environments and greatly improves the positioning accuracy through different optimization methods. In the visual-inertia mode, ORB-SLAM3 first describes the visual-inertia initialization problem as an optimal estimation problem [18], which enables the inertial measurement unit to initialize in a short time, so that the pose of the lost frame can be calculated temporarily by IMU pre-integration when short-term image tracking is lost. Even in the case of long-term data loss, the current active map is saved by the Atlas multi-map system [19], and then a new map is re-established, initialized and tracked on this new map. Based on ORB-SLAM3, fast corner points are extracted by the adaptive dynamic threshold, and a random sampling consensus (RANSAC) method is used to identify candidate ORB features [20]. [PERSON] et al. [21], [PERSON] et al. [22] and [PERSON] et al. [23] improve the performance of dynamic scenes by combining semantic information. Most of the above improvements on the SLAM system require high computational cost to improve the performance of some specific scenes. Excessive computational time cannot meet the real-time requirements of robots in practical applications. At the same time, in the framework of the monocular camera, if the system cannot be well initialized and create an accurate map, it is very detrimental to the expansion of the SLAM system and the tracking of robots in offline state. For the initialization problem of the monocular SLAM system, [PERSON] et al. [17] calculated the homography matrix and fundamental matrix synchronously, then, selecting the model according to the scores of them will increase the success rate of initialization and achieve the automatic initialization process. In the work of [PERSON] et al. [24], a fast monocular initialization process based on the feature is proposed, which can improve the speed of the initialization process. In order to improve the quality and success rate of initialization, [PERSON] et al. [25] proposed an improved iterative strategy based on trust region. [PERSON] et al. [26] added the generalized motion feature assumption in the initialization process, and transformed the solution of the rotation and translation matrix of camera motion into the error elimination problem in the initialization process. However, current monocular initialization methods cannot improve both the accuracy and real-time performance. These methods are all based on SFM, and the initialization process requires the participation of many frames, which is the limitation of the SLAM. In particular, in the initialization of ORB-SLAM3 monocular visual inertial mode, with insufficient IMU excitation will make the initialization process of the system even slower.