# A Real-Time Map Restoration Algorithm Based on ORB-SLAM3
###### Abstract  
In the monocular visual-inertia mode of ORB-SLAM3, the insufficient excitation obtained by the inertial measurement unit (IMU) will lead to a long system initialization time. Hence, the trajectory can be easily lost and the map creation will not be completed. To solve this problem, a fast map restoration method is proposed in this paper, which adresses the problem of insufficient excitation of IMU. Firstly, the frames before system initialization are quickly tracked using bag-of-words and maximum likelyhood perspective-n-point (MLPNP). Then, the grayscale histogram is used to accelerate the loop closure detection to reduce the time consumption caused by the map restoration. After experimental verification on public datasets, the proposed algorithm can establish a complete map and ensure real-time performance. Compared with the traditional ORB-SLAM3, the accuracy improved by about 47.51% and time efficiency improved by about 55.96%. visual-inertial SLAM; ORB-SLAM3; initialization; tracking; bag-of-words; MLPNP; loop closure detection; grayscale histogram +
Footnote †: journal: Medical Imaging  
**Citation:** [PERSON], [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] A Real-Time Map Restoration Algorithm Based on ORB-SLAM3. _Appl. Sci._**2022**, 12, 7780. [[https://doi.org/10.3390/app12157780](https://doi.org/10.3390/app12157780)]([https://doi.org/10.3390/app12157780](https://doi.org/10.3390/app12157780))  
Academic Editor: [PERSON]  
Received: 14 June 2022  
Accepted: 29 July 2022  
Published: 2 August 2022  
**Published:** **Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. **Cognition** (c) 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))). **Copyright** (c) 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/))). **Subscrib** (c) 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons. R-ORBSLAM by combining photometric information with ORB features; [PERSON] et al. [8] adds semantic segmentation network SegNet [9] and moving consistency check to improve dynamic performance, and generates dense semantic octo-tree map [10]; [PERSON] et al. [11] and [PERSON] et al. [12] also studied to improve the performance of ORB-SLAM2 in a dynamic environment. The former added the function of dynamic target detection and background embedding, while the latter combined with central processing unit (SSD) [13]. With the continuous expansion of the application field of SLAM, the requirements for the robustness and stability of the SLAM system are also increasing. Therefore, the fusion of multi-sensors in the SLAM system has become an inevitable trend, and the joint processing of visual information and IMU data is the most feasible method at present. [PERSON] et al. [14] proposed a tightly coupled stereo VIO algorithm OKVIS (open keyframe-based visual-inertial SLAM). Based on the concept of keyframes, they use sliding windows for batch nonlinear optimization and compute Harris corners as well as BRISK descriptors. However, the algorithm can only output poses with six degrees of freedom. Since there are no loop closing threads, it is not a complete SLAM system.