A Real-Time Map Restoration Algorithm Based on ORB-SLAM3

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%.

Automated summary

In the monocular visual-inertia mode of ORB-SLAM3, insufficient excitation obtained by the inertial measurement unit (IMU) results in a long system initialization time, causing easy loss of trajectory and incomplete map creation. To address this problem, a fast map restoration method is proposed, improving accuracy by approximately 47.51% and time efficiency by approximately 55.96% through accelerated tracking and loop closure detection.