Real-Time Dynamic SLAM Using Moving Probability Based on IMU and Segmentation

In dynamic environments, most simultaneous localization and mapping (SLAM) methods face challenges due to potential erroneous data associations caused by the movement of dynamic objects and the possibility of incorrect loop closures when objects move out of the field of view. To address this issue, we proposed real-time dynamic moving probability SLAM (RDP-SLAM), a real-time dynamic SLAM method based on the integration of inertial measurement unit (IMU) and segmentation. In this approach, a novel model was developed for calculating the moving probability of feature points using IMU data, allowing for the elimination of feature points associated with dynamic objects. Additionally, a feature point moving probability propagation model was constructed, which combines IMU data and segmentation results. This model enabled the tracking and classification of feature points into three groups: dynamic, static, and potentially dynamic. In the local and global optimization processes, a weighted objective optimization function was designed specifically for the moving probability of static feature points, thereby enhancing the accuracy of the optimization process. The experimental results conducted on public datasets and real-world scenarios demonstrated that RDP-SLAM was effective in robustly selecting static feature points and improving localization accuracy, particularly achieving a 75% reduction in absolute pose error (APE) compared to the oriented fast and rotated brief SLAM3 (ORB-SLAM3) system in complex dynamic environments.