Abstract
Most current state-of-the-art simultaneous localization and mapping (SLAM) algorithms perform well in static environments. However, their applications in real-world scenarios are limited by the assumption that environments are static because their performance becomes unstable in complex dynamic environments. To enhance system stability and localization accuracy in complex dynamic scenes, this article presents a novel visual-inertial SLAM system called DGM-VINS. In DGM-VINS, a joint geometric dynamic feature extraction module (JGDFE) is designed that can combine the advantages of multiple geometric constraints and effectively reduce the limitations of a single geometric constraint in the application process. In addition, a temporal instance segmentation module (TISM) is presented to establish the temporal correlation of instance objects in consecutive frames, which effectively addresses the instance segmentation issue in complex environments. The inertial measurement unit (IMU) is utilized for motion prediction and consistency detection to improve localization accuracy in challenging environments with weak textures. The proposed methodology is tested in various public datasets and actual scenarios, and the results demonstrate superior accuracy and robustness than existing methods in complex dynamic scenarios.
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