Sun, Y.; Wang, Q.; Yan, C.; Feng, Y.; Tan, R.; Shi, X.; Wang, X. D-VINS: Dynamic Adaptive Visual–Inertial SLAM with IMU Prior and Semantic Constraints in Dynamic Scenes. Remote Sens.2023, 15, 3881.
Sun, Y.; Wang, Q.; Yan, C.; Feng, Y.; Tan, R.; Shi, X.; Wang, X. D-VINS: Dynamic Adaptive Visual–Inertial SLAM with IMU Prior and Semantic Constraints in Dynamic Scenes. Remote Sens. 2023, 15, 3881.
Sun, Y.; Wang, Q.; Yan, C.; Feng, Y.; Tan, R.; Shi, X.; Wang, X. D-VINS: Dynamic Adaptive Visual–Inertial SLAM with IMU Prior and Semantic Constraints in Dynamic Scenes. Remote Sens.2023, 15, 3881.
Sun, Y.; Wang, Q.; Yan, C.; Feng, Y.; Tan, R.; Shi, X.; Wang, X. D-VINS: Dynamic Adaptive Visual–Inertial SLAM with IMU Prior and Semantic Constraints in Dynamic Scenes. Remote Sens. 2023, 15, 3881.
Abstract
Visual inertial SLAM algorithms enable robots to autonomously explore and navigate in unknown scenes. However, most of the current SLAM systems highly rely on static environment assumptions, which fails in the exsitence of motional objects in the real environment. To improve the robustness and localization accuracy of SLAM systems in dynamic scenes, this paper proposes a visual-inertial SLAM framework that fuses semantic and geometric information, called DA-VINS. First, this paper presents a dynamic object classification method based on feature’s current motion state, which obtains temporary static features in the environment. Secondly, a features dynamics check module based on IMU prior and adjacent frame’s geometry constraint is designed to calculate dynamic factors. It also verifies the classification results of temporary static features. Finally, a dynamic adaptive bundle adjustment module based on the features’ dynamic factors is designed to adjust the weights of features in nonlinear optimization. We evaluated our method in public and our dataset. The results show that D-VINS is one of the most real-time, accurate, and robust systems in dynamic scenes.
Computer Science and Mathematics, Computer Vision and Graphics
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