D-VINS: Dynamic Adaptive Visual–Inertial SLAM with IMU Prior and Semantic Constraints in Dynamic Scenes

Abstract

Visual–inertial SLAM algorithms empower robots to autonomously explore and navigate unknown scenes. However, most existing SLAM systems heavily rely on the assumption of static environments, making them ineffective when confronted with dynamic objects in the real world. To enhance the robustness and localization accuracy of SLAM systems in dynamic scenes, this paper introduces a visual–inertial SLAM framework that integrates semantic and geometric information, called D-VINS. This paper begins by presenting a method for dynamic object classification based on the current motion state of features, enabling the identification of temporary static features within the environment. Subsequently, a feature dynamic check module is devised, which utilizes inertial measurement unit (IMU) prior information and geometric constraints from adjacent frames to calculate dynamic factors. This module also validates the classification outcomes of the temporary static features. Finally, a dynamic adaptive bundle adjustment module is developed, utilizing the dynamic factors of the features to adjust their weights during the nonlinear optimization process. The proposed methodology is evaluated using both public datasets and a dataset created specifically for this study. The experimental results demonstrate that D-VINS stands as one of the most real-time, accurate, and robust systems for dynamic scenes, showcasing its effectiveness in challenging real-world scenes.

VIS-SLAM: A Real-Time Dynamic SLAM Algorithm Based on the Fusion of Visual, Inertial, and Semantic Information

“…Liu [18] introduces a dynamic SLAM algorithm based on RGB-D Inertial, which uses YOLOv5 [19] as the object-detection algorithm and combines IMU motion consistency detection and epipolar geometry constraint methods to reject dynamic features. Sun [20] presents the D-VINS algorithm, which combines deep learning, IMU consistency detection, and geometric constraints to estimate the probability of feature point motion and assigns different weights to feature points based on this probability. Although these algorithms improve robustness by leveraging the advantages of sensor fusion, determining reasonable ranges for some hyperparameters in the algorithms, such as the segmentation threshold in IMU consistency detection, can be challenging.…”

ULG-SLAM: A Novel Unsupervised Learning and Geometric Feature-Based Visual SLAM Algorithm for Robot Localizability Estimation

“…Current research on visual SLAM is based on indoor environments, most of which are assumed to be static environments; however, the real environment is dynamic and complex, and the precision of visual SLAM will be difficult to guarantee in a scene containing dynamic objects, such as people or vehicles. Therefore, there is a growing body of research on visual semantic SLAM in dynamic situations (e.g., [17,18]). Brasch et al published DES-SLAM in 2018 [19], introducing a static rate to represent the probability of a map point being static.…”