# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 2 Related Work
### Dynamic VI-SLAM  
Dynamic VI-SLAM solutions can be broadly classified into two categories: constraint-based methods and deep learning-based methods. Table 2 provides an overview of existing dynamic VI-SLAM methods, including camera configuration, the use of additional priors, support for temporary stopping of dynamic objects, and consideration of the uncertainty of dynamic objects. Constraint-based methods, which do not rely on deep learning, typically establish epipolar geometric or dynamic constraints to filter and remove dynamic features. [PERSON] et al. [5] used IMU data to compensate for rotation between consecutive images and calculate motion vectors, filtering out dynamic features exhibiting different motion trends. [PERSON] et al. [6] proposed a dynamic feature point selection method based on multiview geometry. After projecting different images onto the same plane, they located dynamic regions through rotation and translation constraints. Based on self-motion compensation and Dense Inverse Search (DIS) optical flow, [PERSON] et al. [7] proposed a superpixel level fast motion object detection method in their sparse feature visual odometry, which extracts and discards dynamic elements from camera tracking and mapping processes. [PERSON] et al. [8] proposed a robust bundle adjustment that can reject the features of dynamic objects by utilizing the pose prior estimated by IMU preintegration, while discarding dynamic features that deviate significantly from the motion prior while estimating the camera pose. Most of these \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Input** & **Main limitations** &
\begin{tabular}{c} **Dynamic** \\ **Environment** \\ \end{tabular} \\ \hline [PERSON] et al. [18] & & GPU requirement & \(\times\) \\ [PERSON] et al. [19] & Events & Insufficient robustness & \(\times\) \\ [PERSON] and [PERSON] [26] & & Rotational motion only & \(\times\) \\ \hline [PERSON] et al. [20] & Events and depth images & RGB-D camera brings efficiency bottleneck & \(\times\) \\ \hline [PERSON] et al. [23] & Events and RGB images & Relatively low computational efficiency & \(\times\) \\ [PERSON] et al. [24] & & - & \(\times\) \\ \hline [PERSON] et al. [25] & Events, images, and IMU measurements & Not open source insufficient utilization of the motion sensitivity of events & \(\times\) \\ \(\text{E}^{2}\)-VINS (Ours) & & - & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An overview of existing event-based SLAM. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Method** & **Camera Configuration** & \multicolumn{3}{c}{**Practical Consideration**} \\ \cline{3-5}  & **Configuration** & **Prior Knowledge** & **Temporary Stopping** & **Uncertainty** \\ \hline \hline Constraint-based methods & & & \\ \hline [PERSON] et al. [5] & RGB-D & \(\checkmark\) & \(\checkmark\) & \(\times\) \\ [PERSON] et al. [6] & Monocular & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [7] & RGB-D & \(\times\) & \(\times\) & \(\times\) \\ [PERSON] et al. [8] & Monocular & \(\checkmark\) & \(\times\) & \(\checkmark\) \\ \hline \hline Deep learning-based methods & & & \\ \hline DS-SLAM [9] & Monocular & \(\times\) & \(\checkmark\) & \(\times\) \\ DynaSLAM [10] & Monocular & \(\times\) & \(\checkmark\) & \(\times\) \\ SLAMANTIC [11] & Monocular & \(\times\) & \(\checkmark\) & \(\times\) \\ Dynamic-VINS [12] & RGB-D & \(\checkmark\) & \(\checkmark\) & \(\times\) \\ \hline \hline \(\text{E}^{2}\)-VINS (Ours) & Event & \(\times\) & \(\checkmark\) & \(\checkmark\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: An overview of existing dynamic VI-SLAM. methods assume accurate camera pose estimation, with a high proportion of static features. However, when dynamic objects occupy the majority of the field of view, these methods may fail, resulting in inaccurate camera pose estimation. Deep learning-based methods focus on motion recognition and dynamic region partitioning.