# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 4 Experiments  
### System Implementation and Methods for Comparison  
We compared our proposed E\({}^{2}\)-VINS with the following competing methods:  
* **ORB-SLAM3**[1] is a system capable of performing visual, visual-inertial, and multimap SLAM with monocular, stereo, and RGB-D cameras using pinhole and fisheye lens models. It is not only a feature-based, tightly integrated visual-inertial SLAM system that relies on Maximum A Posteriori (MAP) estimation but also a multiple map system that utilizes a novel place recognition method for improved recall. * **DSO**[3] is a direct and sparse formulation for visual odometry. It combines a fully direct approach that minimizes photometric errors with a consistent and joint optimization of all model parameters, including geometry represented as inverse depth in a reference frame and camera motion. * **VINS-FUSION**[28] is an optimization-based multisensor state estimator. It features a tightly coupled monocular visual-inertial state estimator designed by fusing preintegrated IMU measurements with feature observations. * **USLAM**[21] is an indirect monocular method that fuses events, frames, and IMU measurements. Its frontend converts events into frames by motion compensation using the IMU's gyroscope and the median scene depth. Then, FAST corners [39] are extracted and tracked separately on the event frames and the grayscale frames and then passed to a geometric feature-based backend. Similar to the proposed E\({}^{2}\)-VINS, USLAM [21] constructs three-modality tightly coupled SLAM systems using IMU, events, and image frames as inputs. * **DynaVINS**[8] is a novel visual-inertial SLAM framework designed for dynamic environments. It uses robust bundle adjustment based on IMU preintegration and a multihypothesis-based constraints grouping method to reduce the impact of dynamic objects and temporarily static objects, respectively. The above state-of-the-art methods all support multisensor systems. To ensure a fair evaluation of the proposed methods, we used the versions of these systems that are based on monocular cameras and IMUs for comparison, and we kept the parameters of all comparison methods consistent with the official settings. Our E\({}^{2}\)-VINS system was configured with several key parameters to ensure efficient execution of visual-inertial SLAM tasks in dynamic environments. First, the feature tracking section set the maximum number of features to 150 and the minimum distance between features to 15 pixels to prevent overly dense or sparse feature distributions. The frequency for publishing tracking results was set to 10 Hz to ensure accurate estimations. The maximum estimated depth was limited to 10 m. The regularization parameter \(\lambda_{w}\) was set to 2.0 to prevent overfitting, and the momentum coefficient \(\lambda_{m}\) was set to 0.2. The alternating convergence factor was set to 0.9 and served as the convergence threshold for the total residuals during the optimization process. For optimization parameters, the maximum solver time was set to 0.3 milliseconds to ensure real-time performance. The maximum number of solver iterations was limited to 10 to prevent excessive iterations that could degrade performance. The keyframe selection threshold based on parallax was set to 10 pixels to ensure accurate keyframe selection in scenes with significant changes. Finally, the IMU parameters included accelerometer and gyroscope noise standard deviations, as well as accelerometer and gyroscope bias random walk noise values that were set to empirical values (\(n_{a}\): 0.2, \(\mathbf{b}_{a}\): 0.05, \(n_{\omega}\): 0.02, \(\mathbf{b}_{\omega}\): 4 \(\times\) 10\({}^{-5}\)). The gravity magnitude was set to 9.81007 m/s\({}^{3}\). These parameter settings ensure that the system has good robustness and real-time performance in dynamic environments while providing sufficient flexibility to adapt to different application scenarios. All modules in E\({}^{2}\)-VINS were implemented in C++.