# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 5 Discussions  
The primary advantage of E\({}^{2}\)-VINS lies in its ability to robustly handle dynamic environments, which is an aspect where traditional VI-SLAM methods often struggle. By introducing dynamicity-aware weighting, our method enhances the contribution of visual residuals during BA, selectively reducing the influence of dynamic features on the system. This ability to filter out dynamic features through a soft-threshold mechanism significantly improves the robustness of the SLAM system, particularly in scenarios with moving objects. The average improvement of 1.884% in the mean position error (compared to state-of-the-art methods) demonstrates the effectiveness of the dynamicity-adaptive strategy in dynamic environments.  
Despite the strengths of E\({}^{2}\)-VINS, there are some inherent limitations. In cases where the number of static feature points becomes too sparse (e.g., in narrow corridors, tunnels, or low-texture environments), our dynamicity-weighted BA can degrade into an IMU-residual-dominated approach, leading to poor localization performance. This issue arises when dynamic features no longer provide enough constraints, and the system relies too heavily on IMU data for pose estimation. To address this, future improvements should focus on enhancing the system's robustness in low-texture scenarios by incorporating more advanced feature extraction methods or by developing strategies that better balance the contribution of IMU and visual data. Furthermore, the alignment strategy between image frames and events in E\({}^{2}\)-VINS currently depends on hardware synchronization, which is not universally available across all event camera models. While synchronization is supported by specific models such as DAVIS346, ATIS, and CeleX5_mp, this limitation restricts the applicability of our method to these particular cameras. Future research should focus on developing robust software-based alignment algorithms that can accommodate a wider range of event camera models, extending the applicability of our method to more diverse systems.  
Our work builds on existing VI-SLAM and event-camera-based SLAM systems but differs significantly in its approach to handling dynamic environments. Many traditional VI-SLAM systems fail to adequately deal with dynamic objects, often leading to drift or incorrect trajectory estimations in the presence of moving features. Previous methods in dynamic SLAM often use approaches like dynamic object detection or motion segmentation; however, these methods typically rely on either prior knowledge or computationally expensive algorithms that are not real time. By contrast, E\({}^{2}\)-VINS introduces a dynamicity-adaptive approach that filters dynamic features directly within the optimization framework, providing a more efficient and robust solution. Moreover, previous work in event-based SLAM has primarily focused on static environments. Our method is the first to integrate dynamicity weights in the bundle adjustment process, making it significantly more effective in real-world dynamic environments where traditional methods struggle.  
As part of future work, we plan to deploy E\({}^{2}\)-VINS in practical scenarios such as autonomous driving and robotics, where robustness to moving objects is crucial. Real-time testing and performance evaluation in these environments will help identify further optimizations and ensure that the method performs effectively under real-world conditions. Another important direction for future work is improving dynamic feature segmentation to handle more complex dynamic features, such as non-rigid objects or unpredictable movements, which are common in real-world dynamic environments. In light of the current limitation with hardware synchronization, we plan to explore software-based synchronization techniques for event cameras. By enabling synchronization across a broader range of camera models, we can extend the applicability of E\({}^{2}\)-VINS to more diverse sensor setups.