# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 1 Introduction  
Visual-Inertial Simultaneous Localization and Mapping (VI-SLAM) has become a pivotal technology in robotics and computer vision [1, 2, 3]. This technique integrates data from visual sensors, typically monocular or stereo cameras, with inertial measurement unit (IMU) data used to simultaneously estimate the robot's pose and construct a map of the environment. Although VI-SLAM has shown considerable success in static environments,its performance is significantly impeded by the presence of dynamic objects [4]. Dynamic objects, such as moving vehicles, pedestrians, or other unpredictable elements, expand the potential risks of VI-SLAM methods when interacting with real-world environments. These dynamic objects disrupt the data correlation between image frames, causing pose estimation drift or even loss and result in ghosting effects in the generated maps. To address this issue, extensive research has attempted to eliminate the harm of dynamic objects to the entire SLAM system through visual-based dynamic recognition methods. However, these solutions come with inherent limitations more or less. These schemes can be broadly categorized into two types: constraint-based solutions and deep learning-based solutions. The first category establishes geometric or dynamic constraints to identify and remove dynamic features without deep learning [5; 6; 7; 8]. However, these methods typically treat dynamic points as outliers and require precise camera pose estimation, which becomes particularly challenging when dynamic objects occupy a significant portion of the field of view. The second category utilizes deep learning methods for motion segmentation, thereby marking dynamic regions [9; 10; 11; 12]. However, these approaches impose substantial computational burdens and typically can only handle predefined dynamic objects. A feasible approach to addressing this challenge is the integration of additional sensors to enhance dynamic segmentation capabilities. Among these, event cameras, using a novel type of biologically inspired sensor [13; 14; 15], stand out as a particularly promising yet underexplored option, offering a new paradigm for high-speed dynamic visual perception. Unlike traditional cameras that capture intensity images at fixed intervals, event cameras measure asynchronous brightness changes at each pixel, which are known as \"events\". This operating mechanism gives event cameras low latency, high dynamic range, and microsecond-level temporal resolution. While some studies have applied event cameras to SLAM schemes to explore their performance in high-speed motion and high dynamic range (HDR) scenes (e.g., overexposure or dim environments) [16; 17; 18; 19; 20], the research focused on dynamic scenes remains limited. As an attempt to fill the research gap in the study of dynamic scenes, a novel event-enhanced VI-SLAM scheme for dynamic environments, namely, E\({}^{2}\)-VINS, is proposed in this paper. In our E\({}^{2}\)-VINS, the visual-inertial alignment is used for system initialization and IMU measurement calibration. Next, through IMU-assisted self-motion compensation, the events are spatiotemporally aligned, enabling the generation of normalized mean timestamps, which are regarded as dynamicity metrics. Based on these pixel-wise metrics, dynamicity weights are adaptively assigned to the visual residual terms of different features. The system state (camera poses and map points) and dynamicity weights are then jointly and alternately optimized in E\({}^{2}\)-VINS. To ensure that our results are fully reproducible, all the relevant data and codes have been released at [[https://cslinzhang.github.io/EE-VINS/EE-VINS.html](https://cslinzhang.github.io/EE-VINS/EE-VINS.html)]([https://cslinzhang.github.io/EE-VINS/EE-VINS.html](https://cslinzhang.github.io/EE-VINS/EE-VINS.html)), accessed on 27 September 2024. Our key contributions are as follows:  
1.