# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 2 Related Work
### Event-Based SLAM  
Event-based SLAM technology is in the early stages of rapid development, with some research exploring the use of event cameras as novel data sources for visual odometry and SLAM. In 2016, [PERSON] et al. [18] proposed the first complete event-based visual odometry system, incorporating three probabilistic filters to predict camera motion, light brightness gradient, and inverse depth of the scene. This work assumed a constant brightness and linear gradient, and it achieved simultaneous motion tracking and 3D scene reconstruction, but required a GPU due to the huge computational burden. [PERSON] et al. [19] proposed EVO, an event-based visual odometry system that eliminates the need for light brightness reconstruction and operates on a standard CPU. EVO [19] includes two parallel pipelines: a tracking pipeline based on image-to-model alignment and a mapping pipeline for event-based 3D reconstruction. Unfortunately, the localization and mapping accuracy of it are relatively poor due to its unstable event-based bootstrap and feature detection method. USLAM [21] is the first state estimation pipeline that tightly couples events, standard frames, and inertial measurements to provide robust and accurate state estimation. And it can work normally under conditions such as low-light scenes or fast motion. [PERSON] et al. [20] proposed DEVO, which generates semi-dense depth maps by warping depth values from an extrinsically calibrated depth camera and updates the camera pose through geometric 3D-2D edge alignment.  
This approach was further extended to Canny-EVT [22] recently, but it still relies on additional depth prior information from the depth camera to ensure system functionality. [PERSON] et al. [23] proposed EDS, the first system to use a direct method for 6-DoF visual odometry incorporating both event and color information. EDS [23] uses sparse 3D points to predict the brightness increments of pixels through an ordinary optical camera and estimates camera motion by comparing it with event-based brightness increments in error. [PERSON] et al. [24] proposed the first pure event-based 6-DoF motion compensation method and built a novel visual-event odometry, namely, MC-VEO, which uses the event generation model to align event images with RGB images for camera pose estimation. [PERSON] et al. [25] proposed a robust, high-precision, real-time optimized monocular event-based VIO framework that leverages both point and line features. The framework tightly integrates events, images, and IMU measurements; unfortunately, this approach is not available as open source. [PERSON] and [PERSON] [26] introduced the first event-based rotation-only bundle adjustment (BA) approach and developed CMax-SLAM, which eliminates the need to convert events into frames. However, due to the limitations of the contrast maximization framework, CMax-SLAM has been restricted to 3-DoF pure rotational motion estimation and is unsuitable for long-distance trajectory prediction. Overall, as shown in Table 1, existing event-based SLAM approaches are based on the assumption of static scenes and overlook the potential of utilizing event cameras' sensitivity to dynamic objects for enhanced performance in dynamic environments.