# E2-VINS: An Event-Enhanced Visual-Inertial SLAM Scheme for Dynamic Environments
## 4 Experiments
### Quantitative Results  
Tables 3 and 4 report quantitative results of the comparison of our method with other SLAMs in term of Mean Position Error (%) on sequences from the VIODE dataset [42] and the ECMD dataset [43], respectively. The optimal results are highlighted in bold, while the runner-up results are highlighted with an underline. The evaluation experiments were conducted on the complete VIODE dataset [42] and partial sequences of ECMD [43], which correspond to scenes that include dense streets, highways, and nighttime scenes. Due to the absence of event data in the VIODE dataset [42], we used v2e [45] to generate event data from the videos. This generated data served as input for the proposed E\({}^{2}\)-VINS to obtain reliable quantitative comparison results. Figure 5: Visualization of some typical qualitative results. The weighted feature image frames obtained by E\({}^{2}\)-VINS on three test sequences from the VIODE dataset [42] (city_day, city_night, and parking_lot) and four sequences from the ECMD dataset [43] (Dense_street_day_easy_a, Dense_street_day_easy_b, Dense_street_night_easy_a, and Urban_road_day_easy_a) are shown on the upper side. Features with low weights (from dynamic objects) are plotted as red points, while features with high weights (from static objects) are shown as green ones. The trajectories of the compared algorithms and E\({}^{2}\)-VINS on the Dense_street_day_medium_a and Dense_street_night_easy_a sequences of the ECMD dataset [43] are illustrated on the lower side. Table 3 shows that E\({}^{2}\)-VINS outperformed all other baseline methods in translation accuracy except for nighttime sequences, where its performance was comparatively lower. This lower performance in nighttime sequences is attributed to the relatively low quality of the event data generated by v2e [45] from dim nighttime videos, which lack high dynamic range characteristics compared to real event cameras. The dynamic SLAM method DynaVINS [8] showed stronger overall adaptability to dynamic environments compared to other classical methods. In contrast, classical SLAM methods such as ORB-SLAM3 [1] and DSO [3], which perform well in static scenes, were unable to effectively complete localization tasks in nighttime sequences and outdoor high-dynamic scenarios. The results indicate that feature-based methods, due to their sparse feature points, are more resilient to dynamic scene disturbances than direct methods. Furthermore, Table 4 confirms that E\({}^{2}\)-VINS is superior to all other baseline methods in real dynamic scenarios. It is noteworthy that other comparative methods failed to complete the test sequences \"Dense_street_day_easy_a\" and \"Dense_street_day_medium_a\". In the former sequence, there are vehicles queuing up and large vehicles occupying most of the field of view, causing traditional pipeline strategies [1; 28] to treat relatively few static points as outliers. In contrast, with IMU-assisted event data, many dynamic points can be identified by the dynamicity metrics and their weights reduced, resulting in an effect opposite to that of traditional visual-inertial SLAM. In the latter sequence, a large number of large trucks queue under an overpass waiting for traffic lights. In this complex scenario with multiple dynamic targets, classical SLAM methods would treat pseudo-static feature points, which are transformed into dynamic points, as lost tracks, followed by detecting new feature points. During this process, motion estimation errors and trajectory \"drift\" can occur, as shown in the lower side of Figure 5. In the compared schemes, DynaVINS [8] demonstrated a higher success rate than traditional static schemes [1; 3; 21; 28] due to its consideration of dynamic environments and the use of IMU motion priors to filter out dynamic features. And the event-based approach USLAM [21] indeed showed advantages in scenarios with fewer dynamic targets(Urban_road), but the lack of process strategies for moving targets made it unable to cope with scenarios with more dynamic targets(Dense_street).