# Precise Feature Removal Method Based on Semantic and Geometric Dual Masks in Dynamic SLAM
## 1 Introduction  
Simultaneous Localization and Mapping (SLAM) is a critical technology for autonomous robots, self-driving vehicles, unmanned aerial vehicles, and related fields. The aim of SLAM is to allow devices to construct maps of unknown environments while simultaneously performing autonomous localization and navigation [1, 2, 3]. With the rapid advancement of computer vision and deep learning in combination with continuous improvement of hardware computational capabilities, vision-based SLAM systems now demonstrate three core advantages over other sensors such as LiDAR, millimeter-waveradar, and satellite navigation systems. First, in terms of cost-effectiveness, SLAM can achieve centimeter-level positioning accuracy using only consumer grade cameras [4; 5]. Second, in terms of environmental adaptability, independence from external signal sources ensures continuous localization capability in environments where global navigation satellite systems are unavailable (e.g., indoor spaces, tunnels). Most significantly, SLAM systems natively support synergistic processing with computer vision tasks, enabling direct environmental understanding through real-time semantic segmentation algorithms, a unique capability unattainable by other sensors. These competitive advantages establish visual SLAM as a predominant research direction in cost-sensitive applications requiring environmental intelligence, including autonomous mobile robots and augmented-reality navigation systems. Modern visual SLAM systems exhibit highly mature architectures typically comprising several core components, including a feature extraction frontend, a state estimation backend, and loop closure detection modules. Furthermore, advanced SLAM algorithms such as ORB-SLAM3 [6], DSO [7], VSO [8], and LSD-SLAM [9] have achieved highly satisfactory performance levels. However, in practical application scenarios, vision-based SLAM systems typically assume a static environment in which the relative positions among scene points remain constant and all visual changes are attributed to camera motion [10]. This assumption often does not hold in real-world environments, especially in scenarios with dynamic objects, where the positional accuracy and map construction quality of traditional visual SLAM methods may be seriously compromised. Moreover, existing methods generally adopt a strategy of directly discarding all potential dynamic feature points. Although this approach can effectively avoid dynamic interference, it may also mistakenly eliminate some static feature points, thereby affecting the stability and map quality of the SLAM system [11]. In response to these issues, the objective of this study is to propose a dynamic feature point filtering method to enhance the feature point selection capability in dynamic environments. By combining semantic segmentation masks with geometric consistency constraints, we aim to more accurately identify and preserve static feature points, thereby improving the localization accuracy and map construction quality of SLAM systems. Concurrently, our method aims to reduce the interference of dynamic objects on the SLAM system, ensuring stable operation in complex and dynamic environments. Geometric consistency constraints are applied to the filtered feature points for validation (via reprojection error checks) and optimization (via bundle adjustment). Notably, the information of outlier feature points is preserved in order to assist in the ongoing judgment of dynamic regions under ever-changing environments [12; 13; 14]. The main contributions of this paper are as follows:  
1. A dual-mask dynamic feature point filtering method is proposed that combines semantic segmentation masks with outlier information to precisely filter dynamic feature points during the feature matching stage. Instead of simply eliminating the entire dynamic area, this method identifies the local motion areas of dynamic objects. This minimizes false eliminations while still preserving as many static feature points as possible.