Visual SLAM in Dynamic Environments: Robustness and Adaptability

This research paper explores the potential of visual programming languages (VPLs) in expanding the accessibility and applicability of computer vision and Simultaneous Localization and Mapping (SLAM) systems. The paper also discusses the application of VPLs in analyzing and modifying video streams, data from Light Detection and Ranging (LIDAR), stereo cameras, and acoustic sensors, thereby expanding the scope of applications in computer vision. Highlights the potential of VPLs in SLAM systems, which are crucial for robot parameters like navigation, mapping, and Virtual Reality (VR) or Augmented Reality (AR). By incorporating VPLs into SLAM architectures, the paper argues that more ordinary users can engage with SLAM systems, which are based on concepts in computational geometry and computer vision. The paper reviews the extended Kalman filter, covariance intersection, and Graph-based SLAM (GraphSLAM), and discusses How VPLs can be integrated into these algorithms to improve their accessibility and applicabil-ity. The paper proposes several strategies for addressing these challenges, including the development of domain-specific VPLs. The paper also proposes future research directions in this area, including the development of new VPLs for SLAM systems, the integration of VPLs with other computer vision technologies, and the exploration of the potential for VPLs to improve the accuracy and efficiency of SLAM systems. To conclude, the paper discusses the opportunities associated with integrating VPLs into SLAM systems, such as the potential for VPLs to democratize SLAM technology and enable more ordinary users to engage with SLAM systems, and the potential for VPLs to accelerate the development of new SLAM algorithms and applications.