Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 19-24· 0 citations· 14 references
Abstract
Simultaneous Localization and Mapping (SLAM) enables autonomous navigation and mapping for mobile robots, but most visual SLAM systems assume static environments and fail in the presence of dynamic objects, leading to localization drift and distorted maps. To address this, we propose RGS-SLAM, a robust Gaussian Splatting–based SLAM system that integrates YOLOv8 for dynamic object detection and LaMa inpainting to restore occluded regions, ensuring stable tracking and dense map construction. The system employs 3D Gaussian Splatting for efficient scene representation and photorealistic rendering. Experiments on the TUM RGB-D dynamic dataset demonstrate that RGS-SLAM reduces trajectory error by up to 91.6% compared with ORB-SLAM3 and generates high-quality dense maps under dynamic conditions. Moreover, the system runs efficiently on the NVIDIA Jetson AGX Orin, highlighting its feasibility for real-world edge applications.
This paper introduces SAR-SLAM (Semantic-Aware Recognition SLAM), an RGB-D SLAM framework that robustly handles dynamic scenes containing moving people and objects using dual semantic geometric processing, and remains competitive with state-of-the-art dynamic SLAM methods across a range of dynamic scenarios.
Basheer Al-Tawil, Magnus Jung, Thorsten Hempel et al.· Robotics· 0 citations
The proposed Semantic and Geometric Adaptive SLAM system effectively suppresses dynamic artifacts and point-cloud contamination in dense mapping, generating static environment maps with clearer structures and improved geometric consistency.
Xiao-Xuan He, Xiao-Hui Zhang, Jin-Feng Zheng et al.· Engineering Research Express· 0 citations
This work proposes Robust Semantic-aware Gaussian Splatting SLAM (RoSe-SLAM), to address the dynamic challenge by a holistic semantic scene understanding from uncalibrated monocular inputs, achieving accurate camera tracking and high-quality geometry reconstruction.
Wen-Ting Wang, Jia-Xin Guo, Wen-Zhen Dong et al.· 0 citations
This paper proposes LV-GS SLAM, a novel system that integrates LiDAR and visual data for incremental, large-scale reconstruction with real-time tracking, and develops a keyframe-based submap management framework that dynamically adjusts memory allocation based on both primitive density and inter-frame overlap ratio, ef...
