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DMSG-SLAM: Cascaded Semantic and Geometric Filtering for RGB-D Tracking and Mapping in Dynamic Environments
Beicheng Li1, Enhui Zheng1, Huailiang Wang1
1School of Mechanical and Electrical Engineering, China Jiliang University, Hangzhou 310018, China.
Sensors (Basel, Switzerland)
|June 26, 2026
Summary
Dynamic environments challenge visual SLAM (simultaneous localization and mapping) systems. DMSG-SLAM enhances localization accuracy by fusing depth, semantic, and geometric data to effectively remove dynamic objects, improving performance by up to 90%.
Area of Science:
- Robotics
- Computer Vision
- Artificial Intelligence
Background:
- Traditional visual SLAM struggles with localization drift in dynamic environments due to moving objects.
- Existing semantic segmentation and depth-based masking methods have limitations, including under-segmentation and missed detections of truncated objects.
Purpose of the Study:
- To propose DMSG-SLAM, a cascaded visual SLAM framework designed to improve localization accuracy in dynamic environments.
- To fuse depth-mask, semantic information, and geometric constraints for robust dynamic object handling.
Main Methods:
- A lightweight object detection network with depth consistency generates initial masks for dynamic feature removal.
- A rotation-aware local epipolar geometric filtering mechanism suppresses residual features near object boundaries.
- Adaptive epipolar thresholding and TSDF-based dense volumetric mapping enhance surface reconstruction and filtering under challenging motion.
Main Results:
- DMSG-SLAM effectively removes dynamic features and mitigates perceptual blind spots caused by occlusion or truncation.
- The system demonstrates competitive accuracy in highly dynamic environments, outperforming ORB-SLAM2.
- Localization performance showed improvements of up to 90% on the TUM RGB-D dataset.
Conclusions:
- DMSG-SLAM offers a robust solution for visual SLAM in dynamic environments by effectively integrating multiple information sources.
- The proposed methods significantly enhance localization accuracy and map consistency compared to existing approaches.
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