September 23, 2025SymmetryOpen Access

MSGS-SLAM: Monocular Semantic Gaussian Splatting SLAM

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The MSGS-SLAM system achieves robust semantic feature integration without dedicated depth sensors, enhancing deployment potential.
By utilizing monocular vision and three-dimensional gaussian distribution, the system significantly reduces hardware costs and complexities.
A keyframe selection algorithm based on semantic guidance effectively mitigates pose drift errors during prolonged operations.
Evaluation on standard datasets shows MSGS-SLAM matches existing models on key performance metrics like ATE RMSE and mIoU.

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Implication

This research introduces MSGS-SLAM which enhances semantic understanding in robotics, suggesting a cost-effective alternative to traditional depth-sensor-required systems.

Cite This Study

Yang et al. (2025) studied this question.

synapsesocial.com/papers/68d43d75713b0b5dfea7d74c https://doi.org/10.3390/sym17091576

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Implication

This research introduces MSGS-SLAM which enhances semantic understanding in robotics, suggesting a cost-effective alternative to traditional depth-sensor-required systems.

MSGS-SLAM: Monocular Semantic Gaussian Splatting SLAM

Key Points

Discussion

Implication

Cite This Study

MSGS-SLAM: Monocular Semantic Gaussian Splatting SLAM

Key Points

Discussion

Implication

Cite This Study

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