GS-LTS: 3D Gaussian Splatting-Based Adaptive Modeling for Long-Term Service Robots

3D Gaussian Splatting (3DGS) has garnered significant attention in robotics for its explicit, high fidelity dense scene representation, demonstrating strong potential for robotic applications. However, 3DGS-based methods in robotics primarily focus on static scenes, with limited attention to the dynamic scene changes essential for long-term service robots. These robots demand sustained task execution and efficient scene updates-challenges current approaches fail to meet. To address these limitations, we propose GS-LTS (Gaussian Splatting for Long-Term Service), a 3DGS-based system enabling indoor robots to manage diverse tasks in dynamic environments over time. GS-LTS detects scene changes (e.g., object addition or removal) via single-image change detection, employs a rule-based policy to autonomously collect multi-view observations, and efficiently updates the scene representation through Gaussian editing. Additionally, we propose a simulation-based benchmark that automatically generates scene change data as compact configuration scripts, providing a standardized, user-friendly evaluation benchmark. Experimental results demonstrate GS-LTS's advantages in reconstruction, navigation, and superior scene updates-faster and higher quality than the image training baseline-advancing 3DGS for long-term robotic operations. Code and benchmark are available at:this https URL.

@article{fu2025_2503.17733,
  title={ GS-LTS: 3D Gaussian Splatting-Based Adaptive Modeling for Long-Term Service Robots },
  author={ Bin Fu and Jialin Li and Bin Zhang and Ruiping Wang and Xilin Chen },
  journal={arXiv preprint arXiv:2503.17733},
  year={ 2025 }
}