In complex missions such as search and rescue,robots must make intelligent decisions in unknown environments, relying on their ability to perceive and understand their surroundings. High-quality and real-time reconstruction enhances situational awareness and is crucial for intelligent robotics. Traditional methods often struggle with poor scene representation or are too slow for real-time use. Inspired by the efficacy of 3D Gaussian Splatting (3DGS), we propose a hierarchical planning framework for fast and high-fidelity active reconstruction. Our method evaluates completion and quality gain to adaptively guide reconstruction, integrating global and local planning for efficiency. Experiments in simulated and real-world environments show our approach outperforms existing real-time methods.
@article{arxiv.2409.17624,
title = {HGS-Planner: Hierarchical Planning Framework for Active Scene Reconstruction Using 3D Gaussian Splatting},
author = {Zijun Xu and Rui Jin and Ke Wu and Yi Zhao and Zhiwei Zhang and Jieru Zhao and Fei Gao and Zhongxue Gan and Wenchao Ding},
journal= {arXiv preprint arXiv:2409.17624},
year = {2024}
}