Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration
<|reference_start|>Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration: Implicit neural representations have demonstrated significant promise for 3D scene reconstruction. Recent works have extended their applications to autonomous implicit reconstruction through the Next Best View (NBV) based method. However, the NBV method cannot guarantee complete scene coverage and often necessitates extensive viewpoint sampling, particularly in complex scenes. In the paper, we propose to 1) incorporate frontier-based exploration tasks for global coverage with implicit surface uncertainty-based reconstruction tasks to achieve high-quality reconstruction. and 2) introduce a method to achieve implicit surface uncertainty using color uncertainty, which reduces the time needed for view selection. Further with these two tasks, we propose an adaptive strategy for switching modes in view path planning, to reduce time and maintain superior reconstruction quality. Our method exhibits the highest reconstruction quality among all planning methods and superior planning efficiency in methods involving reconstruction tasks. We deploy our method on a UAV and the results show that our method can plan multi-task views and reconstruct a scene with high quality.<|reference_end|>
@article{zeng2024autonomous,
    title={Autonomous Implicit Indoor Scene Reconstruction with Frontier
  Exploration},
    author={Jing Zeng, Yanxu Li, Jiahao Sun, Qi Ye, Yunlong Ran, Jiming Chen},
    journal={IEEE International Conference on Robotics and Automation (ICRA
  2024)},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10218},
    primaryClass={cs.RO cs.AI}
}