Autonomous Implicit Indoor Scene Reconstruction with Frontier Exploration
This work addresses the challenge of efficient and complete indoor scene reconstruction for robotics applications, representing an incremental improvement over existing methods.
The paper tackles the problem of incomplete coverage and inefficiency in autonomous implicit 3D scene reconstruction by integrating frontier-based exploration with implicit surface uncertainty, resulting in the highest reconstruction quality among planning methods and superior planning efficiency.
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.