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.

通过引入基于前沿探索任务的全局覆盖和基于隐式表面不确定性的重建任务，结合使用颜色不确定性实现隐式表面不确定性的方法，提出了一种自适应策略来在视角路径规划中切换模式，以减少时间并保持卓越的重建质量。该方法在所有规划方法中展示了最高的重建质量和在涉及重建任务的方法中的优越规划效率。我们在一架无人机上部署了我们的方法，并且结果显示我们的方法可以计划多任务视图并以高质量重建场景。

自主隐式室内场景重建与边界探索

Autonomous Implicit Indoor Scene Reconstruction with Frontier  Exploration

Active Simultaneous Localisation and Mapping (SLAM) is a critical problem in
autonomous robotics, enabling robots to navigate to new regions while building
an accurate model of their surroundings. Visual SLAM is a popular technique
that uses virtual elements to enhance the experience. However, existing
frontier-based exploration strategies can lead to a non-optimal path in
scenarios where there are multiple frontiers with similar distance. This issue
can impact the efficiency and accuracy of Visual SLAM, which is crucial for a
wide range of robotic applications, such as search and rescue, exploration, and
mapping. To address this issue, this research combines both an existing
Visual-Graph SLAM known as ExploreORB with reinforcement learning. The proposed
algorithm allows the robot to learn and optimize exploration routes through a
reward-based system to create an accurate map of the environment with proper
frontier selection. Frontier-based exploration is used to detect unexplored
areas, while reinforcement learning optimizes the robot's movement by assigning
rewards for optimal frontier points. Graph SLAM is then used to integrate the
robot's sensory data and build an accurate map of the environment. The proposed
algorithm aims to improve the efficiency and accuracy of ExploreORB by
optimizing the exploration process of frontiers to build a more accurate map.
To evaluate the effectiveness of the proposed approach, experiments will be
conducted in various virtual environments using Gazebo, a robot simulation
software. Results of these experiments will be compared with existing methods
to demonstrate the potential of the proposed approach as an optimal solution
for SLAM in autonomous robotics.

本研究提出将探索 ORB 与强化学习方法相结合，通过奖励规则，优化 frontiers 的探测，提高 SLAM 在自主机器人的应用的精度和效率，并使用 Gazebo 进行了实验验证。