单目相机实现大范围3D环境自主探索，解决稀疏深度与不确定性问题。
MonoSpheres: Large-Scale Monocular SLAM-Based UAV Exploration through Perception-Coupled Mapping and Planning
- 结合感知与规划，通过稀疏单目SLAM动态优化地图和路径。
- 在真实户外环境中完成大范围探索，首次实现单目3D自主导航。
- 适合无深度传感器的无人机或移动机器人研究者参考。
自主探索未知环境是移动机器人的关键能力，但仅配备单目相机且无密集测距传感器的机器人仍面临挑战。本文提出一种新型单目视觉探索方法，通过显式考虑稀疏单目SLAM前端特性，在映射与规划中协同处理深度数据稀疏、自由空间间隙及深度不确定性问题。映射模块在纹理稀疏区域过采样自由空间，并跟踪障碍物位置不确定性；规划模块通过快速重规划与感知导向的航向控制应对自由空间不确定性。研究还表明，在考虑视差要求与无纹理表面可能性的前提下，基于前沿的探索可在稀疏单目深度数据下实现。我们在多样的真实世界与仿真环境中进行了广泛评估，包括消融实验。据作者所知，该方法是首个在真实非结构化室外环境中实现3D单目探索的方法。代码已开源，以支持后续研究。
原文摘要 · Abstract (English)
Autonomous exploration of unknown environments is a key capability for mobile robots, but it is largely unsolved for robots equipped with only a single monocular camera and no dense range sensors. In this paper, we present a novel approach to monocular vision-based exploration that can safely cover large-scale unstructured indoor and outdoor 3D environments by explicitly accounting for the properties of a sparse monocular SLAM frontend in both mapping and planning. The mapping module solves the problems of sparse depth data, free-space gaps, and large depth uncertainty by oversampling free space in texture-sparse areas and keeping track of obstacle position uncertainty. The planning module handles the added free-space uncertainty through rapid replanning and perception-aware heading control. We further show that frontier-based exploration is possible with sparse monocular depth data when parallax requirements and the possibility of textureless surfaces are taken into account. We evaluate our approach extensively in diverse real-world and simulated environments, including ablation studies. To the best of the authors' knowledge, the proposed method is the first to achieve 3D monocular exploration in real-world unstructured outdoor environments. We open-source our implementation to support future research.
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