Navigation using a monocular camera is pivotal for autonomous operation on tiny aerial robots due to their perfect balance of versatility, cost and accuracy. In this paper, we introduce MinNav, a navigation stack based on optical flow and its uncertainty to fly through a scene with static and dynamic obstacles and unknown-shaped gaps without any prior knowledge of the scene components and/or their locations/ordering. We further improve success rate by using the activeness of the robot to move around in an exploratory way to find obstacles and navigate. We successfully evaluate and demonstrate the proposed approach in many real-world experiments in various environments with static and dynamic obstacles and unknown-shaped gaps with an overall success rate of 70%. To the best of our knowledge, this is the first solution to tackle all the aforementioned navigation cases without prior knowledge using a monocular camera. Our approach is on par in performance with depth based methods with factors of magnitude less computation required and can readily run onboard tiny aerial robots. The accompanying video, supplementary material, code and dataset can be found at https://pear.wpi.edu/research/minnav.html

翻译：摘要：单目相机导航因其在多功能性、成本和精度间的完美平衡，成为微型自主飞行机器人自主运行的关键技术。本文提出MinNav导航栈——一种基于光流及其不确定性的导航系统，无需任何场景组件及其位置/顺序的先验知识，即可穿越包含静态与动态障碍物及未知形状间隙的场景。我们进一步通过利用机器人的主动性进行探索性运动以寻找障碍物并实施导航，显著提升了任务成功率。通过大量真实环境实验（涵盖各类静态/动态障碍物及未知形状间隙场景），我们成功验证了该方法，总体成功率达70%。据我们所知，这是首个无需先验知识即可应对上述所有导航场景的单目相机解决方案。本方法性能与基于深度的方法相当，但计算量降低数个数量级，且可直接部署于微型空中机器人。配套视频、补充材料、代码及数据集详见https://pear.wpi.edu/research/minnav.html。