A major challenge in deploying the smallest of Micro Aerial Vehicle (MAV) platforms (< 100 g) is their inability to carry sensors that provide high-resolution metric depth information (e.g., LiDAR or stereo cameras). Current systems rely on end-to-end learning or heuristic approaches that directly map images to control inputs, and struggle to fly fast in unknown environments. In this work, we ask the following question: using only a monocular camera, optical odometry, and offboard computation, can we create metrically accurate maps to leverage the powerful path planning and navigation approaches employed by larger state-of-the-art robotic systems to achieve robust autonomy in unknown environments? We present MonoNav: a fast 3D reconstruction and navigation stack for MAVs that leverages recent advances in depth prediction neural networks to enable metrically accurate 3D scene reconstruction from a stream of monocular images and poses. MonoNav uses off-the-shelf pre-trained monocular depth estimation and fusion techniques to construct a map, then searches over motion primitives to plan a collision-free trajectory to the goal. In extensive hardware experiments, we demonstrate how MonoNav enables the Crazyflie (a 37 g MAV) to navigate fast (0.5 m/s) in cluttered indoor environments. We evaluate MonoNav against a state-of-the-art end-to-end approach, and find that the collision rate in navigation is significantly reduced (by a factor of 4). This increased safety comes at the cost of conservatism in terms of a 22% reduction in goal completion.

翻译：在部署超小型微型飞行器（<100克）时，其主要挑战在于无法搭载提供高分辨率度量深度信息（如激光雷达或立体相机）的传感器。现有系统依赖端到端学习或启发式方法，直接映射图像到控制输入，但在未知环境中难以实现快速飞行。本研究提出以下问题：仅使用单目相机、光学里程计和机外计算，能否构建度量精确的地图，以利用大型先进机器人系统中强大的路径规划与导航方法，在未知环境中实现稳健自主性？我们提出MonoNav：一种面向微型飞行器的快速三维重建与导航架构，利用深度预测神经网络的最新进展，通过单目图像与位姿流实现度量精确的三维场景重建。MonoNav采用现成的预训练单目深度估计与融合技术构建地图，随后通过搜索运动基元规划无碰撞轨迹至目标点。在大量硬件实验中，我们验证了MonoNav如何使Crazyflie（37克微型飞行器）在杂乱室内环境中实现快速导航（0.5米/秒）。与先进端到端方法的对比表明，导航碰撞率显著降低（降低4倍），这种安全性提升以目标完成率降低22%的保守性为代价。