Flying insects employ elegant optical-flow-based strategies to solve complex tasks such as landing or obstacle avoidance. Roboticists have mimicked these strategies on flying robots with only limited success, because optical flow (1) cannot disentangle distance from velocity and (2) is less informative in the highly important flight direction. Here, we propose a solution to these fundamental shortcomings by having robots learn to estimate distances to objects by their visual appearance. The learning process obtains supervised targets from a stability-based distance estimation approach. We have successfully implemented the process on a small flying robot. For the task of landing, it results in faster, smooth landings. For the task of obstacle avoidance, it results in higher success rates at higher flight speeds. Our results yield improved robotic visual navigation capabilities and lead to a novel hypothesis on insect intelligence: behaviours that were described as optical-flow-based and hardwired actually benefit from learning processes.

飞行昆虫采用优雅的基于光流的策略来解决复杂的任务，例如着陆或避障。机器人学家在飞行机器人上模仿了这些策略，但成功有限，因为光流（1）无法解开距离与速度的距离，（2）在非常重要的飞行方向上信息量较少。在这里，我们提出了一个解决这些基本缺点的方法，让机器人学会通过视觉外观估计到物体的距离。学习过程从基于稳定性的距离估计方法中获得监督目标。我们已经成功地在一个小型飞行机器人上实施了这个过程。对于着陆任务，它会导致更快、更平稳的着陆。对于避障任务，它会在更高的飞行速度下获得更高的成功率。