FLIVVER：受苍蝇小叶启发的视觉速度估计和测距
FLIVVER: Fly Lobula Inspired Visual Velocity Estimation & Ranging
摘要：
一个微小的昆虫或昆虫大小的机器人能够估计其绝对速度和与附近物体的距离的机制仍然未知。然而，这种能力对于需要在飞行期间估计风向的行为至关重要，例如气味羽流跟踪。对昆虫的神经科学和行为研究表明，它们依赖于对图像运动或光流的感知来估计相对运动，相当于它们与世界上物体的速度和距离的比值。因此，关键的开放挑战是将这两种状态与它们的比率的单一测量解耦。尽管现代SLAM（同时定位与地图构建）方法为机器人系统提供了对该问题的解决方案，但是这些方法通常依赖于昆虫可能无法执行的计算，诸如同时跟踪多个个体视觉特征、记住世界的3D地图以及使用迭代算法来解决非线性优化问题。在这里，我们提出了一种新的算法，FLIVVER，它结合了动态向前运动的几何形状与灵感从昆虫视觉处理\textit{directly}估计绝对地面速度的光流和加速度信息的组合。我们的算法提供了一个明确的假设，昆虫可能会估计绝对速度，也提供了一个理论框架，设计快速的模拟电路，有效的状态估计，可应用于昆虫大小的机器人。
英文摘要：
The mechanism by which a tiny insect or insect-sized robot could estimate its absolute velocity and distance to nearby objects remains unknown. However, this ability is critical for behaviors that require estimating wind direction during flight, such as odor-plume tracking. Neuroscience and behavior studies with insects have shown that they rely on the perception of image motion, or optic flow, to estimate relative motion, equivalent to a ratio of their velocity and distance to objects in the world. The key open challenge is therefore to decouple these two states from a single measurement of their ratio. Although modern SLAM (Simultaneous Localization and Mapping) methods provide a solution to this problem for robotic systems, these methods typically rely on computations that insects likely cannot perform, such as simultaneously tracking multiple individual visual features, remembering a 3D map of the world, and solving nonlinear optimization problems using iterative algorithms. Here we present a novel algorithm, FLIVVER, which combines the geometry of dynamic forward motion with inspiration from insect visual processing to \textit{directly} estimate absolute ground velocity from a combination of optic flow and acceleration information. Our algorithm provides a clear hypothesis for how insects might estimate absolute velocity, and also provides a theoretical framework for designing fast analog circuitry for efficient state estimation, which could be applied to insect-sized robots.
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