  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.
