FLIVVER: Fly Lobula Inspired Visual Velocity Estimation & Ranging
This addresses a critical challenge in bio-inspired robotics for enabling autonomous navigation in small-scale systems, though it is incremental as it builds on existing optic flow and SLAM concepts.
The paper tackles the problem of estimating absolute velocity and distance from optic flow for insects or insect-sized robots, presenting FLIVVER, a novel algorithm that combines geometry and insect-inspired processing to directly estimate absolute ground velocity, offering a hypothesis for insect capabilities and a framework for efficient robotic state estimation.
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.