Insects rely on the perception of image motion, or optic flow, to estimate their velocity relative to nearby objects. This information provides important sensory input for avoiding obstacles. However, certain behaviors, such as estimating the absolute distance to a landing target, accurately measuring absolute distance traveled, and estimating the ambient wind speed require decoupling optic flow into its component parts: absolute ground velocity and distance to nearby objects. Behavioral experiments suggest that insects perform these calculations, but their mechanism for doing so remains unknown. Here we present a novel algorithm that combines the geometry of dynamic forward motion with known features of insect visual processing to provide a hypothesis for how insects might directly estimate absolute ground velocity from a combination of optic flow and acceleration information. Our robotics-inspired-biology approach reveals three critical requirements. First, absolute ground velocity can only be directly estimated from optic flow during times of active acceleration and deceleration. Second, spatial pooling of optic flow across a receptive field helps to alleviate the effects of noise and/or low resolution visual systems. Third, averaging velocity estimates from multiple receptive fields further helps to reject noise. Our algorithm provides a hypothesis for how insects might estimate absolute velocity from vision during active maneuvers, and also provides a theoretical framework for designing fast analog circuitry for efficient state estimation that can be applied to insect-sized robots.

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Odor plumes in turbulent environments are intermittent and sparse, yet many animals effectively navigate to odor sources to find food [1]. Computational and lab-scaled experiments [2, 3, 4] have suggested that information about the source distance may be encoded in odor signal statistics, yet it is unclear whether useful and continuous distance estimates can be made under real-world flow conditions. Here we analyze odor signals from outdoor experiments with a sensor moving across large spatial scales in desert and forest environments to show that odor signal statistics can yield useful estimates of distance. The probability distribution of statistics from individual whiffs (contiguous odor sequences) are correlated with distance, but their correlation coefficient is poor. However, we show that a useful estimate of distance can be found by incorporating whiff statistics from time history of ∼10-seconds, resulting in a strong correlation ofR2∼0.70. We identified whiff concentration and duration to be the most informative features. By combining distance estimates from a linear model with wind-relative motion dynamics, we were able to estimate source distance in a 60×60 m2area with median errors of 3-8 meters, a distance at which point odor sources are within visual range for animals such as mosquitoes [5, 6]. Finally, we show that such estimates are only feasible if odor signal information is recorded at temporal resolutions of≥20 Hz. Together, our results provide a compelling case for how odor signal statistics could be used by animals, or man-made machines, to optimize plume tracking decisions at large spatial scales with natural wind.

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