Abstract
Jumping spiders (Salticidae) rely on accurate depth perception for predation and navigation. They accomplish depth perception, despite their tiny brains, by using specialized optics. Each principal eye includes a multitiered retina that simultaneously receives multiple images with different amounts of defocus, and from these images, distance is decoded with relatively little computation. We introduce a compact depth sensor that is inspired by the jumping spider. It combines metalens optics, which modifies the phase of incident light at a subwavelength scale, with efficient computations to measure depth from image defocus. Instead of using a multitiered retina to transduce multiple simultaneous images, the sensor uses a metalens to split the light that passes through an aperture and concurrently form 2 differently defocused images at distinct regions of a single planar photosensor. We demonstrate a system that deploys a 3-mm-diameter metalens to measure depth over a 10-cm distance range, using fewer than 700 floating point operations per output pixel. Compared with previous passive depth sensors, our metalens depth sensor is compact, single-shot, and requires a small amount of computation. This integration of nanophotonics and efficient computation brings artificial depth sensing closer to being feasible on millimeter-scale, microwatts platforms such as microrobots and microsensor networks.
Highlights
Contributed by Federico Capasso, September 24, 2019
We model the image I (x, y) formed on a photosensor as the convolution of the camera point spread function (PSF) with the magnified, all-in-focus object pattern as it would be observed with a pinhole camera
The blur change between the 2 images can be seen in Fig. 4A, which shows PSFs for each of the 2 images [I+(x, y), I−(x, y)] that were measured by using a green light-emitting diode (LED) mated to a 10-μm-diameter pinhole and placed at different depths Z along the optical axis
Summary
∂I (x ,y) ∂σ [18, 19]. , where δI (x , y) is the change of image intensity induced by a small, known variation of the PSF width (δσ). From these 2 images (Fig. 2 C, Left), we compute the per-pixel difference δI (x , y) = I+(x , y) − I−(x , y) and the image Laplacian ∇2I (x , y). Even with filtering, random noise in the captured images (I+(x , y), I−(x , y)) results in errors in the measured depth Z (x , y). With constants γ1, γ2, γ3 that are determined by the optics (SI Appendix, section 1.2) This measurable quantity sZ (x , y) can serve as an indicator of the reliability of the measured depth Z (x , y) at each pixel (x , y). A higher confidence value C at pixel location (x , y) indicates a smaller value of sZ and a more accurate depth measurement Z (SI Appendix, section 1.2). The depth map is thresholded by confidence to show only the depth at pixels where the latter is greater than 0.5
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