A metalens is the flat and ultrathin surface made of billions of sub-wavelength elements and can bend light like traditional optical lens, which make it attract enormous interests. However, two fundamental issues need to be addressed before metalens can replace traditional lens. First, a large diameter (up-to-cm) metalens is extremely difficult to simulate and optimize, due to the large area, lack of periodicity, and multiple parameters on billions of sub-wavelength elements. Second, to obtain a high-quality image within certain distance, a variable focus length is highly desired in most of modern optical system. In this work, we develop an analytical model based on an optical phased array antenna with the focusing phase profile, and accurately predict the far field radiation pattern for a large-area metalens with significant low computational cost. The beam-width of system and depth-of-focus (DOF) are given with respect to wavelength, element spacing and aperture size. To realize the focus tuning function on silicon metalens, the cascaded PIN junction phase shifters enhanced by Fabry-Perrot cavity are attached to metalens to enable the 2π phase variation. At last, a silicon metalens with a diameter of 400um and a focus of 93um, at 1.55um wavelength is verified in FDTD simulation. The results show that the beam-width, DOF and focus tuning range agree well with the analytical model result. The f/10 axial displacement is achieved with a carrier injection of 1019 cm-3. This focus tuning mechanism could be deployed to many attractive near-infrared applications, such as fluorescence microscopy and LIDAR.
For robots used for the indoor environment detection, positioning and navigation with a Light Detection and Ranging system (Lidar), the accuracy of map building, positioning and navigation, is largely restricted by the motion accuracy. Due to manufacture error and transmission error of the mechanical structure, sensors easily affected by the environment and other factors, robots’ cumulative motion error is inevitable. This paper presents a series of methods and solutions to overcome those problems, such as point set partition and feature extraction methods for processing Lidar scan points, feature matching method to correct the motion process, with less computation, more reasonable and rigorous threshold, wider scope of application, higher efficiency and accuracy. While extracting environment features and building indoor maps, these methods analyze the motion error of the robot and correct it, improving the accuracy of movement and map without any additional hardware. Experiments prove that the rotation error and translation error of the robot platform used in experiments can by reduced by 50% and by 70% respectively. The methods evidently improve the motion accuracy with a strong effectiveness and practicality.
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