Abstract: Disclosed is a method for constructing a bionic binocular active visual simultaneous localization and mapping (SLAM) system. The method includes: S1, constructing a binocular active visual system, peripheral vision is simulated using a panoramic camera, a panoramic value map is constructed based on the panoramic camera, and a binocular bionic eye gaze control strategy based on the panoramic value map is proposed; S2, designing a SLAM algorithm framework which is suitable for motion of the two bionic eyes, where relative pose optimization of left and right cameras based on a Perspective-n-Point algorithm and depth calculation for binocular matching points based on triangulation are adopted in a SLAM tracking thread; and S3, with the assistance of camera poses estimated by the SLAM system and sparse landmark point coordinates, training a self-supervised depth estimation network, to provide dense depth estimation for a mapping module of the SLAM system, achieving dense mapping.

Abstract: The present disclosure provides an auto focus (AF) method and system for an electro-hydraulic (EH) lens with an adjustable focus, and an electronic device, and relates to the field of lens focusing technologies. The method mainly includes: determining a state space, an action space, and a reward function of a reinforcement learning (RL) method; constructing sample data based on the foregoing determined state space, action space, and reward function, and storing the sample data in an experience pool; and using sample data in the experience pool as training data of a deep neural network (DNN) architecture when there are M sets of the sample data in the experience pool, and obtaining a trained AF policy with reference to a deep deterministic policy gradient (DDPG) algorithm and a single hill climbing optimization (HCO) algorithm.

Abstract: Disclosed is an active positioning method for bionic binocular vision during execution of a simultaneous localization and mapping (SLAM) task. The method includes: capturing a panoramic image using a panoramic camera and detecting key scene information; assigning values to pixels in the panoramic image to obtain a panoramic value map; projecting field-of-view areas of left and right bionic eye cameras separately onto the panoramic value map, to obtain a current binocular field-of-view projection area; calculating a mean value of the current binocular field-of-view projection area in the panoramic value map; comparing the mean value of the current binocular field-of-view projection area with a value threshold, and obtaining a binocular field-of-view projection area with a mean value higher than the value threshold by moving the binocular bionic cameras; and finally using a high-value image captured by the left and right bionic eye cameras as an input of a SLAM system.