公开(公告)号：US20220254146A1

公开(公告)日：2022-08-11

申请号：US17595079

申请日：2020-10-30

Applicant: GOERTEK INC.

Inventor： Libing ZOU ,  Yifan ZHANG ,  Baoming LI ,  Yue NING

IPC: G06V10/774 ,  G06V10/46 ,  G06N3/08

Abstract: The present application discloses a method for filtering image feature points and a terminal. The method for filtering image feature points includes: providing quality score values to feature points extracted from an image, and according to the feature points and the quality score values of the feature points, training a neural-network model; after one time of filtering has started up, acquiring one frame of an original image and extracting feature points in the original image; inputting the original image and the feature points in the original image into the neural-network model, obtaining and outputting quality score values corresponding to the feature points in the original image; and according to the quality score values, filtering the feature points in the original image. The method for filtering image feature points can improve the success rate of the matching of the feature points in relocated application scenes, thereby improving the locating efficiency.

公开(公告)号：US20220113420A1

公开(公告)日：2022-04-14

申请号：US17309923

申请日：2020-10-24

Applicant: GOERTEK INC.

Inventor： Yue NING ,  Yifan ZHANG ,  Libing ZOU ,  Fuqiang ZHANG

IPC: G01S17/89 ,  G06N20/20

Abstract: A plane detection method and device based on a laser sensor are disclosed. The method includes: acquiring data of the laser sensor after starting detection; inputting the data into a detection model trained in advance, wherein the detection model is obtained by training with data corresponding to a medium type selected in advance and is capable of recognizing the medium type selected; judging whether an object to which the data belongs is a plane, and if the object is a plane, determining the medium type of the plane; and setting corresponding optimization methods for different medium types, and optimizing the data according to the medium type. The laser sensor recognizes the medium type by the machine learning model, and optimizes the two-dimensional laser data according to the recognition results, and thus forms a more refined map and performs more accurate positioning based on the two-dimensional laser data.