Goertek Inc.

Baoming Li

Libing Zou

Yifan Zhang

Tianrong Dai

Shunran Di

Xueqiang Wang

Fuqiang Zhang

Yue Ning

Jinxi Cao

Shanshan Min

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.

Method for filtering image feature points and terminal

The present application discloses a method and device for updating a map. The method for updating a map according to the present embodiment includes: in a process of movement of a robot, when it is detected that an actual environment is different from an environment that is indicated by a global map that has already been established, starting up map updating, and establishing an initial local map; determining a locating point according to acquired sensor data and the global map, and optimizing the initial local map according to the locating point, to obtain an optimized local map; and covering a corresponding area of the global map by using the optimized local map, to complete updating of the global map. The embodiments of the present application improve the locating accuracy, ensure the speed and efficiency of the map updating, and save time.

Method and device for updating map

A global path planning method and device for an unmanned vehicle are disclosed. The method comprises: establishing an object model through a reinforcement learning method, wherein the object model includes: a state of the unmanned vehicle, an environmental state described by a map picture, and an evaluation index of a path planning result; building a deep reinforcement learning neural network based on the object model established, to obtain a stable neural network model; inputting the map picture of the environment state and the state of the unmanned vehicle into the deep reinforcement learning neural network after trained, and generating a motion path of the unmanned vehicle. According to the present disclosure, the environment information in the scene is marked through the map picture, and the map features are extracted through the deep neural network, thereby simplifying the modeling process of the map scene.

Global path planning method and device for an unmanned vehicle

The present disclosure discloses a path planning method and device and a mobile device. The method comprises: collecting environmental information in a viewing angle by a sensor of a mobile device, processing the environmental information by using an SLAM algorithm, and constructing a grid map; dividing the grid map to obtain a plurality of pixel blocks, using an area constituted of pixel blocks not occupied by obstacles as a search area for path planning, and obtaining a processed grid map; determining reference points by using pixel points in the search area, and deploying topological points on the processed grid map according to the reference point determined and constructing a topological map; and calculating an optimal path from a starting point to a preset target point by using a predetermined algorithm according to the topological map constructed. The present disclosure improves path planning efficiency and saves storage resources.

Path planning method and device and mobile device

A method and device for extracting key frames in simultaneous localization and mapping and a smart device. The method includes acquiring an image frame from an image library storing a plurality of image frames of an unknown environment, and performing feature extraction on the image frame to obtain information of feature points, wherein the information includes a quantity of feature points; acquiring relative motion information of the image frame relative to the previous key frame, and calculating an adaptive threshold currently used by using the relative motion information; and selecting a key frame according to the information of feature points and the adaptive threshold indicating space information of image frames.

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Weifang, China

Baoming Li