Ubtech Robotics Corp Ltd

Chenchen Jiang

Youjun Xiong

Zhichao Liu

Jianxin Pang

Rui Guo

Yongsheng Zhao

Kun Xie

Simin Zhang

Longbiao Bai

Jian Zhang

Yu Tang

Zhanjia Bi

Hongjian Liu

The present disclosure provides a robot mapping method as well as a robot and a computer readable storage medium using the same. The method includes: detecting a marker with identification information capable of being identified by the robot in a current scene; determining whether the detected marker meets a preset condition; and mapping the current scene based on the marker, if the detected marker meets the preset condition. The robot mapping method can not only map the current scene, but also effectively reduce the difficulty of loops and the number of false loops.

Robot mapping method and robot and computer readable storage medium using the same

The present disclosure provides an autonomous mobile apparatus and a control method thereof. The method includes: starting a SLAM mode; obtaining first image data captured by a first camera; extracting a first tag image of positioning tag(s) from the first image data; calculating a three-dimensional camera coordinate of feature points of the positioning tag(s) in a first camera coordinate system of the first camera based on the first tag image; calculating a three-dimensional world coordinate of the feature points of the positioning tag(s) in a world coordinate system based on a first camera pose of the first camera when obtaining the first image data in the world coordinate system and the three-dimensional camera coordinate; and generating a map file based on the three-dimensional world coordinate of the feature points of the positioning tag(s).

Autonomous mobile apparatus and control method thereof

A loop closure detection method, a mobile device, and a computer readable storage medium are provided. The method includes: collecting images in different detection directions simultaneously through C0 cameras installed on the mobile device to obtain an image data group comprising C0 images; calculating feature information of each image in the image data group; performing a loop closure detection in C0 sub-threads respectively based on the feature information to obtain a loop closure detection result of each sub-thread; and determining a loop closure detection result of the mobile device based on the loop closure detection result of each sub-thread. In this manner, cross detections in a plurality of detection directions can be realized, which breaks through the limitation of loop closure detection in the prior art with respect to path direction, avoids repeated paths in the same direction, and greatly improves the mapping efficiency.

Loop closure detection method, mobile device and computer readable storage medium

The present disclosure provides a localization method as well as a helmet and a computer readable storage medium using the same. The method includes: extracting first feature points from a target image; obtaining inertial information of the carrier, and screening the first feature points based on the inertial information to obtain second feature points; triangulating the second feature points of the target image to generate corresponding initial three-dimensional map points, if the target image is a key frame image; performing a localization error loopback calibration on the initial three-dimensional map points according to at least a predetermined constraint condition to obtain target three-dimensional map points; and determining a positional point of the specific carrier according to the target three-dimensional map points. In this manner, the accuracy of the localization of a dynamic object such as a person when moving can be improved.

Localization method and helmet and computer readable storage medium using the same

The present disclosure provides a robot visual image feature extraction method as well as an apparatus and a robot using the same. The method includes: collecting image data through visual sensor(s) of the robot, and collecting angular velocity data through inertial sensor(s) of the robot; calculating a relative pose between image frames in the image data based on the angular velocity data; extracting feature points of the first image frame in the image data; calculating a projection position of each feature point of the k-th image frame in the k+1-th image frame based on a relative pose between the k-th image frame and the k+1-th image frame; and searching for each feature point in the projection position in the k+1-th image frame, and performing a synchronous positioning and a mapping based on the searched feature point. In this manner, the feature points of dynamic objects are eliminated.

Robot vision image feature extraction method and apparatus and robot using the same

The present disclosure provides a virtual rail based cruise method as well as an apparatus and a robot using the same. The method includes: obtaining a digital map including a virtual rail; performing a path planning based on the virtual rail, a current position of the robot, and a cruise end point to obtain a cruise path; and obtaining parameter(s) of the robot by calculating through a preset path tracking algorithm based on the cruise path and the current position of the robot, and controlling the robot based on the control parameter(s). In this manner, the problems of the prior art that needs to lay a rail or set an auxiliary device which causes high cost and inconvenience in usage as well as the rail needs to be re-laid or the auxiliary device needs to be reinstalled when the route is to be changed can be solved.

Virtual rail based cruise method and apparatus and robot using the same

The present disclosure provides a positioning method and a robot using the same. The method includes: obtaining, through the visual sensor, a current frame image; obtaining, through the ultra-wideband tag, distance of a robot from an ultra-wideband anchor; performing a feature matching on the current frame image and an adjacent frame image to generate partial map point(s); determining whether the current frame image is a key frame image; and optimizing a pose of the visual sensor corresponding to the key frame image through a joint objective function in response to the current frame image being the key frame image, where the joint objective function at least comprises a distance cost function of the ultra-wideband anchor and a visual residual cost function. Through the above-mentioned method, the accuracy of the positioning of the robot can be improved.

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Shenzhen, China

Chenchen Jiang