Huawei Technologies Co., Limited

Ubtech Robotics Corp Ltd

Zte Corporation

Ocean's King Lighting Science and Technology Co., Ltd.

Xi Luo

Youjun Xiong

Mingjie Zhou

Wenbo Ma

Liming Wang

Huan Yun Zhang

Ping Song

Mingguo Zhao

Yugang Liu

Qiang Zhou

Li Cheng

Qingtao Li

Jing Yang

Jing Tang

Yan Qi

Tian Tian

Sotirios Stasinopoulos

Yixin Li

A scene data obtaining method as well as a model training method and a computer readable storage medium using the same are provided. The method includes: building a virtual simulation scene corresponding to an actual scene, where the virtual simulation scene is three-dimensional; determining a view frustum corresponding to preset view angles in the virtual simulation scene; collecting one or more two-dimensional images in the virtual simulation scene and ground truth object data associated with the one or more two-dimensional images using the view frustum corresponding to the preset view angles; and using all the two-dimensional images and the ground truth object data associated with the one or more two-dimensional images as scene data corresponding to the actual scene. In this manner, the data collection does not require manual annotation, and the obtained data can be used for training deep learning-based perceptual models.

Scene data obtaining method and model training method, apparatus and computer readable storage medium using the same

The present disclosure provides a map generation method, localization method, and simultaneous localization and mapping method. The method includes: recognizing fiducial markers in the motion area; taking a position as origin of a global coordinate system of the robot, and obtaining pose information of the fiducial markers; the robot moving to a next position, recognizing the fiducial markers with determined coordinate information and underdetermined coordinate information respectively, and obtaining pose information of the fiducial marker of the undetermined coordinate information with respect to the origin based on that of the determined coordinate information; repeating the previous step until the pose information of all the fiducial markers are obtained; and generating a marker map associated with coordinate information of all fiducial markers. The method is capable of generating a map of the motion area through the fiducial markers and further realizing autonomous localization.

Simultaneous localization and mapping methods of mobile robot in motion area

Embodiments of the present invention provide a method and an apparatus for optimizing a neighbor relation. The method for optimizing a neighbor relation in the present invention includes: determining, by a centralized device, to-be-processed neighboring cells according to a preset rule, generating an optimization suggestion according to the determined to-be-processed neighboring cells, and delivering the optimization suggestion to a radio network controller RNC, so that the RNC optimizes the to-be-processed neighboring cells according to the optimization suggestion. According to the embodiments of the present invention, automatic optimization of the neighbor relation is implemented.

Method and apparatus for optimizing neighbor relation

Embodiments of the present invention relate to a method for determining a scrambling code conflict and an apparatus for determining a scrambling code conflict. The method includes: determining a serving cell before a handover of a user equipment fails, and determining a current serving cell of the user equipment; and comparing frequencies and scrambling codes of the current serving cell and a neighboring cell of the serving cell before the handover fails, and determining, according to a result of the comparing, that a scrambling code conflict exists between the current serving cell and the neighboring cell of the serving cell before the handover fails. A probability of a handover failure of a user equipment is reduced, so as to ensure that a service of the user equipment runs normally.

Method for determining scrambling code conflict and apparatus for determining scrambling code conflict

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Shenzhen, China

Xi Luo