Huawei Technologies Co., Limited

Yajun Gao

Wanlong Li

Jianfei Li

Feng Wen

Xueshi Li

This disclosure provides a moving object tracking method and apparatus. The method includes: obtaining a current frame captured by a camera; predicting a current state vector of the camera based on an inertial measurement unit IMU and the current frame, to obtain a predicted value of the current state vector of the camera; predicting a current state vector of a target object that is moving in the current frame, to obtain a predicted value of the current state vector of the target object; and updating a Kalman state vector based on a measurement result of an image feature in the current frame. According to technical solutions provided in this disclosure, a target object that is moving in a surrounding environment can be tracked and a pose of the target object can be estimated while a pose of a system can be estimated.

Moving object tracking method and apparatus

Systems and methods of visual simultaneous localization and mapping (SLAM) based on point and line features are disclosed. The method includes: receiving a visual image frame input by a camera device; extracting feature points and feature lines from the visual image frame; predicting a first pose of the camera device using the feature points; observing an extracted first feature line to determine a feature line measurement of the first feature line; obtaining a global feature line state vector set of the camera device, where the global feature line state vector set includes feature line state vectors of N historical feature lines, and N is a positive integer; and updating the first pose using the feature line measurement and the global feature line state vector set. In this way, feature point-based motion estimations and observation features of feature lines observed in an environment are integrated to update a pose of the camera device in real time, thereby improving visual SLAM accuracy.

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Beijing, China

Yajun Gao