Abstract: The method includes: determining a reference sensor from at least two sensors and sampling moments of the reference sensor; when obtaining observation data of a sensor K at a moment t1, determining a type of the sensor K; if the sensor K is a first sensor of a first type, determining a sampling moment T1 that is closest to t1 and an interpolation coefficient ?1, and calculating first rotation data and first translation data that are at T1, and a first covariance matrix; if the sensor K is a second sensor of a second type, determining two sampling moments T2 and T3 that are closest to t1 and interpolation coefficients ?2 and ?3, and calculating second rotation data and second translation data that are of the second sensor from T2 to T3, and a second covariance matrix; merging rotation data, translation data, and covariance matrices that are at the sampling moments.

Abstract: 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.

Abstract: 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.

Abstract: The method includes: determining a reference sensor from at least two sensors and sampling moments of the reference sensor; when obtaining observation data of a sensor K at a moment t1, determining a type of the sensor K; if the sensor K is a first sensor of a first type, determining a sampling moment T1 that is closest to t1 and an interpolation coefficient ?1, and calculating first rotation data and first translation data that are at T1, and a first covariance matrix; if the sensor K is a second sensor of a second type, determining two sampling moments T2 and T3 that are closest to t1 and interpolation coefficients ?2 and ?3, and calculating second rotation data and second translation data that are of the second sensor from T2 to T3, and a second covariance matrix; merging rotation data, translation data, and covariance matrices that are at the sampling moments.

Abstract: The method is: receiving a visual image frame input by a camera; extracting feature points and feature lines of the visual image frame; predicting a first pose of the camera device by 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 by 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.

Abstract: A method and device for storage are provided in embodiments of the present invention. The method includes: acquiring a storage position of a table entry in a hierarchical binary tree that includes a first-part binary tree and second-part binary trees, wherein nodes of the first-part binary tree point to the second-part binary trees through pointers; and inserting the table entry to the hierarchical binary tree according to the storage position. In the embodiments of the present invention, the hierarchical binary tree structure with pointers is adopted to store table entry content, reducing the times of moving table entries and greatly improving the algorithm refresh rate.

Abstract: A method and device for storage are provided in embodiments of the present invention. The method includes: acquiring a storage position of a table entry in a hierarchical binary tree that includes a first-part binary tree and second-part binary trees, wherein nodes of the first-part binary tree point to the second-part binary trees through pointers; and inserting the table entry to the hierarchical binary tree according to the storage position. In the embodiments of the present invention, the hierarchical binary tree structure with pointers is adopted to store table entry content, reducing the times of moving table entries and greatly improving the algorithm refresh rate.