classified sliding window method, SLAM positioning method, system thereof and electronic equipment

Abstract

The invention discloses a classified sliding window method, an SLAM positioning method, a system thereof and electronic equipment. The classified sliding window method comprises the following steps: S110: determining whether the number of all observation frames in a window reaches the maximum window number of the window; s120, when the number of the observation frames reaches the maximum window number, according to the relative pose between the oldest first frame and the oldest second frame in the window, eliminating a preset number of observation frames from the window in batches at intervals; and S130, when the number of the observation frames is smaller than the maximum window number, further determining whether the number of the observation frames is larger than a preset frame number threshold value or not, and if yes, selectively rejecting the observation frames from the window according to the feature point tracking rate of the current observation frame, and if not, selectively rejecting the observation frames from the window according to the feature point tracking rate of the current observation frame; and if not, reserving all observation frames in the window.

Description

technical field [0001] The invention relates to the technical field of SLAM, in particular to a classification sliding window method, a SLAM positioning method, a system thereof, and an electronic device. Background technique [0002] With the continuous improvement of computing technology, intelligent technology and sensor technology, intelligent applications such as AR / VR, drones and intelligent robots have poured into the market, making positioning and map construction (Simultaneous Localization AndMapping, SLAM for short) technology also attracts attention . Usually, the SLAM problem can be basically divided into two parts: the front end and the back end: the front end mainly processes the data acquired by the sensor and converts it into a relative pose or a form that other machines can understand; The problem of empirical estimation, that is, the optimal estimation of pose or map. The current SLAM positioning technology generally uses visual inertial odometry (VIO) fo...

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Problems solved by technology

[0005] However, compared with EKF-SLAM, although the filter optimization method such as S-MSCKF has improved real-time performance and positioning accuracy, its positioning accuracy still cannot meet the requirements of applications such as AR / VR, and the complex motion conditions case, there will be an irreversible drift

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