IMU-based SLAM motion blur pose tracking algorithm

Abstract

The invention discloses an IMU-based SLAM motion blur pose tracking algorithm. The algorithm comprises the following steps of S1, performing ORB feature extraction on an input image sequence, judgingthe type of the image sequence according to the quantity of feature points, and according to a judgment result, alternatively performing the step S2 or S3; S2, if the image sequence is a normal image,estimating an initial pose of a camera by utilizing a uniform motion model, and executing bundle adjustment of motion parameters; and S3, if the image sequence is a motion blur image, obtaining an estimated pose by utilizing an IMU motion equation, obtaining an optimized pose by utilizing an extended Kalman filter, and combining the estimated pose with the optimized pose to obtain a final pose ofthe camera. For the problem of loss due to the fact that SLAM cannot perform camera locating and tracking on a motion blur section in the image sequence, the camera pose is calculated and optimized in combination with and by utilizing a kinematical equation of an inertial measurement unit and the extended Kalman filter, so that the SLAM can obtain continuous and reliable camera pose locating andtracking.

Description

technical field [0001] The invention relates to a tracking algorithm, in particular to an IMU-based SLAM motion fuzzy pose tracking algorithm, belonging to the fields of computer vision and robots. Background technique [0002] Simultaneous localization and mapping (SLAM) is a research hotspot in the field of computer vision and robotics in recent years. SLAM technology can construct and update maps in unknown environments, and track and locate in real time. Early SLAM methods used filtering to solve problems. Davinson et al. proposed a real-time single-camera MonoSLAM method, which uses extended Kalman filtering as the backend to track very sparse feature points at the front end. Eade et al. proposed a scale-variable monocular SLAM method, which uses particle filtering and top-down search to draw a large number of landmarks in real time. Most of these methods use filters to process image frames to correlate and estimate the positions of map points and camera poses. Since ...

AI Technical Summary

Problems solved by technology

[0005] To sum up, how to provide an IMU-based SLAM motion blur pose tracking algorithm to solve the problem that SLAM cannot perform camera positioning and tracking loss on the motion blur segment in the image sequence has become the common expectation of technicians in the industry. one of the problems solved

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