Aiming at the problem that the rolling effect of rolling shutter camera will cause image distortion and then affect the positioning accuracy of the system,a visual inertial odometry with point transfer correction based on inertial measurement unit(IMU)information is proposed.Firstly,in view of the distortion problem of the rolling image,the point transfer method of the trifocal tensor is used to correct the rolling image,which is equivalent to the global image input to the system.Secondly,in order to ensure that the algorithm runs in real time on embedded hardware,extended Kalman filter is used for information fusion to improve the positioning accuracy of the system and reduce the requirement of computing resources.Then,static detection and anomaly detection are introduced to ensure the robustness of the system.Finally,the proposed algorithm is run in real time on a mobile phone at a frame rate of about 25 Hz,and the experiment is carried out in real environment.The experimental results on the public data sets show that compared with the RS-VSIN-MONO algorithm,the proposed algorithm improves the positioning accuracy by 27%,which verifies that the proposed algorithm can effectively integrate visual and inertial information to reduce the positioning error caused by the rolling curtain effect and improve the robustness of the system.
simultaneous localization and mappingvisual-inertial odometryrolling shutter cameraextended Kalman filter
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