Secondary loopback detection positioning method based on IMU and vision in similar environment

Abstract

The invention discloses a secondary loopback detection positioning method based on IMU and vision in a similar environment. The secondary loopback detection positioning method comprises the followingsteps of 1, calibrating and synchronizing parameters of a binocular camera and IMU, 2, image feature extraction and image matching, 3, pose estimation and movement track formation, 4, loopback detection, 5, a secondary loop-back detection mechanism, and 6, repositioning. According to the method, coarse comparison constraint can be performed on the current image frame pose through the IMU pose information, that is, direction consistency constraint pre-judgment is performed by comparing the IMU current position direction with the primary closed-loop image position direction, surrounding scene similarity is prevented, a closed loop can be judged according to the image similarity, according to the problem that the current actual position of the IMU cannot be the same position, wrong loopback is caused, positioning errors caused by similar images in a similar environment are prevented through direction pre-judgment of the IMU, the updated high-precision pose after repositioning is used forcorrecting the IMU accumulated drift error, and the robustness of the secondary loopback detection positioning method is improved.

Description

technical field [0001] The invention relates to a secondary loop detection and positioning method, in particular to a secondary loop detection and positioning method based on IMU and vision in a similar environment. Background technique [0002] UAVs are developing in the direction of intelligent UAVs and intelligent systems, and the ability of autonomous positioning and navigation determines the level of intelligence of UAVs. Unmanned aerial vehicle (UAV) puts forward an urgent demand for its autonomous positioning ability in the fields of military reconnaissance, power inspection, geological exploration, forest fire prevention, etc., especially with the emergence of satellite navigation signal interference and deception technology, UAV requires It has completely autonomous positioning and navigation capabilities to get rid of the dependence on satellite navigation signals. Vision and inertial (Inertial measurement unit, IMU for short) combined SLAM (Simultaneous Localizat...

AI Technical Summary

Problems solved by technology

Through the complementarity of two different sensors, the IMU can perform error correction and compensation on the visual positioning information, which improves the real-time positioning capability of the UAV. However, as time goes by, the IMU itself also has accumulated drift errors after long-term operation. If no effective measures are taken, the drone will still fail to locate after a long time

At the same time, in the vision and IMU combined positioning method, the existing back-end loop detection positioning method optimizes the pose of the previous related image frames by judging whether the current position is a previously visited environment area, and there is a problem of misjudgment. This is because there are a large number of similar environments in the actual environment, such as groups of buildings with similar appearance, rolling sand dunes, and dense forest environments, which will cause loopback detection in a non-GPS environment due to a large number of similar images in similar environments. Difficult judgment of closed loop, resulting in the fact that it is a loop, but the algorithm judges that it is not a loop, or it is not actually a loop. The algorithm judgment is a misjudgment of the loop, resulting in a wrong loop and positioning failure

Therefore, in a similar environment without GPS, the key problem that needs to be solved urgently is how to prevent error loopback due to a large number of similar environment pictures, resulting in positioning failure

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