Positioning method and system based on visual inertial navigation information fusion

Abstract

The invention discloses a positioning method and system based on visual inertial navigation information fusion. The method comprises steps as follows: acquired sensor information is preprocessed, wherein the sensor information comprises an RGB image and depth image information of a depth vision sensor and IMU (inertial measurement unit) data; external parameters of a system which the depth visionsensor and an IMU belong to are acquired; the pre-processed sensor information and external parameters are processed with an IMU pre-integration model and a depth camera model, and pose information isacquired; the pose information is corrected on the basis of a loop detection mode, and the corrected globally uniform pose information is acquired. The method has good robustness in the positioning process and the positioning accuracy is improved.

Description

technical field [0001] The invention relates to sensor fusion technology, in particular to a positioning method and system based on visual inertial navigation information fusion. Background technique [0002] At present, visual inertial navigation fusion technology is widely used in 3D reconstruction, positioning and navigation of unmanned vehicles and drones, and automatic driving, aiming to provide real-time robust and accurate position and attitude. The mainstream visual inertial navigation fusion technology is based on the filter method. The filter-based method mainly uses the Kalman filter and its variants. The inertial navigation information is obtained according to the kinematic model of the inertial measurement unit to obtain the prior distribution of the system state vector, and the Kalman gain is updated by using the observation model of the visual sensor to obtain The posterior distribution of the system state vector. In the specific processing, according to whe...

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Problems solved by technology

[0003] The filter-based method, regardless of loose coupling or tight coupling, has a common problem that it cannot effectively eliminate the cumulative error and cannot deal with the loopback problem.

[0004] For this reason, an optimization-based method is proposed in the industry. The optimization-based method can solve the above-mentioned accumulated errors and defects that cannot handle loopbacks. However, the robustness of the algorithm is relatively low and cannot be popularized.

[0005] In particular, the external parameter calibration in the current optimization-based method mainly uses the kalibr toolbox, and using the kalibr toolbox can only be calibrated offline and requires a special calibration board, which is not easy to operate and cannot be used directly online

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