A Pose Estimation Method for Mobile Platform Based on Indirect Kalman Filter

Abstract

The invention discloses a mobile platform pose estimation method based on indirect Kalman filtering. The method includes the following steps that difference between a calculation value XI of a strapdown inertial navigation system and observation data XN of a global positioning system serves as observation input of an indirect Kalman filter, a prediction equation is built in the indirect Kalman filter, output of the filter is an estimated value of an error, part of parameter feedback in the output is used for correcting parameters of observation computation of the strapdown inertial navigation system and the global positioning system so as to correct error calculation of the two systems, and the sum of parameters, like coordinates and postures, in XI and the estimated value serves as an estimated value of all values in a final system, wherein please see the estimated value in the description. Compared with the traditional method in which a direct Kalman filtering algorithm is adopted, more accurate pose estimation results can be given, and work efficiency of a robot is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of mobile platform pose estimation methods, in particular to a mobile platform pose estimation method based on indirect Kalman filtering. Background technique [0002] For mobile platforms (such as mobile robots), the core problem is to automatically calculate the position and attitude of the mobile platform, where the position is the Cartesian coordinates (x, y, z) in three-dimensional space, and the attitude is the attitude angle (φ , θ, γ), after obtaining the pose, the motion trajectory of the mobile platform can be controlled through the control algorithm, so that it can move according to the set trajectory. [0003] The commonly used hardware framework for mobile platforms is inertial navigation devices (gyroscopes, accelerometers and other inertial navigation devices) + observation devices (such as GPS, laser, vision and other observation devices), and the pose algorithm generally uses the direct Kalm...

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Problems solved by technology

[0003] The commonly used hardware framework for mobile platforms is inertial navigation devices (gyroscopes, accelerometers and other inertial navigation devices) + observation devices (such as GPS, laser, vision and other observation devices), and the pose algorithm generally uses the direct Kalman filter algorithm. The direct quantity of the attitude angle is used as the state variable, and the direct quantity of the observation device is the observed value. The Kalman filter prediction and observation equations are established for calculation. The filter equations are relatively complicated, and the equations often contain many nonlinear formulas, so it is impossible to use linear Kalman filter equations. The Mann filter algorithm is used for calculation, and the extended Kalman filter algorithm is generally used, which affects the simplification and accuracy of the calculation, and also has an adverse effect on the data fusion of multiple sensors. Therefore, a better method is needed to estimate the pose.

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