Robot pose positioning method and system

Abstract

The invention discloses a robot pose positioning method and a system, and relates to the field of robot positioning, wherein the method comprises the following steps of: acquiring IMU odometer data asa local reference system, acquiring a pose state vector and a covariance matrix at the previous time according to the local reference system, sampling the pose state vector at the previous time, performing unscented transformation to sampling points, predicting the pose state vector and the covariance matrix at the previous time subjected to unscented transformation by using a system model to obtain a prediction value at the current time, carrying out filtering treatment to the prediction value at the current time according to the actual measurement value to obtain the relative pose measurement value at the current time; after filtering, obtaining the global pose estimation at the current time according to the relative pose measurement value at the current time through coordinate transformation; and carrying out robot pose positioning according to the global pose estimation value at the current time. The unscented Kalman filter algorithm is combined with IMU odometer data and actual measurement values collected by GPS satellites or vision systems to obtain global pose estimation values, which are robust to complex environments and improve positioning precision.

Description

technical field [0001] The present invention relates to the field of robot positioning, in particular to a method and system for robot pose positioning. Background technique [0002] In the field of robotics, when it is necessary to solve the positioning problem in an unknown environment, the method of simultaneous localization and mapping (SLAM) is usually adopted. The core idea is to use sensor data to build a map of the surrounding environment in real time and realize the robot itself. position. The SLAM problem for robots is mainly the positioning problem of robots, which is essentially a state estimation problem. Therefore, the Kalman filter algorithm (KF) is usually used for state estimation to achieve positioning. However, the classical Kalman filter algorithm is only for linear systems, and practical problems are often based on more complex nonlinear systems. For nonlinear systems, the Extended Kalman Filter (EKF) algorithm, which is approximately linearized, is of...

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Problems solved by technology

However, the classic Kalman filter algorithm is only for linear systems, and practical problems are often based on more complex nonlinear systems.

For nonlinear systems, the Extended Kalman Filter (EKF) algorithm, which is approximately linearized, is mostly used. It uses the first-order Taylor expansion to deal with the linearization of nonlinear problems, ignoring the higher-order items of Taylor expansion, and there is a certain error in the solution.

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