Robot pose positioning method and system

A positioning method and robot technology, applied in manipulators, program-controlled manipulators, manufacturing tools, etc., can solve problems such as complex nonlinear systems and solution errors

Active Publication Date: 2020-05-12
SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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  • Abstract
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  • Claims
  • Application Information

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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 approx

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  • Robot pose positioning method and system
  • Robot pose positioning method and system
  • Robot pose positioning method and system

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Embodiment 1

[0038] Embodiment 1 of the present invention provides a pose positioning method.

[0039] This embodiment adopts the unscented Kalman filter algorithm, which uses Sigma point sampling and unscented transformation to realize the approximation of the probability density distribution, which can retain high-order terms and improve the solution accuracy, but the premise of the unscented Kalman filter algorithm is that The system noise variance matrix Q and the observation noise variance matrix R, in practical applications, the mathematical models of the two are difficult to construct, or they change every moment, and cannot be accurately described. If the unscented Kalman filter algorithm is directly used, it may lead to the solution The local optimal state estimation of the mobile robot finally causes a large error in the positioning result of the mobile robot.

[0040] Therefore, in this embodiment, the IMU odometer is used as the local reference system, and the data collected by...

Embodiment 2

[0120] This embodiment provides a robot pose positioning system, which is used to implement the method described in Embodiment 1, such as image 3 As shown, it is a structural block diagram of the robot pose positioning system of this embodiment, including:

[0121] Acquisition module 100: used to collect IMU odometer data as a local reference system, and obtain the previous moment pose state vector and the previous moment covariance matrix according to the local reference system;

[0122] Sampling module 200: for sampling the pose state vector at the last moment, and performing unscented transformation on the sampling points;

[0123] Prediction module 300: used to use the system model to predict the unscented transformed last moment pose state vector and last moment covariance matrix to obtain the current moment prediction value, the current moment prediction value includes: the current moment pose state vector and the covariance matrix at the current moment;

[0124] Filt...

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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...

Claims

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Application Information

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IPC IPC(8): B25J9/16
CPCB25J9/1602
Inventor 张凯黄鑫董宇涵
Owner SHENZHEN GRADUATE SCHOOL TSINGHUA UNIV
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