Method and system for real-time positioning and map construction of ellipsoid boundary Kalman filter

A technology of Kalman filtering and map construction, which is used in geographic information databases, structured data retrieval, navigation calculation tools, etc.

Active Publication Date: 2020-10-16
HUAZHONG UNIV OF SCI & TECH
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  • Application Information

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Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a real-time positioning and map construction method and system based on ellipsoid boundary Kalman filter, thereby solving the problem of real-time positioning and map construction of the existing member filter and Kalman filter A technical issue that limits accuracy in map building

Method used

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  • Method and system for real-time positioning and map construction of ellipsoid boundary Kalman filter
  • Method and system for real-time positioning and map construction of ellipsoid boundary Kalman filter
  • Method and system for real-time positioning and map construction of ellipsoid boundary Kalman filter

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specific example

[0110] figure 2 The specific program flow chart of the real-time positioning and mapping of mobile robots based on the ellipsoid boundary Kalman filter algorithm is given.

[0111] Consider the SLAM problem of mobile robot planar motion, refer to image 3 , the kinematic equation can be given in the Cartesian coordinate system:

[0112]

[0113] Here the SLAM system state vector is x k =[x(k) y(k) φ(k)] T , which represents the position and orientation of the robot, and the noise vector is Gaussian noise, is unknown but bounded noise.

[0114] The speed of the robot v c It is measured by the encoder on the rear wheel, that is, v e is the rear wheel speed calculated by the encoder, L is the distance between the front and rear axles, h is the distance between the center of the rear axle and the encoder, b is the vertical distance from the center of the rear axle to the laser sensor, a is the center of the rear axle to the laser sensor horizontal distance.

[011...

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Abstract

The invention discloses an ellipsoidal boundary Kalman filtering instant positioning and map building method and system, provides a model containing characteristics of set membership filtering and Kalman filtering, and considers model randomness and bounded uncertain factors at the same time. The model takes bounded but uncertain noise into account in a state transition equation and an observationequation, and has better uncertain metric characteristics. In addition, a recursive framework with random uncertainty in the Kalman filter is reserved, and therefore the advantages of the KF are reserved in the iteration process. In a set membership filtering framework, linearization should best adapt to a nonlinear function on a state estimation set rather than only at a state estimation point,and a linearization model is obtained by minimizing a weighted square error between a function value and a linear function approximation on the whole state estimation set. The nonlinear system state variable parameter optimal estimation precision and the system calculation stability are improved through the nonlinear system state variable parameter optimal estimation method.

Description

technical field [0001] The invention belongs to the field of navigation guidance and control, and more specifically relates to a real-time positioning and map construction method and system based on ellipsoid boundary Kalman filtering. Background technique [0002] When an autonomously moving robot is in a new environment, such as rescue in a collapsed building and exploration in an unknown environment, intelligent methods are needed to find its way in the environment. In this process, the robot should know its current location and the map of its environment. This problem can be referred to as Simultaneous Localization and Mapping (SLAM). The goal of SLAM is to find the orientation of the robot from any initial position while building a model of the unknown environment. The SLAM problem can be described as: the robot starts to move from an unknown position in an unknown environment, locates itself according to position estimation and maps during the movement process, and bu...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F16/29G01C21/20G06F30/20
CPCG01C21/20G06F30/20
Inventor 黄剑程荣曹瑜周铭肖华
Owner HUAZHONG UNIV OF SCI & TECH
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