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Potential equilibrium multi-Bernoulli filtering SLAM method based on multiple robots

A multi-robot, multi-Bernoulli technology, applied in the direction of instruments, measuring devices, surveying and navigation, etc., can solve the problems of low SLAM accuracy and poor real-time performance, and achieve the effect of improving accuracy

Pending Publication Date: 2022-02-18
JIANGSU UNIV OF SCI & TECH
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] Purpose of the invention: The present invention aims to provide a multi-robot potential equalization multi-Bernoulli filter SLAM method to solve the problems of low precision and poor real-time performance of multi-robot SLAM

Method used

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  • Potential equilibrium multi-Bernoulli filtering SLAM method based on multiple robots
  • Potential equilibrium multi-Bernoulli filtering SLAM method based on multiple robots
  • Potential equilibrium multi-Bernoulli filtering SLAM method based on multiple robots

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Embodiment Construction

[0062] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0063] Depend on Figure 1-Figure 3 It can be seen that the multi-robot-based potential equalization multi-Bernoulli filtering SLAM method of the present invention comprises the following steps:

[0064] (1) Establish the RFS model of observations and map features.

[0065] Use the Gaussian mixture form to realize CBMber-SLAM map estimation, and obtain its parameter expression form. Before modeling, explain as follows:

[0066] 1) Each particle obeys the linear Gaussian motion model and observation model, and uses EKF to linearize it when it is nonlinear.

[0067] f k|k-1 (x|ξ)=N(x;F k-1 ξ,Q k-1 ) (1)

[0068] g k (z|x)=N(z;H k x,R k ) (2)

[0069] f k|k-1 (x|ξ) represents the state transition function at time k, g k (z|x) represents the likelihood function of the map feature at time k; N(; m, P) represents the Gaussian distributi...

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Abstract

The invention discloses a potential equilibrium multi-Bernoulli filtering SLAM method based on multiple robots. The method comprises the following steps of (1) establishing an observation and map feature multi-Bernoulli RFS model; (2) converting an SLAM problem into independent robot pose state estimation and map feature state estimation; (3) obtaining a robot pose state estimation predicted value at the moment k; (4) obtaining map feature state estimation at the moment k by using a potential equalization strategy; (5) trimming and combining the updated Gaussian items; (6) correcting map feature state estimation by using a Gaussian item; (7) the Gaussian item being fused into each robot observation set and substituted into the robot prior information set at the k+1 moment; (8) using the filter in the step (4) to estimate and adjust the weight of each particle map of the robot, and using particle weighted average to update the pose state estimation predicted value at the moment k; and (9) executing the step (3) when k is equal to k+1. According to the method, a problem of feature number over-estimation in SLAM is solved, and multi-robot SLAM precision is improved.

Description

technical field [0001] The invention relates to a SLAM method, in particular to a potential equalization multi-Bernoulli filtering SLAM method based on multi-robots. Background technique [0002] Navigation technology is essential for mobile robots to work in unknown environments, and simultaneous localization and mapping (SLAM) is the main navigation technology in recent years. The continuous improvement of navigation algorithm accuracy, speed, real-time and stability has been the focus of research. In some complex environments, such as underwater exploration environment, indoor fire rescue environment, etc., due to dense clutter, the data association accuracy of traditional SLAM algorithms will be reduced, and the amount of calculation will be greatly increased, resulting in a decline in the accuracy of traditional SLAM methods. To solve complex data association problems, one way is to avoid data association problems, and another way is to use more efficient algorithms, s...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01C21/20
CPCG01C21/20G01C21/38
Inventor 章飞张子菁姬传堂
Owner JIANGSU UNIV OF SCI & TECH