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Improved FastSLAM algorithm based on clustering and membrane calculation

A membrane computing and clustering technology, applied in computing, navigation computing tools, computer components, etc., can solve problems such as poor results, and achieve the effects of increasing diversity, expanding search range, and low efficiency

Active Publication Date: 2019-12-20
ANHUI UNIV OF SCI & TECH
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Problems solved by technology

However, these methods are not effective in alleviating the problem of particle degradation

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  • Improved FastSLAM algorithm based on clustering and membrane calculation
  • Improved FastSLAM algorithm based on clustering and membrane calculation
  • Improved FastSLAM algorithm based on clustering and membrane calculation

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Embodiment Construction

[0033] Such as figure 1 As shown, the process of an improved FastSLAM algorithm based on clustering and membrane calculation is:

[0034] (1) Initialize, obtain the pose x of the robot at the current moment t t , the environment map feature map t , from time t to time t+1, the movement amount u of the robot t , the observed quantity z t , observation noise covariance matrix H t ;

[0035] (2) Sampling, at x t Randomly set L particles around, each particle represents a possible pose of the robot, and then according to the proposed probability distribution Where (k) represents the kth pose, and samples the possible poses of the robot at time t+1. The closer to the proposed distribution, the greater the weight of the particles, and Q particles are obtained to form the particle set φ t , let the weight of each particle be w (k) , k∈[1,2,…,Q]:

[0036] (3) Clustering, according to the weight of each particle, the particle set φ t All particles in are clustered into M gro...

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Abstract

The invention discloses an improved FastSLAM algorithm based on clustering and membrane calculation, and the algorithm comprises a series of processes: initialization, sampling, clustering, membrane calculation optimization, weight calculation, pose calculation, map updating and resampling, and the functions of robot quick self-positioning and environment map construction are achieved. According to the method, a particle set is preprocessed by using a clustering method; the high parallelism of an algorithm is optimized by using membrane calculation; the particle searching speed is increased, the searching range is enlarged, the particle degeneration condition is relieved, the diversity of particles is guaranteed, the particles are promoted to be distributed near the real pose more quickly,and the robot positioning and mapping precision and speed are effectively improved.

Description

technical field [0001] The invention relates to an improved method of the FastSLAM algorithm for simultaneous localization and mapping of a robot based on clustering and membrane calculation. Background technique [0002] Simultaneous Localization and Mapping (SLAM) refers to the positioning of a mobile robot through its own sensors in an unknown environment, and simultaneously constructs a map of the surrounding environment, which is a key technology for tasks such as autonomous navigation and obstacle avoidance. At present, the most commonly used methods to solve SLAM problems are probability-based methods, among which the Fast Simultaneous Localization and Mapping (FastSLAM) algorithm is the most prominent. FastSLAM breaks the Gaussian Environmental constraints are widely used. However, the frequent resampling of FastSLAM will cause the particle set of the robot to estimate the pose to lose diversity, and as time continues to grow, the positioning accuracy will continue ...

Claims

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Application Information

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IPC IPC(8): G06K9/62G01C21/20
CPCG01C21/20G06F18/23213
Inventor 韩涛黄友锐徐善永陈亮凌六一唐超礼许家昌
Owner ANHUI UNIV OF SCI & TECH
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