FastSLAM method based on particle proposal distribution improvement and adaptive particle resampling

A technique of proposed distribution and resampling, applied in special data processing applications, instruments, electrical digital data processing, etc., can solve problems such as the inability to guarantee the positive definiteness of the posterior covariance matrix, non-local sampling, etc.

Active Publication Date: 2017-04-26
ZHEJIANG UNIV
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Problems solved by technology

However, when the dimension of the state vector is high, the weight of the central Sigma point of the tasteless Kalman filter may be negative, so the positive definiteness of the posterior covariance matrix

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  • FastSLAM method based on particle proposal distribution improvement and adaptive particle resampling
  • FastSLAM method based on particle proposal distribution improvement and adaptive particle resampling
  • FastSLAM method based on particle proposal distribution improvement and adaptive particle resampling

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Embodiment Construction

[0118] In order to describe the present invention more specifically, the technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0119] In this embodiment, it is assumed that the robot adopts a two-dimensional linear velocity motion model and a distance-azimuth measurement model, so the spatial coordinate dimensions of the robot and the landmarks are both 2.

[0120] Such as figure 1 As shown, the present invention is based on the FastSLAM method of improving particle proposal distribution and adaptive particle resampling, comprising the following steps:

[0121] Step 1, express the joint posterior probability of robot pose and landmark feature map at time k as N k A particle set consisting of particles:

[0122]

[0123] Among them, (i) represents the particle number, Indicates the weight of the i-th particle at time k, with respectively represent the estimated value of...

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Abstract

The invention discloses a FastSLAM method based on particle proposal distribution improvement and adaptive particle resampling. The method comprises the steps that 1, a square root transformation unscented Kalman filter is utilized to estimate optimal particle proposal distribution, and the pose state of a robot is sampled; 2, a square root volume Kalman filter is utilized to update feature map information corresponding to each particle; 3, an adaptive particle resampling method based on relative entropy is utilized to determine the quantity of particles needed at the current moment; 4, the pose stat of the robot and guidepost feature map information are determined according to a particle set obtained after resampling. A traditional FastSLAM algorithm is improved from the two aspects of quality and quantity of the sampling particles at the same time, thus, the numerical stability and accuracy of the algorithm in the estimation process are enhanced, and the quality of the sampling particles is improved; in the particle resampling process, the least quantity of the needed particles is dynamically determined according to estimation uncertainty, and therefore the calculation efficiency of the algorithm is improved.

Description

technical field [0001] The invention belongs to the field of autonomous navigation of mobile robots, in particular to a FastSLAM method based on improved particle proposal distribution and adaptive particle resampling. Background technique [0002] As an important branch of intelligent robots, mobile robots have been widely used in fields such as manufacturing industry, national defense and military, aerospace, health care, and home services. In order to realize that mobile robots can successfully complete specific tasks without manual intervention in complex and unknown environments, mobile robots must have the ability to navigate autonomously. As a key prerequisite for autonomous navigation, a mobile robot needs to estimate its own attitude through the internal and external sensors it carries, and at the same time describe the unknown environment with a map. The Simultaneous Localization and Mapping (SLAM) method of mobile robots can make full use of the correlation betwe...

Claims

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Application Information

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IPC IPC(8): G06F17/50
CPCG06F30/20
Inventor 陈耀武徐巍军黄余格
Owner ZHEJIANG UNIV
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