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Particle filter positioning and map construction method based on adaptive genetic algorithm

A genetic algorithm and particle filter technology, applied in the field of particle filter positioning and map construction based on adaptive genetic algorithm, can solve problems such as particle exhaustion, and achieve the effects of improving accuracy, protecting diversity, and improving operating efficiency

Pending Publication Date: 2018-12-25
黎建军
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Problems solved by technology

[0004] The object of the present invention is the particle filter localization and map construction method based on the adaptive genetic algorithm, the present invention uses the adaptive genetic algorithm to optimize the particle set representing the posterior distribution of the path, removes the resampling in the general particle filter SLAM method, and solves the problem of It solves the "particle exhaustion problem" caused by resampling, and improves the operating efficiency of the entire SLAM system

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  • Particle filter positioning and map construction method based on adaptive genetic algorithm
  • Particle filter positioning and map construction method based on adaptive genetic algorithm
  • Particle filter positioning and map construction method based on adaptive genetic algorithm

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Embodiment Construction

[0101] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0102] Such as figure 1 As shown, the particle filter positioning and map construction method based on adaptive genetic algorithm of the present invention comprises the following steps:

[0103] Step 1: Initialization: set the initial position and end position of the robot movement;

[0104] Step 2: Judgment: During the movement of the robot from the initial position to the end position, judge whether the current position of the robot has reached the end position. If it reaches the end position, the robot will stop moving;

[0105] Step 3: State estimation: If the end position is not reached, use the unscented Kalman filter algorithm to calculate the proposed distribution of the pose of each particle at k-1 time;

[0106] Step 3: Feature matching: Obtain the actual observation value of the robot on the environmental characteristics at time k, and use ...

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Abstract

The invention discloses a particle filter positioning and map construction method based on an adaptive genetic algorithm. The method comprises the steps of: initializing; judging whether the current position of the robot reaches the ending position; estimating the state; matching features; updating the state; estimating path; and optimizing the path distribution of robot at a k moment using an adaptive genetic algorithm. According to the particle filter positioning and map construction method based on an adaptive genetic algorithm, a particle set representing a path posterior distribution is optimized using an adaptive genetic algorithm, resampling in general particle filter SLAM method is eliminated, the problem of particle depletion caused by resampling is solved and the running efficiency of the whole SLAM system is improved.

Description

technical field [0001] The invention relates to a particle filter-based simultaneous positioning and map construction method, in particular to a particle filter positioning and map construction method based on an adaptive genetic algorithm. Background technique [0002] With the vigorous development of robot technology, the autonomous cognition ability of robots to the unknown environment has become a research hotspot in robotics. Constructing a map of the unknown environment, that is, the positioning and navigation of mobile robots, is one of the key research contents and research hotspots of robot autonomous cognition. Among them, Simultaneous Localization and Mapping (SLAM) is an effective means for mobile robots to achieve localization and navigation, that is, the robot extracts features based on the acquired robot-related data, and after feature matching, finally independently constructs a map of the unknown environment in which the robot is located. Map and obtain its...

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Application Information

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IPC IPC(8): G01C21/20
CPCG01C21/20
Inventor 黎建军李博
Owner 黎建军
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