Positioning method of AMCL algorithm based on Gaussian model and storage medium

Abstract

The invention provides a positioning method of an AMCL algorithm based on a Gaussian model and a storage medium. The method comprises the steps of receiving laser data, and converting the laser data into map coordinate system data according to a set conversion parameter; filtering data exceeding a map range and a sensor range; judging whether a recently occupied grid of a measurement point in the map coordinate system data is less than a threshold value or not, and if so, marking the measurement point as a laser matching point; normalizing the actual distance between the laser matching point and the occupied grid through a Gaussian model, dividing the normalized data into three groups, and respectively calculating the accumulated normalized distance; carrying out weighted average on the three groups of normalized distances, and calculating a laser matching score of single-frame laser data; and determining the positioning accuracy of the laser data according to the laser matching score. According to the method, the problems of easy failure of robot positioning initialization and continuous positioning under the condition of large environment change, such as the condition that a sensor is shielded by a dynamic obstacle, are solved.

Description

technical field [0001] The invention relates to the field of robot positioning, in particular to a Gaussian model-based AMCL algorithm positioning method and a storage medium. Background technique [0002] The AMCL (Adaptive Monte Carlo Algorithm) algorithm is a common algorithm for positioning a robot with prior map information using sensors such as lidar to perceive the surrounding environment of the robot. A known map tracks the pose of the robot. Compared with the ordinary Monte Carlo algorithm, the adaptive Monte Carlo algorithm is embodied in: to solve the robot kidnapping problem, it will find that the average score of the particles suddenly decreases, which means that the correct particles are abandoned in a certain iteration At this time, re-sprinkle some particles in the whole world; solve the problem of fixed number of particles, because sometimes when the robot is positioned successfully, the particles are all concentrated in one piece, and it is not necessary t...

AI Technical Summary

Problems solved by technology

[0004] In view of the above-mentioned technical problems, the main purpose of the present invention is to provide a positioning method based on the AMCL algorithm of the Gaussian model, to solve the problem that the robot positioning initialization is prone to failure and the continuous positioning problem under large environmental changes such as sensors being blocked by dynamic obstacles

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