Positioning method and device, intelligent robot and computer-readable storage medium

Abstract

The application discloses a positioning method, which is applied to an intelligent robot. It is characterized in that the intelligent robot is equipped with a plurality of sensors for detecting the detection pose of the intelligent robot. The sensor includes a laser sensor, and the positioning method includes acquiring A plurality of detection data detected when detecting the detection pose, the laser sensor is used to detect the laser data; calculate the adaptive confidence degree of the laser data in at least one degree of freedom according to the laser data; calculate at least one detection data according to the adaptive confidence degree The adaptive weight of the pose constraint in the graph optimization model; the graph optimization model and the objective function of the graph optimization model are constructed according to at least one detection data and the adaptive weight; and the optimal pose of the intelligent robot is calculated according to the objective function. The application also discloses a positioning device, an intelligent robot and a computer-readable storage medium.

Description

technical field [0001] The present application relates to the field of robot technology, and more specifically, to a positioning method and device, an intelligent robot, and a computer-readable storage medium. Background technique [0002] Intelligent robot positioning algorithms usually use a filter framework to construct motion equations and observation equations, and combine odometer data and laser matching to achieve pose estimation. However, when the surrounding environment of the intelligent robot changes drastically or the laser data does not return data obviously, the observation equation will have a large error, resulting in an error in the pose estimation. At the same time, when the system has more reference sensor data, the filter algorithm cannot perform deep fusion on these data. This makes the robot unable to work in a more complex environment, which may easily cause the positioning of the intelligent robot to drift and easily lead to the loss of the intellige...

AI Technical Summary

Problems solved by technology

However, when the surrounding environment of the intelligent robot changes drastically or the laser data does not return the data obviously, the observation equation will have a large error, resulting in an error in the pose estimation.

At the same time, when the system has more reference sensor data, the filter algorithm cannot perform deep fusion of these data

It makes the robot unable to work in a more complex environment, which may easily cause the positioning of the intelligent robot to drift and easily lead to the loss of the intelligent robot

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