Indoor global positioning method for service robots

Abstract

The invention provides a service robot indoor global localization method. The method comprises the following steps: step one, arranging four base stations on two opposite walls in a room, and respectively arranging two measuring instruments on two sides of a service robot; step two, establishing a robot coordinate system and a global coordinate system, wherein the positions of two measuring instruments in the robot coordinate system are Pr0=(1,y0,z), and Pr1=(0,y1,z); step three, calculating to obtain the global position (P) of the service robot; step four, calculating to obtain the orientation (theta) of the service robot. Compared with the prior art, the provided method has the advantages that a service robot can be high precisely, dynamically, and globally positioned indoor, and the orientation of the service robot can be acknowledged.

Description

technical field [0001] The invention relates to the mechanical field, in particular to an indoor global positioning method for a service robot. Background technique [0002] If a service robot wants to complete navigation tasks autonomously in an indoor environment, it needs to know its global position in the environment first, that is, the autonomous positioning problem of the robot. At present, the autonomous positioning of robots mostly uses distance sensors such as lasers or depth images, and SLAM technology is used to create indoor environmental maps and estimate the robot's position online through map matching. This type of positioning method belongs to local positioning, and its main problems are that it is greatly affected by environmental changes, the problem of robot kidnapping (kidnap), and it is difficult to converge in an unstructured environment. Another type of positioning method belongs to global positioning, such as the position of the active robot based on...

AI Technical Summary

Problems solved by technology

However, most of these methods use single-point position measurement, and because of the lack of reference constraints, they cannot estimate the color noise caused by environmental interference, so they are mostly used for rough position estimation or static position estimation, which is difficult to meet the high-precision indoor positioning requirements of mobile robots and difficult to obtain real-time Higher robot orientation

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