Robot distributed type representation intelligent semantic map establishment method

Abstract

The invention discloses a robot distributed type representation intelligent semantic map establishment method which comprises the steps of firstly, traversing an indoor environment by a robot, and respectively positioning the robot and an artificial landmark with a quick identification code by a visual positioning method based on an extended kalman filtering algorithm and a radio frequency identification system based on a boundary virtual label algorithm, and constructing a measuring layer; then optimizing coordinates of a sampling point by a least square method, classifying positioning results by an adaptive spectral clustering method, and constructing a topological layer; and finally, updating the semantic property of a map according to QR code semantic information quickly identified by a camera, and constructing a semantic layer. When a state of an object in the indoor environment is detected, due to the adoption of the artificial landmark with a QR code, the efficiency of semantic map establishing is greatly improved, and the establishing difficulty is reduced; meanwhile, with the adoption of a method combining the QR code and an RFID technology, the precision of robot positioning and the map establishing reliability are improved.

Description

technical field [0001] The invention belongs to the technical field of mobile robot navigation, and relates to a method for creating a robot distributed representation intelligent semantic map for the simultaneous positioning and map construction (Simultaneous Localization and Mapping, SLAM) problem of a mobile robot in an indoor environment. Background technique [0002] In recent years, with the widespread application of mobile robots in the fields of service, detection, and logistics, SLAM issues have drawn close attention from academia and engineering circles, and are regarded as the key to fully intelligent mobile robots. [0003] However, most of the SLAM research still stays on the positioning of the robot itself and the reconstruction of the physical environment, and does not involve the interaction between the robot and humans, which makes the robot unable to complete human semantic commands, which greatly limits the robot's job functions and reduces the workload. e...

AI Technical Summary

Problems solved by technology

However, the former only considers the natural landmarks. In the indoor working environment, there is generally a lack of natural environment features that are easy to capture, so it cannot solve the problem caused by the sparseness of natural landmarks; while the latter considers the sparseness of natural landmarks, supplemented by QRcode to express the environment , but the visual sensor is easily limited by the viewing angle and distance, and will fail when blocked by a large object, which will lead to a decrease in positioning accuracy and affect the reliability of semantic map construction

Images