Line feature map creation method of mobile robot based on laser range finder

Abstract

The invention discloses a line feature map creation method of a mobile robot based on a laser range finder and belongs to the technical field of mobile robot map creation. The line feature map creation method of the mobile robot based on the laser range finder includes the following steps: firstly, starting the laser range finder to scan an external environment, then preprocessing scanned laser data through an average filtering method to filter noise points; utilizing exponential distribution to approximate posterior distribution of laser data points, conducting region segmentation on the laser measurement data points through a dynamic threshold method, dividing the laser measurement data points into N region pieces after the region segmentation, and conducting segment fitting on the data points in the regions through a golden section method; and extracting feature corners through a method of combining Hough transformation and an area threshold value, achieving transition from an image space to a parameter space, building local environment maps, and achieving the fusion of the local environment maps by utilizing a sector matching method. The line feature map creation method of the mobile robot based on the laser range finder has the advantages of being easy in calculation and high in accuracy and improving rapidity of algorithm execution.

Description

technical field [0001] The invention relates to a method for creating a map by a mobile robot based on a laser rangefinder, in particular to a method for creating a line feature map based on a laser rangefinder, and belongs to the technical field of map creation for mobile robots. Background technique [0002] Mobile robots are usually able to locate and navigate based on environmental maps, so map creation is a basic problem in the study of mobile robots. Usually, the representation methods of environment map are: grid map, topological map, and feature map. Grid maps are easy to create and understand, but when the environment space is relatively large, the storage capacity of the map will increase, which will increase the complexity of time and space; when the robot is in an unstructured environment, the topological map will bring It is difficult to locate the robot. For example, it is difficult to determine whether it is the same node through the topological map in places...

AI Technical Summary

Problems solved by technology

The principle of the least squares method is simple, intuitive to use but more complicated to calculate

For the extraction of feature corners, the Hough transform is introduced, which realizes the transformation from image space to parameter space. Compared with the general determination of wall corners by the slope between line segments, it has good fault tolerance and robustness for the problem of boundary discontinuity. However, in the Hough space, the amount of calculation increases with the increase of parameters.

For line segment matching, three test conditions are currently given. Only when all three conditions are met, the line segment is considered to match, and there are many restrictions.

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