3D point cloud FPFH characteristic-based real-time three dimensional space positioning method

Abstract

The present invention relates to a 3D point cloud FPFH characteristic-based real-time three dimensional space positioning method. The method comprises a step 1) of obtaining the 3D point cloud data from a depth camera; a step 2) of selecting the point cloud key frames; 3) a point cloud pre-processing step; 4) a characteristic description step of using an ISS algorithm to obtain the point cloud key points and obtaining the FPFH characteristics of the key points; 5) a point cloud registration step of firstly utilizing a sampling consistency initial registration algorithm to carry out the FPFH characteristic-based initial registration on two point clouds, and then using an ICP algorithm to carry out the secondary registration on an initial registration result; 6) a coordinate transformation step of obtaining a change matrix of the three dimensional space coordinates of a mobile robot, and transforming the coordinate of the current point cloud into an initial position via a transformation matrix; a step 7) of repeating the steps 1) to 6), and calculating the coordinate of the robot relative to the initial position along with the movement of the robot. The method of the present invention has a better accuracy for the real-time positioning of the mobile robot on a bad illumination or completely dark condition.

Description

technical field [0001] The invention relates to the field of positioning of mobile robots, and can greatly compensate for the inability to realize the positioning function due to unstable feature extraction caused by unstable illumination or insufficient light, and the positioning result is also more real-time and accurate. Background technique [0002] Positioning is the process of determining the position of the robot in its operating environment. More specifically, it uses the information of the prior environment map, the current robot pose estimation, and the observation values ​​of the sensor, etc., after certain processing and transformation. , yielding a more accurate estimate of the robot's current position. Using information sensed by sensors to obtain reliable localization is the most basic and important function of autonomous mobile robots, and it is also an important research topic that has attracted much attention and is challenging in mobile robot research. ...

AI Technical Summary

Problems solved by technology

[0007] In order to overcome the inaccurate acquisition of color information of the robot under poor lighting or complete darkness conditions, which leads to large errors in registration results and inaccurate positioning results, the present invention provides a robot that is not affected by lighting conditions and has high accuracy. The real-time three-dimensional space positioning method based on the 3D point cloud FPFH feature, which is relatively high and has low computational time and space complexity, uses the three-dimensional point cloud and three-dimensional local features to register the front and rear consecutive frames, and directly obtains the three-dimensional position of the mobile robot. The 3D space positioning information of the mobile robot can be obtained in real time. This method can be used for but not limited to the 3D space positioning of the mobile robot based on vision

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