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Large-Scale 3D Environment Map Creation Method Based on Graph Optimization Theory

An environmental map, large-scale technology, applied in the direction of program control manipulators, manufacturing tools, manipulators, etc., to achieve the effect of ensuring efficiency and reliability

Active Publication Date: 2020-08-14
XI AN JIAOTONG UNIV
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Problems solved by technology

[0014] The purpose of the present invention is to overcome the shortcomings of the existing three-dimensional environment map creation method, to provide a large-scale three-dimensional environment map creation method based on graph optimization theory

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  • Large-Scale 3D Environment Map Creation Method Based on Graph Optimization Theory
  • Large-Scale 3D Environment Map Creation Method Based on Graph Optimization Theory
  • Large-Scale 3D Environment Map Creation Method Based on Graph Optimization Theory

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Embodiment Construction

[0051] The present invention will be further described below in conjunction with accompanying drawing.

[0052] see figure 1 , the large-scale 3D environment map creation method based on graph optimization theory is divided into three parts, each part includes the following steps:

[0053] 1) The local pose is obtained by the clipping iterative closest point algorithm and the fast multi-scale descriptor-based corresponding propagation algorithm. The specific steps are as follows:

[0054] (1a) Register two frames of 3D point clouds acquired at adjacent moments by clipping iterative closest point algorithm;

[0055] (1b) After obtaining the registration result, verify the registration result; if the verification is successful, the registration result can be output as the pose estimation result of the mobile robot;

[0056] (1c) If the verification fails, use a fast multi-scale descriptor-based correspondence propagation algorithm to re-estimate the local pose of the mobile ro...

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Abstract

The invention provides a large-scale three-dimensional environmental map establishing method based on the graph optimization theory. The large-scale three-dimensional environmental map establishing method based on the graph optimization theory comprises the steps that firstly, local poses of a mobile robot are sequentially estimated by means of a clipping iterative closest point algorithm, the reliability of an estimation result is judged, if the result is not reliable, the local posts of the mobile robot at the current moment are re-estimated by means of a rapid corresponding propagation algorithm based on multi-scale descriptors, a pose graph is gradually established at the same time, the peaks of the graph express the pose at each moment of the mobile robot, and edges of the graph express the constraint between connected poses; then, a closed loop hypothesis and a verification method are provided and used for detecting the closed loop of the pose graph; and finally, a pose constraint equation is solved by means of a motion averaging method, and then the accurate overall poses of the mobile robot are obtained. An experiment result shows that the large-scale three-dimensional environmental map establishing method based on the graph optimization theory can well establish a large-scale 3D environmental map.

Description

technical field [0001] The present invention relates to the fields of mobile robots, navigation and positioning, and computer vision, and in particular to a method of sequentially estimating the local poses of mobile robots using the cropped iterative closest point algorithm TrICP and a fast multi-scale descriptor-based correspondence propagation algorithm, and using adaptive closed-loop assumptions and verification methods to detect closed loops in pose graphs, and methods to compute accurate global poses of mobile robots using moving average methods. Background technique [0002] With the rapid development of computers and sensor equipment, mobile robot technology has been widely used in various fields related to human production and life. In the process of performing various tasks autonomously, mobile robots need to obtain reliable pose information, and the acquisition of pose information depends on accurate environment maps. For this reason, in addition to installing va...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): B25J9/16
Inventor 姜祖涛祝继华林之阳李钟毓李垚辰庞善民
Owner XI AN JIAOTONG UNIV
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