Method for densifying monocular SLAM feature point map

A feature point, single-purpose technology, applied in the field of densified monocular SLAM feature point map, can solve the problems of inability to move flexibly, unable to navigate with surgical robots, etc., and achieve the effect of precise navigation

Active Publication Date: 2020-10-30
SUZHOU UNIV +1
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  • Application Information

AI Technical Summary

Problems solved by technology

Binocular cameras and RGB-D cameras cannot move flexibly in the human intestine due to volume limitations, so the only way to map the intestines is to use an endoscope with a monocular camera
Monocular SLAM can quickly track the camera, but the constructed map is a sparse feature point map, which can only be used for positioning and cannot be used for navigation of surgical robots

Method used

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  • Method for densifying monocular SLAM feature point map
  • Method for densifying monocular SLAM feature point map
  • Method for densifying monocular SLAM feature point map

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Embodiment Construction

[0038] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, so that those skilled in the art can better understand the present invention and implement it, but the examples given are not intended to limit the present invention.

[0039] refer to figure 1 As shown, the present invention discloses a dense monocular SLAM feature point map method, comprising the following steps:

[0040] S1. Delaunay triangulation is performed on the feature points in the key frame of each frame to obtain the key frame after triangulation.

[0041] Such as figure 2 Shown is the Delaunay triangulation performed on the pixel plane by the feature points in the key frame of the first frame.

[0042] The key frame in this step is the key frame selected during the monocular SLAM operation, and the Delaunay triangulation is a two-dimensional triangulation on the pixel plane of the key frame.

[0043] S2. Project the triangulated ...

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Abstract

The invention discloses a method for densifying a monocular SLAM feature point map, which is characterized by comprising the following steps: S1, carrying out Delaunay triangulation on feature pointsin each frame of key frame to obtain a triangulated key frame; s2, projecting the triangulated key frame to a three-dimensional space to obtain coordinates of the triangulated key frame in a space coordinate system, and constructing a three-dimensional image of the key frame; s3, setting a triangular side length threshold of the triangulation, and filtering abnormal points in the three-dimensionalimage to obtain an optimized three-dimensional image; and S4, performing uniform point supplement on each triangle in the optimized three-dimensional image to obtain a dense monocular SLAM feature point map. Accurate navigation of the surgical robot can be realized.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for densifying monocular SLAM feature point maps. Background technique [0002] The development of SLAM (Simultaneous Localization And Mapping) technology makes it a new solution for endoscopic scene reconstruction. SLAM is the positioning and synchronous map construction of robots in unknown areas in the field of mobile robots. The visual SLAM with visual sensors as the main body is mainly divided into monocular, binocular and RGB-D according to sensor classification. Binocular cameras and RGB-D cameras cannot move flexibly in the human intestine due to volume limitations, so the only way to map the intestines is to use an endoscope with a monocular camera. Monocular SLAM can quickly track the camera, but the constructed map is a sparse feature point map, which can only be used for positioning and cannot be used for navigation of surgical robots. Contents of...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T3/00G06T17/20G06K9/00
CPCG06T3/005G06T17/20G06V20/46Y02T10/40
Inventor 郁树梅郭文康孙荣川干旻峰孙立宁
Owner SUZHOU UNIV
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