Scene segmentation and target modeling method based on concavo-convex and RSD features

A technology of scene segmentation and modeling method, which is applied in the fields of unmanned driving, unmanned aerial vehicle, underwater robot, deep space exploration, and computer vision. Computational operation limit usage and other issues

Inactive Publication Date: 2019-06-07
BEIJING UNIV OF CHEM TECH
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Problems solved by technology

Another class of algorithms uses learning-based methods combined with optimization of conditional random fields and Markov random fields to deal with segmentation problems, but their high compu

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  • Scene segmentation and target modeling method based on concavo-convex and RSD features
  • Scene segmentation and target modeling method based on concavo-convex and RSD features
  • Scene segmentation and target modeling method based on concavo-convex and RSD features

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Embodiment Construction

[0035] The technical solution of the present invention will be further described below in conjunction with the accompanying drawings.

[0036] Step 1, use the PCL point cloud library, Realsense Grabber software package, Relasense SDK, RealsenseSR300 driver, and Realsense RS300 device to obtain the scene point cloud image;

[0037]Step 2, perform super-volume clustering over-segmentation on the basis of the denoised scene point cloud, control the super-voxel clustering process according to the formula (1), and divide the entire space;

[0038]

[0039] where (R seed =0.01m) is the distance of the seed, D c is the difference in color, D n Denotes the difference on the normal, D s Represents the difference in point distance. w c Indicates the weight of color difference in supervoxel similarity, w s Represents the weight of the point distance difference in the similarity, w n Represents the weight of the difference on the normal line in the similarity, which defines the ...

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Abstract

The invention discloses a scene segmentation and target extraction method based on concavo-convex and RSD features, and belongs to the technical field of computer vision. The method comprises the following steps: step 1, acquiring a scene point cloud by using Realsense and a PCL, and performing radius filtering; step 2, over-segmenting the scene point cloud based on hyper-body clustering; step 3,completing scene segmentation based on concave-convex re-clustering by using an LCCP method on the basis of the superbody clustering obtained in the step 2; and modeling the target object according tothe RSD geometrical characteristics of the segmented scene point cloud target object. Experimental results show that the target object modeling problem in any scene can be effectively solved. The method is a learning-free method, and has the advantages of low calculation time consumption and high occasion applicability.

Description

technical field [0001] The invention belongs to the technical field of computer vision, relates to the grasping suitable for robots in any scene, and can be used in the fields of unmanned driving, unmanned aerial vehicles, deep space detection, underwater robots and the like. Background technique [0002] With the development of science and technology, robots are becoming more and more intelligent. Currently, robots pursue autonomy and high intelligence. When a robot performs autonomous or intelligent tasks in an unknown environment, it needs to understand the environment. It needs to recognize and understand objects and objects in the current field of view or environment like the human brain, and perceive its own environment. Image segmentation technology is the key technology to enable robots to perceive and recognize the environment. With the rapid development of laser 3D ranging technology, there have been many 3D cameras that can collect 3D information in 3D space scen...

Claims

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Application Information

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IPC IPC(8): G06T7/10
Inventor 陈国华王耀增张爱军邢健康敬欣李季余洋洋
Owner BEIJING UNIV OF CHEM TECH
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