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Image processing method in underwater vision SLAM system

An image processing and vision technology, applied in image data processing, image analysis, instruments, etc., can solve the problems of difficulty in real-time performance, affect the real-time performance of algorithms, and consume a lot of time, achieve rapid and accurate extraction, overcome feature point redundancy, Overcome the effect of poor real-time performance

Active Publication Date: 2015-04-29
JIANGSU UNIV OF SCI & TECH IND TECH RES INST OF ZHANGJIAGANG
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Problems solved by technology

However, the SIFT algorithm has many advantages, but real-time performance is a major problem it faces, mainly because the SIFT algorithm only needs to successfully match 3 or more feature points for a target due to the large number of feature descriptors. to confirm the existence of the target
However, the SIFT algorithm can often provide thousands of feature points in an image, and 50% to 80% of these feature points are not used and need to be filtered out. They will consume a lot of time in the process of extraction and matching. time, which seriously affects the real-time performance of the algorithm

Method used

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  • Image processing method in underwater vision SLAM system
  • Image processing method in underwater vision SLAM system

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Embodiment

[0043] like figure 1 As shown, an image processing method in an underwater visual SLAM system comprises the following steps:

[0044] Step 1: Build a visual SLAM system model

[0045] Step 1.1: Build a carrier motion model

[0046] The carrier motion model is to model the motion of the robot under effective external force and noise conditions. Its main function is to calculate the state of the robot at the next moment based on the motion state of the carrier at the previous moment. The model is expressed by the following formula:

[0047] x v (k+1)=F v [x v (k), u v (k+1),k+1]+V v (k+1)

[0048] where x v (k) is the carrier state vector at time k, the equation F v [ ] is the dynamic equation of the carrier, u v (k+1) is the effective inside and outside input at time k+1, V v Indicates some uncertain factors.

[0049] For these unpredictable factors, the measure we take is to introduce a random variable to simulate these uncertain factors, using V v , and it satisf...

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Abstract

The invention discloses an image processing method in an underwater vision SLAM (Simultaneous Localization And Mapping) system. The image processing method comprises the following steps: establishing an underwater imaging model; processing influence of underwater environment factors on a camera imaging; carrying out image feature extraction and matching. According to the method, extraction on feature points of an underwater environment image in the later period is reinforced; according to a data correlation method which is newly disclosed on the basis of the improved SLAM system, the feature points are extracted in a matched manner, so that the feature points can be more rapidly and accurately extracted; real-time performance of an SIFT algorithm is improved. The method which uses a relative position factor of a binocular camera and a way point position factor as auxiliary conditions for carrying out correlation can effectively solve the problems of mismatching and poor matching efficiency in the data correlation.

Description

technical field [0001] The invention relates to an underwater visual SLAM system, in particular to an image processing method in the underwater visual SLAM system. Background technique [0002] As an important development direction of autonomous navigation of mobile robots, vision-based SLAM systems have gradually received attention in recent years. SLAM (Simultaneous Positioning and Mapping) technology is the key technology for the intelligentization of mobile robots. The current visual SLAM mainly relies on the method of road signs, that is, the environment information of the robot is obtained through the visual sensor, and the information is used to locate and compose the image itself. The implementation method is the EKF (Extended Kalman Filter) commonly used in SLAM ) and PF (Particle filter, particle filter) and other methods. [0003] Montiel and others mainly studied the SLAM problem based on monocular vision, and they mainly used the method of inverse depth parame...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06V10/462G06V10/757G06V20/05
Inventor 杨平乐潘志宏张仕杰陈文博程海洋
Owner JIANGSU UNIV OF SCI & TECH IND TECH RES INST OF ZHANGJIAGANG
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