Robot vision SLAM closed-loop detection method based on stack type combined auto-encoder

A technology of robot vision and autoencoder, which is applied in the direction of neural learning methods, instruments, computer parts, etc., can solve the problems of dimension explosion and easy loss of network features, achieve good accuracy, improve accuracy and robustness , The effect of good feature robustness

Inactive Publication Date: 2020-10-09
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

Traditional stacked autoencoders are usually stacked with multiple layers of the same autoencoder. This kind of network is easy to lose features or cause dimensionality explosion problems.

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  • Robot vision SLAM closed-loop detection method based on stack type combined auto-encoder
  • Robot vision SLAM closed-loop detection method based on stack type combined auto-encoder
  • Robot vision SLAM closed-loop detection method based on stack type combined auto-encoder

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Embodiment Construction

[0048] The technical solutions in the embodiments of the present invention will be described clearly and in detail below with reference to the drawings in the embodiments of the present invention. The described embodiments are only some of the embodiments of the invention.

[0049] The technical scheme that the present invention solves the problems of the technologies described above is:

[0050] Aiming at the deficiencies of the existing technology, a stacked combined autoencoder composed of multi-layer stacking of noise reduction autoencoder, convolutional autoencoder and sparse autoencoder is designed to extract the features of the scene image, and then Use the output features for loop closure detection. This network model based on unsupervised learning has excellent performance in terms of generalization ability and robustness, effectively improving the accuracy and robustness of closed-loop detection, and the data set used during training does not need to carry labels, r...

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Abstract

The invention discloses a robot vision SLAM closed-loop detection method based on a stack type combined auto-encoder, and belongs to the field of mobile robot vision SLAM. The method comprises the following steps: S1, preprocessing a visual SLAM scene image, and inputting the visual SLAM scene image into a stack type auto-encoder model; s2, training a network model layer by layer, iterating network parameters by adopting a stochastic gradient descent algorithm, and continuously adjusting the model parameters to minimize a reconstruction error; s3, extracting a feature vector of the visual SLAMscene image by using the trained stack type combined auto-encoder; s4, calculating the similarity between the feature vector VK of the kth key frame (current frame) of the visual SLAM scene and the feature vectors V1, V2,..., VN of the historical key frames; and S5, comparing the similarity score with a set threshold value, and if the similarity score is greater than the set threshold value, judging that a closed loop is formed. According to the invention, the accuracy and robustness of visual SLAM closed-loop detection can be effectively improved.

Description

technical field [0001] The invention belongs to the field of mobile robot visual SLAM, in particular to a visual SLAM closed-loop detection method based on a stacked combined autoencoder. Background technique [0002] Simultaneous Localization and Mapping (SLAM) refers to the real-time positioning and construction of quantitative environmental maps by robots during their movement in an unknown environment. Visual SLAM uses a camera as a sensor to build a three-dimensional environment map in real time. A complete visual SLAM system mainly includes four modules: front-end visual odometry, back-end nonlinear optimization, loop closure detection and mapping. Loop closure detection is a key module in visual SLAM, which plays a very important role in eliminating accumulated errors. Closed-loop detection refers to judging whether the robot has returned to a certain position that already exists in the map when the current observation information and map information are given. Mos...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/10G06N3/048G06N3/045G06F18/241
Inventor 罗元肖雨婷张毅
Owner CHONGQING UNIV OF POSTS & TELECOMM
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