Monocular vision mileage calculation method based on deep learning and attention mechanism
A technology of monocular vision and deep learning, which is applied in computing, image data processing, instruments, etc., to achieve the effect of improving the overall speed, increasing the computing speed, and avoiding artificiality
Active Publication Date: 2019-12-03
UNIV OF ELECTRONICS SCI & TECH OF CHINA
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[0005] In view of the above-mentioned deficiencies in the prior art, the monocular visual mileage calculation method based on deep learning and attention mechanism provided by the present invention solves the problems existing in the traditional visual mileage calculation method
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[0053] The specific embodiments of the present invention are described below so that those skilled in the art can understand the present invention, but it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art, as long as various changes Within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions and creations using the concept of the present invention are included in the protection list.
[0054] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0055] Such as figure 1 As shown, a monocular visual odometry calculation method based on deep learning and attention mechanism, including the following steps:
[0056] S1. Use the attention mechanism to construct the attention mechanism module, and build a convolutional neural network on the basis...
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The invention discloses a monocular vision mileage calculation method based on deep learning and an attention mechanism, and the method comprises the specific steps: collecting an original monocular color image, and cutting the size of the image into a unified size; inputting the cut ith image and the (i + 1) th image into a PWCnet optical flow calculation module to obtain an optical flow vector field, and equally dividing the optical flow vector field into four optical flow sub-vector fields according to four quadrants; inputting the four optical flow sub-vector fields into a convolutional neural network, and respectively obtaining four sub-feature vectors; combining the four sub-feature vectors into a total feature vector, and then inputting the total feature vector into a fully connected network to obtain an estimated pose vector; and collecting pictures in real time, sending the pictures to the convolutional neural network in sequence to obtain a plurality of continuous estimated pose vectors, and obtaining estimated mileage through the plurality of continuous estimated pose vectors. The method has good visual mileage calculation precision and calculation speed.
Description
technical field [0001] The invention belongs to the field of autonomous positioning of mobile robots, and in particular relates to a monocular vision mileage calculation method based on deep learning and attention mechanism. Background technique [0002] Visual odometry technology is a front-end technology in simultaneous visual positioning and map construction. The inter-frame pose estimation obtained by the visual odometry can obtain a local map, and the local map can obtain a global map of the path traveled by the odometry after the back-end optimization. In this way, tasks such as map construction and 3D reconstruction can be further carried out. [0003] Visual odometry technology is widely used in mobile robot autonomous positioning, automatic driving, and virtual reality technology, and it is a hot research field in recent years. The main research task of visual odometry technology is to use visual feature vectors for accurate inter-frame pose estimation. Tradition...
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IPC IPC(8): G06T7/73
CPCG06T2207/10024G06T2207/20081G06T2207/20084G06T2207/20132G06T2207/30244G06T7/73
Inventor 肖卓凌刘旺蓝心悦郭志勇
Owner UNIV OF ELECTRONICS SCI & TECH OF CHINA