WO2021237443A1 - Procédé et appareil de positionnement visuel, dispositif et support d'enregistrement lisible - Google Patents

Procédé et appareil de positionnement visuel, dispositif et support d'enregistrement lisible Download PDF

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Publication number
WO2021237443A1
WO2021237443A1 PCT/CN2020/092284 CN2020092284W WO2021237443A1 WO 2021237443 A1 WO2021237443 A1 WO 2021237443A1 CN 2020092284 W CN2020092284 W CN 2020092284W WO 2021237443 A1 WO2021237443 A1 WO 2021237443A1
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photos
positioning
visual positioning
panoramic
neural network
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PCT/CN2020/092284
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English (en)
Chinese (zh)
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陈尊裕
吴珏其
胡斯洋
陈欣
吴沛谦
张仲文
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蜂图志科技控股有限公司
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Priority to CN202080001067.0A priority Critical patent/CN111758118B/zh
Priority to JP2022566049A priority patent/JP7446643B2/ja
Priority to PCT/CN2020/092284 priority patent/WO2021237443A1/fr
Publication of WO2021237443A1 publication Critical patent/WO2021237443A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/97—Determining parameters from multiple pictures
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/044—Recurrent networks, e.g. Hopfield networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20084—Artificial neural networks [ANN]

Definitions

  • This application relates to the field of positioning technology, and in particular to a visual positioning method, device, equipment, and readable storage medium.
  • the principle of visual positioning based on machine learning Use a large number of real scene photos with location markers for training, and get a neural network model whose input is a photo (RGB numerical matrix) and output is a specific location. After obtaining the trained neural network model, the user only needs to take a picture of the environment to get the specific shooting location.
  • This method needs to collect a large number of photo samples of the use environment as a training data set. For example, some documents record that in order to realize the visual positioning of a 35-meter-wide street corner store, 330 photos need to be collected, and in order to realize the visual positioning of a 140-meter street (positioning only on one side), 1,500 Multiple photos; in order to realize the positioning of a certain factory, the factory needs to be divided into 18 areas, and each area needs to take 200 images. It can be seen that in order to ensure the visual positioning effect, it is necessary to collect a large number of on-site photos as training data, and these photos must be taken to every corner of the scene, which is very time-consuming and labor-intensive.
  • the purpose of this application is to provide a visual positioning method, device, equipment, and readable storage medium, which use panoramic photos in a real map to train a neural network model, which can solve the problem of difficult sample collection in visual positioning.
  • a visual positioning method including:
  • the positioning model is a neural network model trained by using panoramic photos in a real map
  • the final position is determined.
  • said determining the final position using a plurality of said candidate positions includes:
  • the geometric center of the geometric figure is taken as the final positioning.
  • it also includes:
  • the standard deviation is used as the positioning error of the final positioning.
  • the process of training the neural network model includes:
  • the geographic mark includes a geographic location and a specific orientation
  • the neural network model is trained using the training samples, and the trained neural network model is determined as the positioning model.
  • said performing the anti-distortion transformation on several of said panoramic photos to obtain several groups of plane projection photos with the same aspect ratio including:
  • each of the panoramic photos is divided according to different focal length parameters, and several groups of plane projection photos with different viewing angles are obtained.
  • dividing each of the panoramic photos according to different focal length parameters to obtain several groups of plane projection photos with different viewing angles includes:
  • Each of the panoramic photos is segmented according to the number of segments corresponding to the original image coverage greater than a specified percentage, and several groups of adjacent images have plane projection photos with overlapping viewing angles.
  • the process of training the neural network model further includes:
  • the training samples are supplemented by using scene photos obtained from the Internet or environment photos collected from the positioning environment.
  • performing random segmentation on the wide-angle photos to obtain the atlas to be tested includes:
  • random division is performed on the wide-angle photo with an original image coverage greater than a specified percentage, and a set of atlases to be tested matching the number of divisions is obtained.
  • a visual positioning device includes:
  • the atlas to be tested acquisition module is used to acquire wide-angle photos, and randomly segment the wide-angle photos to obtain the atlas to be tested;
  • the candidate positioning acquisition module is used to input the atlas to be tested into a positioning model for positioning recognition to obtain multiple candidate positionings;
  • the positioning model is a neural network model trained by using panoramic photos in a real-world map;
  • the positioning output module is used to determine the final positioning by using a plurality of the candidate positionings.
  • a visual positioning device including:
  • Memory used to store computer programs
  • the processor is used to implement the above-mentioned visual positioning method when the computer program is executed.
  • the real map is a map where you can see the real street scene, and the real map includes a 360-degree real scene.
  • the panoramic photo in the real map is the real street view map, which overlaps with the application environment of visual positioning.
  • the neural network module is trained by using the panoramic photos in the real map to obtain a positioning model for visual positioning. After obtaining the wide-angle photos, perform random segmentation on the wide-angle photos to obtain the atlas to be tested. Input the atlas to be tested into the positioning model for positioning recognition, and then multiple candidate positionings can be obtained. Based on these candidate positions, the final position can be determined.
  • a positioning model can be obtained by training the neural network model based on the panoramic photos in the real scene map, and the visual positioning can be completed based on the positioning model, which solves the problem of difficulty in the collection of visual positioning training samples.
  • the embodiments of the present application also provide devices, equipment, and readable storage media corresponding to the above-mentioned visual positioning method, which have the above-mentioned technical effects, and will not be repeated here.
  • Fig. 1 is an implementation flowchart of a visual positioning method in an embodiment of the application
  • FIG. 2 is a schematic diagram of a perspective segmentation in an embodiment of this application.
  • FIG. 3 is a schematic structural diagram of a visual positioning device in an embodiment of the application.
  • FIG. 4 is a schematic structural diagram of a visual positioning device in an embodiment of this application.
  • Fig. 5 is a schematic diagram of a specific structure of a visual positioning device in an embodiment of the application.
  • the visual positioning method provided in the embodiment of the present invention can be directly applied to a cloud server, or in a local device.
  • the device that needs to be positioned can be positioned through a wide-angle photo if it has the functions of taking pictures and networking.
  • FIG. 1 is a flowchart of a visual positioning method in an embodiment of the application. The method includes the following steps:
  • Wide-angle that is, pictures taken with a wide-angle lens or panoramic mode. Simply put, the smaller the focal length, the wider the field of view, and the wider the range of the scene that can be accommodated in the photo.
  • the panoramic photos in the real map are used to train the neural network model. Therefore, in order to better perform visual positioning, when using the positioning model for visual positioning, the required photos are also wide-angle photos.
  • the user can use the wide-angle mode (or ultra-wide-angle mode) or the panoramic mode to take a picture of the surrounding environment at a location that needs to be positioned.
  • the angle of view exceeds 120 degrees (of course, it can also be other degrees, such as 140 degrees, 180 degrees, etc.). Photo.
  • the wide-angle photos are obtained, they are randomly divided to obtain the atlas to be tested composed of several divided photos.
  • the specific number of photos divided into the wide-angle photo can be set according to the training effect of the positioning model of the world and the actual positioning accuracy requirements.
  • the larger the number of divisions the higher the positioning accuracy.
  • the more training iterations of the model The longer the training time.
  • the wide-angle photo when segmenting the wide-angle photo, can also be randomly divided according to the number of divisions with the original image coverage greater than a specified percentage, to obtain the atlas to be tested matching the number of divisions.
  • the wide-angle photos can be randomly divided into N pieces with an aspect ratio of 1:1 (it should be noted that the aspect ratio can also be other ratios, and the aspect ratio is the same as the aspect ratio of the training sample used for training the positioning model. That is, the image whose height is 1/3 to 1/2 of the height of the wide-angle photo is used as the atlas to be measured.
  • the number of N is set according to the training effect and positioning accuracy needs.
  • N When the training effect is slightly poor and the positioning accuracy is high, select a higher value of N.
  • the number of N can be set to 100 (of course, other values can also be selected , Such as 50, 80, etc., will not be enumerated here one by one).
  • the random segmentation result requires the coverage of the original image (that is, the wide-angle photo) to be >95% (of course, it can also be set to other percentages, which will not be enumerated here).
  • S102 Input the atlas to be tested into the positioning model for positioning recognition, and obtain multiple candidate positioning.
  • the positioning model is a neural network model trained by using panoramic photos in the real map.
  • each segmented photo in the atlas to be measured is input into the positioning model for positioning recognition, and an output about the positioning result is obtained for each photo.
  • the positioning result corresponding to each divided photo is used as a candidate positioning.
  • the process of training a neural network model includes:
  • Step 1 Obtain a number of panoramic photos from the real-world map, and determine the geographic location of each real-world photo;
  • Step 2 Perform anti-distortion transformation on several panoramic photos to obtain several groups of plane projection photos with the same aspect ratio;
  • Step 3 Mark a geographic mark for each group of plane projection photos according to the correspondence with the panoramic photos; the geographic mark includes the geographic location and the specific orientation;
  • Step 4 Use the geo-tagged plane projection photos as training samples
  • Step 5 Use the training samples to train the neural network model, and determine the trained neural network model as the positioning model.
  • the panoramic photo can be subjected to anti-distortion transformation, and then several groups of plane projection photos with the same length ratio can be obtained. Since there is a corresponding relationship between the panoramic photos and the geographic locations in the real scene map, in this embodiment, the geographic locations of a group of planar projection photos divided from the same panoramic photo correspond to the geographic locations of the panoramic photos. In addition, when dividing a panoramic photo, the segmentation is performed based on the angle of view. Therefore, the orientation of the divided photo is also clear. In this embodiment, the geographic location and the specific orientation are used as geographic markers and added. In other words, every flat projection photo has a corresponding geographic location and specific orientation.
  • the trained neural network model is the positioning model. Specifically, a collection of photos with specific locations and specific orientations can be used as the data pool. Randomly select 80% of the data pool as the training set, and the remaining 20% as the test set. The ratio can also be adjusted according to the actual training situation. Input the training set into the initialized or pre-trained neural network model of the large-scale image set for training, and use the test set to verify the training results.
  • CNN Convolutional Neural Network
  • convolutional neural network which is a feedforward neural network, including convolutional layer (alternating convolutional layer) and pooling layer) and its derivative structure
  • LSTM Long Short-Term Memory, long and short-term memory network
  • RNN time recurrent neural network
  • hybrid structures etc.
  • the specific neural network used is not limited.
  • the panoramic photos can be segmented according to different focal length parameters, so as to obtain plane projection photos with different viewing angles as training samples.
  • each panoramic photo can be divided according to different focal length parameters in the anti-distortion transformation to obtain several groups of plane projection photos with different viewing angles. That is, the number of divisions n is determined according to the focal length parameter F.
  • Fig. 2 is a schematic diagram of a viewing angle segmentation in an embodiment of this application.
  • the focal length parameter F can also be changed to other values, such as 1.0 and 1.3, to obtain plane projection photos with other perspectives.
  • the panoramic photo when segmenting the panoramic photo, can also be segmented according to the number of segments corresponding to the original image coverage greater than a specified percentage. That is, under the same viewing angle, the adjacent pictures have a flat projection photo with a covering angle. Specifically, each panoramic photo is segmented according to the number of segments corresponding to the original image coverage greater than a specified percentage, and several groups of adjacent images have overlapping perspective plane projection photos. That is, in order to enrich the shooting angle of the photo, it is recommended that the number of divisions be greater than the number of equal divisions when the focal length is fixed.
  • the axis perpendicular to the ground of the panoramic photo projection spherical surface is the rotation axis, and the center of the line of sight (the arrow in the figure 2) is rotated every 45 degrees to split a plane projection photo with a viewing angle of 90 degrees.
  • the adjacent pictures There will be a 45-degree overlapping viewing angle.
  • the orientation data is then marked for the resulting flat projection photo. Because the value of F can also be 1.0 and 1.3, the viewing angle is about 60 degrees and 30 degrees, respectively, and the value of n can also be 12 and 24. You can also set more F values and increase the number of n to further improve the coverage of the training set. Generally, the coverage rate is greater than 95%.
  • the process of training the neural network model can also be used from Use the Internet to obtain scene photos, or supplement the training samples with environmental photos collected from the positioning environment.
  • the final location can be determined based on these candidate locations. After obtaining the final positioning, it can be output for the user to view.
  • one location can be randomly selected from the candidate locations as the final location, or several candidate locations can be randomly selected from the candidate locations, and the geometric centers of geometric figures corresponding to these candidate locations can be taken as the final location.
  • candidate locations with a high degree of overlap can also be used as the final location.
  • the candidate positions can be clustered and filtered, and the candidate positions that are free from most positioning positions can be removed, and then based on The remaining candidate positions determine the final position.
  • the implementation process includes:
  • Step 1 Perform clustering processing on multiple candidate locations, and use the clustering results to screen multiple candidate locations;
  • Step 2 Use the selected candidate locations to construct geometric figures
  • Step three take the geometric center of the geometric figure as the final positioning.
  • a clustering algorithm such as DBSCAN (Density-Based Spatial Clustering of Applications with Noise) can be used to classify candidate locations, and adjacent location data can be classified into one category.
  • the positioning error can also be determined.
  • the final location is used to calculate the standard deviation of multiple candidate locations; the standard deviation is used as the location error of the final location. That is, the variance between each candidate location and the final location is calculated and accumulated to obtain the final location error.
  • the real map is a map where you can see the real street scene, and the real map includes a 360-degree real scene.
  • the panoramic photo in the real map is the real street view map, which overlaps with the application environment of visual positioning.
  • the neural network module is trained by using the panoramic photos in the real map to obtain a positioning model for visual positioning. After obtaining the wide-angle photos, perform random segmentation on the wide-angle photos to obtain the atlas to be tested. Input the atlas to be tested into the positioning model for positioning recognition, and then multiple candidate positionings can be obtained. Based on these candidate positions, the final position can be determined.
  • a positioning model can be obtained by training the neural network model based on the panoramic photos in the real scene map, and the visual positioning can be completed based on the positioning model, which solves the problem of difficulty in the collection of visual positioning training samples.
  • the embodiments of the present application also provide corresponding improvement solutions.
  • the same steps as in the above-mentioned embodiments or the corresponding steps can be referred to each other, and the corresponding beneficial effects can also be referred to each other, which will not be repeated in the preferred/improved embodiments herein.
  • the embodiment of the present application also provides a visual positioning device.
  • the visual positioning device described below and the visual positioning method described above can be referred to each other.
  • the visual positioning device includes:
  • the atlas to be tested acquisition module 101 is used to acquire wide-angle photos, and randomly segment the wide-angle photos to obtain the atlas to be tested;
  • the candidate location acquisition module 102 is used to input the atlas to be tested into a location model for location recognition to obtain multiple candidate locations;
  • the location model is a neural network model trained by using panoramic photos in the real map;
  • the positioning output module 103 is used for determining the final positioning using multiple candidate positionings.
  • the wide-angle photos are obtained, and the wide-angle photos are randomly divided to obtain the atlas to be tested; the atlas to be tested is input to the positioning model for positioning recognition, and multiple candidate positions are obtained; the positioning model is Use the neural network model trained on the panoramic photos in the real map; use multiple candidate locations to determine the final location.
  • the real map is a map where you can see the real street scene, and the real map includes a 360-degree real scene.
  • the panoramic photo in the real map is the real street view map, which overlaps with the application environment of visual positioning.
  • the neural network module is trained by using the panoramic photos in the real map to obtain a positioning model for visual positioning. After obtaining the wide-angle photos, perform random segmentation on the wide-angle photos to obtain the atlas to be tested. Input the atlas to be tested into the positioning model for positioning recognition, and multiple candidate positionings can be obtained. Based on these candidate positions, the final position can be determined.
  • a positioning model can be obtained by training the neural network model based on the panoramic photos in the real map, and based on the positioning model, the visual positioning can be completed, which solves the problem of difficulty in the collection of visual positioning training samples.
  • the positioning output module 103 specifically includes:
  • the positioning screening unit is used to perform clustering processing on multiple candidate locations, and use the clustering results to screen multiple candidate locations;
  • the geometric figure construction unit is used to construct a geometric figure by using several candidate positions obtained by screening;
  • the final positioning determining unit is used to take the geometric center of the geometric figure as the final positioning.
  • the positioning output module 103 further includes:
  • the positioning error determining unit is used to calculate the standard deviation of multiple candidate positioning by using the final positioning; the standard deviation is used as the positioning error of the final positioning.
  • the model training module includes:
  • the panoramic photo obtaining unit is used to obtain several panoramic photos from the real-world map and determine the geographic location of each real-world photo;
  • the anti-distortion transformation unit is used to perform anti-distortion transformation on several panoramic photos to obtain several groups of plane projection photos with the same aspect ratio;
  • the geotagging unit is used to tag each group of plane projection photos with geotags according to the corresponding relationship with the panoramic photos; the geotags include geographic location and specific orientation;
  • the training sample determination unit is used to use the geographic-tagged plane projection photos as the training sample
  • the model training unit is used to train the neural network model using training samples, and determine the trained neural network model as a positioning model.
  • the anti-warping transformation unit is specifically used to segment each panoramic photo according to different focal length parameters in the anti-warping transformation to obtain several groups of plane projection photos with different viewing angles.
  • the anti-distortion transformation unit is specifically used to divide each panoramic photo according to the number of divisions whose coverage ratio of the corresponding original image is greater than a specified percentage, to obtain planes with overlapping viewing angles in several groups of adjacent pictures Project photos.
  • model training module further includes:
  • the sample supplement unit is used to supplement the training samples by using the scene photos obtained from the Internet or the environment photos collected from the positioning environment.
  • the atlas acquisition module 101 to be tested is specifically configured to perform random segmentation of the wide-angle photo with the original image coverage greater than a specified percentage according to the number of segments, to obtain the image to be tested matching the number of segments set.
  • the embodiment of the present application also provides a visual positioning device.
  • the visual positioning device described below and the visual positioning method described above can be referenced correspondingly.
  • the visual positioning device includes:
  • the memory 410 is used to store computer programs
  • the processor 420 is configured to implement the steps of the visual positioning method provided in the foregoing method embodiment when executing a computer program.
  • FIG. 5 is a schematic diagram of a specific structure of a visual positioning device provided by this embodiment.
  • the visual positioning device may have relatively large differences due to different configurations or performance, and may include one or more processors ( Central processing units, CPU) 420 (for example, one or more processors) and memory 410, one or more of which store computer application programs 413 or data 412.
  • the memory 410 may be short-term storage or persistent storage.
  • the computer application program may include one or more modules (not shown in the figure), and each module may include a series of instruction operations on the data processing device.
  • the central processing unit 420 may be configured to communicate with the memory 410 and execute a series of instruction operations in the memory 410 on the visual positioning device 301.
  • the visual positioning device 400 may also include one or more power supplies 430, one or more wired or wireless network interfaces 440, one or more input and output interfaces 450, and/or one or more operating systems 411.
  • the steps in the visual positioning method described above can be implemented by the structure of the visual positioning device.
  • the embodiment of the present application also provides a readable storage medium, and a readable storage medium described below and a visual positioning method described above can be referenced correspondingly.
  • a readable storage medium in which a computer program is stored, and when the computer program is executed by a processor, the steps of the visual positioning method provided by the foregoing method embodiment are implemented.
  • the readable storage medium can specifically be a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk, or an optical disk that can store program codes. Readable storage medium.

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Abstract

Procédé et appareil de positionnement visuel, dispositif, et support d'enregistrement lisible, le procédé comprenant : l'acquisition d'une photo à grand angle, et la segmentation aléatoire de la photo à grand angle pour obtenir un atlas à mesurer (S101) ; et l'entrée de l'atlas dans un modèle de positionnement pour une identification de position pour obtenir une pluralité de positions candidates, le modèle de positionnement étant un modèle de réseau de neurones artificiels formé à l'aide des photos panoramiques dans une carte réelle (S102) ; et la détermination de la position finale à l'aide de la pluralité de positions candidates (S103). Le modèle de positionnement peut être obtenu par formation du modèle de réseau de neurones artificiels sur la base des photos panoramiques dans la carte réelle, et le positionnement visuel peut être achevé sur la base du modèle de positionnement, ce qui résout le problème de la difficulté de collecte d'échantillons d'apprentissage pour un positionnement visuel.
PCT/CN2020/092284 2020-05-26 2020-05-26 Procédé et appareil de positionnement visuel, dispositif et support d'enregistrement lisible WO2021237443A1 (fr)

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