WO2022000713A1 - Procédé d'auto-positionnement par réalité augmentée basé sur un ensemble d'aviation - Google Patents

Procédé d'auto-positionnement par réalité augmentée basé sur un ensemble d'aviation Download PDF

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Publication number
WO2022000713A1
WO2022000713A1 PCT/CN2020/108443 CN2020108443W WO2022000713A1 WO 2022000713 A1 WO2022000713 A1 WO 2022000713A1 CN 2020108443 W CN2020108443 W CN 2020108443W WO 2022000713 A1 WO2022000713 A1 WO 2022000713A1
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Prior art keywords
assembly
scene
positioning
pose
self
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PCT/CN2020/108443
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English (en)
Chinese (zh)
Inventor
叶波
唐健钧
丁晓
常壮
金莹莹
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南京翱翔信息物理融合创新研究院有限公司
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Publication of WO2022000713A1 publication Critical patent/WO2022000713A1/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/05—Geographic models
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
    • G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
    • G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
    • G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/04—Indexing scheme for image data processing or generation, in general involving 3D image data

Definitions

  • the invention relates to the technical field of self-positioning, in particular to an augmented reality self-positioning method based on aviation assembly.
  • the virtual reality guided assembly has been widely used in the field of complex product assembly, but the virtual reality equipment can only provide a single virtual information, there is no information in the real environment, the sense of substitution is not strong, so the use of augmented reality equipment for aviation products Assembly guidance avoids the disadvantage that virtual reality equipment can only provide a single virtual scene information.
  • the core of augmented reality is multi-sensor fusion self-positioning technology, which is widely used in unmanned driving, sweeping robots, logistics robots and augmented reality. middle. Through sensors such as the camera and inertial measurement unit carried by the carrier, the position and attitude of the carrier relative to the environment can be obtained in real time.
  • the inertial measurement unit Combined with the inertial measurement unit, it has the advantages of good position and attitude estimation in a short time, and the camera will cause problems when the camera moves rapidly. Due to the shortcomings of fuzzy defects, the positioning accuracy of multi-sensor fusion is greatly improved. However, limited by the principle of visual positioning, in the blank area with fewer feature points, the device cannot be positioned and the positioning accuracy is poor.
  • the purpose of the present invention is to provide an augmented reality self-positioning method based on aviation assembly, which improves the long delivery cycle of aviation product assembly, the complex operation and the weak sense of substitution, aiming at the above-mentioned shortcomings of the prior art.
  • An augmented reality self-positioning method based on aviation assembly which includes designing a system framework, building an assembly scene, constructing a high-precision three-dimensional map of the assembly scene, building self-positioning scene information, designing a self-positioning vision system and a timing positioning process, specifically including the following step:
  • Step 1 The design system framework adopts the client-server parallel development mode to receive and send data.
  • the client is connected to the server wirelessly, and is used to transmit the assembly scene and assembly process information to the server.
  • the server is wirelessly connected to the client, and is used to transmit the parsed pose of the assembly scene feature points and label information to the client;
  • Step 2 After completing the system framework design of the first step, build an assembly scene.
  • the assembly scene includes a parts area, a to-be-assembled area, and a label area.
  • the label area includes a plurality of labels, which are used to associate the position and attitude relationship between the plurality of labels and transmit them to the server;
  • Step 3 After completing the construction of the assembly scene in the second step, construct a high-precision three-dimensional map of the assembly scene.
  • the construction of the high-precision three-dimensional map of the assembly scene first uses the distance information provided by the depth camera and the inertial measurement unit to obtain the density of the assembly scene. 3D map, then use the Apriltag tag to fill the dense 3D map of the assembly scene with information to create a discrete map, and then fuse the dense 3D map and the discrete map to form a high-precision 3D map of the assembly scene, and transmit it to the server;
  • Step 4 The high-precision three-dimensional map of the three-dimensional feature map of the assembly scene constructed in the step three is transmitted to the construction self-positioning scene information, and the construction self-positioning scene information is first analyzed.
  • Apriltag tags are attached to less areas to form a tag set of the assembly scene, and then the relative pose relationship between the tag sets is measured, and then the spatial position relationship of the assembly parts is established according to the assembly process and assembly manual, and transmitted to the server;
  • Step 5 The spatial position relationship of the construction self-positioning scene information in the step 4 is transmitted to the design self-positioning vision system, and the designing the self-positioning vision system includes creating a virtual model, real-time computing equipment pose and virtual and real scene fusion,
  • the created virtual model is connected to the pose of the real-time computing device, and is used for the AR development platform to build a three-dimensional scene, and the three-dimensional space coordinates of the virtual model are set according to the spatial positional relationship of the assembly parts, and then the augmented reality device is placed in the scene.
  • the real-time computing device pose is connected to the virtual and real scene fusion, and is used to load the virtual object to the client to realize the fusion display of the virtual object and the assembly scene;
  • Step 6 After completing the design of the self-positioning vision system in the fifth step, the timing positioning process is performed.
  • the timing positioning process first completes the initialization of the self-positioning vision system in the to-be-assembled area of the part, then loads the high-precision three-dimensional map and opens two thread, and then compare the poses of the two threads. If the error meets the set requirements, the self-positioning vision system outputs the fusion result positioning; The system outputs the corrected pose.
  • the client includes AR glasses, an inertial measurement unit and an industrial computer, the inertial measurement unit includes a sensor, and the industrial computer is connected to the sensor for controlling the sensor to transmit the calculated data to the server through a serial port.
  • step 3 the depth camera is used to collect a week's video of the assembly scene, perform feature extraction and optical flow tracking on the collected video images, filter the extracted video features, and then extract feature frames for feature point retention.
  • step 3 the information filling includes the key frame of the Apriltag label and the label corner point information corresponding to the key frame.
  • step 6 the loading of the high-precision three-dimensional map is divided into two threads, one thread is to detect the Apriltag label information in real time, then estimate the relative label pose of the depth camera according to the Apriltag label, and then convert the label and the spatial position relationship of the self-positioning scene.
  • the pose relative to the world coordinate; another thread is to fuse the inertial measurement unit according to the feature points in the assembly scene for fusion positioning, and obtain the pose of the depth camera relative to the world coordinate system in real time.
  • step 5 The specific steps of step 5 are: (1) Calculate the pose of the Apriltag tag; (2) Calculate the IMU pose; (3) Calculate the VSLAM pose; (4) Transfer the calculated pose to the server and fuse the virtual model. The three-dimensional space coordinates are then transmitted to the client for fusion display.
  • the device pose includes the pose of the Apriltag tag, the IMU pose, and the VSLAM pose.
  • the beneficial effects of the invention are as follows: the operator wears the augmented reality device, the server interprets the assembly instructions, and at the same time, the assembly instructions are presented in front of the operator in the form of virtual information, guiding the operator to go to the parts area to find parts, and guiding the operator to the area to be assembled. , instruct the operator to assemble the precautions, effectively improve the operator's understanding of the task, lower the operator's operating threshold, ensure the efficient and reliable completion of the assembly task, and can also accurately locate in the blank area with fewer feature points.
  • the beneficial effects of the invention are as follows: the operator wears the augmented reality device, the server interprets the assembly instructions, and at the same time, the assembly instructions are presented in front of the operator in the form of virtual information, guiding the operator to go to the parts area to find parts, and guiding the operator to the area to be assembled. , instruct the operator to assemble the precautions, effectively improve the operator's understanding of the task, lower the operator's operating threshold, ensure the efficient and reliable completion of the assembly task, and can also accurately locate in the blank area with fewer feature points.
  • FIG. 1 is a frame diagram of an augmented reality system of the present invention
  • Fig. 2 is the assembling scene frame diagram of the present invention
  • Fig. 3 is the collection flow chart of the high-precision three-dimensional map of the assembly scene of the present invention.
  • FIG. 4 is a flow chart of the real-time positioning technology of the assembly process and equipment of the present invention.
  • the present invention includes designing a system framework, building an assembly scene, building a high-precision three-dimensional map of the assembly scene, building self-positioning scene information, designing a self-positioning vision system and a timing positioning process, and specifically includes the following steps:
  • Step 1 The design system framework adopts the client-server parallel development mode to receive and send data.
  • the client is connected to the server wirelessly, and is used to transmit the assembly scene and assembly process information to the server.
  • the server is wirelessly connected to the client, and is used to transmit the parsed pose of the assembly scene feature points and label information to the client;
  • Step 2 After completing the system framework design of the first step, build an assembly scene.
  • the assembly scene includes a parts area, a to-be-assembled area, and a label area.
  • the label area includes a plurality of labels, which are used to associate the position and attitude relationship between the plurality of labels and transmit them to the server; wherein the position and posture relationship between the labels is obtained through the multiple labels. Select any label as the start label, set its position as the origin (0, 0, 0), the initial rotation attitude as (0, 0, 0), and the rest of the labels do displacement rotation relative to the start label, and the displacement rotation is as Position and rotation initial pose.
  • Step 3 After completing the construction of the assembly scene in the second step, construct a high-precision three-dimensional map of the assembly scene.
  • the construction of the high-precision three-dimensional map of the assembly scene first uses the distance information provided by the depth camera and the inertial measurement unit to obtain the density of the assembly scene. 3D map, then use the Apriltag tag to fill the dense 3D map of the assembly scene with information to create a discrete map, and then fuse the dense 3D map and the discrete map to form a high-precision 3D map of the assembly scene, and transmit it to the server;
  • Step 4 The high-precision three-dimensional map of the three-dimensional feature map of the assembly scene constructed in the step three is transmitted to the construction self-positioning scene information, and the construction self-positioning scene information is first analyzed.
  • Apriltag tags are attached to fewer areas to form a tag set of the assembly scene, and then the relative pose relationship between the tag sets is measured. Calculate the relative pose of the assembly scene, and then establish the spatial position relationship of the assembly parts according to the assembly process and assembly manual, and transmit them to the server;
  • Step 5 The spatial position relationship of the construction self-positioning scene information in the step 4 is transmitted to the design self-positioning vision system, and the designing the self-positioning vision system includes creating a virtual model, real-time computing equipment pose and virtual and real scene fusion,
  • the created virtual model is connected to the pose of the real-time computing device, and is used for the AR development platform to build a three-dimensional scene, and the three-dimensional space coordinates of the virtual model are set according to the spatial positional relationship of the assembly parts, and then the augmented reality device is placed in the scene.
  • the real-time computing device pose is fused with the virtual and real scene, and is used to load the virtual object into the AR glasses on the client to realize the fusion display of the virtual object and the assembly scene,
  • the device pose includes the pose of the Apriltag tag, the IMU pose, and the VSLAM pose.
  • Step 6 After completing the design of the self-positioning vision system in the fifth step, the timing positioning process is performed.
  • the timing positioning process first completes the initialization of the self-positioning vision system in the to-be-assembled area of the part, then loads the high-precision three-dimensional map and opens two thread, and then compare the poses of the two threads. If the error meets the set requirements, the self-positioning vision system outputs the fusion result positioning; The system outputs the corrected pose, where the label pose is detected by the depth camera on the augmented reality device, and then calculates the position and pose of the label relative to the augmented reality device.
  • the client includes AR glasses, an inertial measurement unit and an industrial computer, the inertial measurement unit includes a sensor, and the industrial computer is connected to the sensor for controlling the sensor to transmit the calculated data to the server through a serial port.
  • step 3 the depth camera is used to collect a week's video of the assembly scene, perform feature extraction and optical flow tracking on the collected video images, filter the extracted video features, and then extract feature frames for feature point retention.
  • step 3 the information filling includes the key frame of the Apriltag label and the label corner point information corresponding to the key frame.
  • step 6 the loading of the high-precision three-dimensional map is divided into two threads, one thread is to detect the Apriltag label information in real time, then estimate the relative label pose of the depth camera according to the Apriltag label, and then convert the label and the spatial position relationship of the self-positioning scene.
  • the pose relative to the world coordinate; another thread is to fuse the inertial measurement unit according to the feature points in the assembly scene for fusion positioning, and obtain the pose of the depth camera relative to the world coordinate system in real time.
  • step five The specific steps of step five are:
  • the coordinate system of the tag code is , the depth camera coordinate system is , any point on the label code
  • the coordinates in the depth camera coordinate system are , the corresponding relationship between the two is:
  • R is the rotation matrix representing the rotation of the depth camera coordinate system relative to the label code coordinate system
  • T is the translation vector representing the translation of the depth camera coordinate system relative to the label code
  • the image coordinate system is , the pixel coordinate system is , the point on the label code Imaging points in the image plane with the depth camera The corresponding relationship between them is:
  • formula (2) is the center of the image plane, are the normalized focal lengths of the x and y axes, is the depth camera internal parameter matrix, which can be used to convert the depth camera coordinate system to the image plane coordinate system; set , use the least squares method to solve formulas (1) and (2) to obtain the internal parameter matrix of the depth camera ; When the depth camera detects the tag, use the Apriltag algorithm to get R and T.
  • Step 1 Design the system framework: use the client-server parallel development mode to receive and send data. First, connect the AR glasses, inertial measurement unit and industrial computer in the client to the server wirelessly to connect the assembly scene. And the assembly process information is transmitted to the server, and then the server is wirelessly connected to the client to transmit the parsed pose of the assembly scene feature points and label information to the client;
  • Step 2 build an assembly scene: after completing the design system framework of step 1, assemble the parts to be assembled for aviation placed in the parts area in the to-be-assembled area, and then select label 1 in the 8 label areas arranged
  • the position of label 1 is set as the origin (0, 0, 0)
  • the initial rotation attitude is (0, 0, 0).
  • label 2 performs displacement rotation relative to label 1, and its displacement rotates
  • the rest of the labels are analogous, in which the rotation posture of each label is set to (0, 0, 0), that is, the spatial position of each label is adjusted to ensure that its orientation is consistent, such as Table 1 shows:
  • Step 3 Build a high-precision 3D map of the assembly scene: After completing the construction of the assembly scene in the second step, then use the depth camera to complete the initialization at label 1, and then perform video collection around the assembly scene, and the collected video images are processed. Feature extraction and optical flow tracking, filter the extracted video features, extract feature frames for feature point retention, and then combine with the distance information provided by the inertial measurement unit to obtain a dense three-dimensional map of the assembly scene; The dense 3D map of the assembly scene is filled with key frames and the label corner information corresponding to the key frames, and then a discrete map is established and fused with the dense 3D map to form a high-precision 3D map of the assembly scene;
  • Step 4 Build the self-positioning scene information: transfer the high-precision 3D map of the three-dimensional feature map of the assembly scene in step 3 to the building self-positioning scene information, then analyze the high-precision 3D map, and analyze the high-precision 3D map in the area with fewer feature points. Attach the artificial label Apriltag to form the label set of the assembly scene, then measure the relative pose relationship between the label sets, and then establish the spatial position relationship of the assembly parts according to the assembly process and assembly manual;
  • Step 5 Design a self-positioning vision system: transfer the spatial positional relationship of the building self-positioning scene information in step 4 to the design self-positioning vision system, which includes creating a virtual model, real-time computing device pose and virtual reality.
  • Scene fusion which connects the created virtual model with the pose of the real-time computing device, is used for the AR development platform to build a three-dimensional scene, and sets the three-dimensional spatial coordinates of the virtual model according to the spatial positional relationship of the assembled parts, and then places the augmented reality device on the In the scene, the pose of the depth camera in the device is calculated in real time, and then the real-time computing device pose is fused with the virtual and real scene to load the virtual object onto the AR glasses to realize the fusion display of the virtual object and the assembly scene;
  • Step 6 timing positioning process: After completing the design of the self-positioning vision system in the fifth step, the timing positioning process is carried out.
  • the timing positioning process first completes the initialization of the self-positioning vision system in the part to be assembled area, and then loads a high-precision three-dimensional map and Open two threads, and then compare the poses of the two threads. If the error meets the set requirements, the self-positioning vision system will output the fusion result positioning; if the error is too large, use the label pose to correct the fusion pose. Finally, the self-positioning vision system outputs the corrected pose, thereby completing the self-positioning of the aviation assembly.

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  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
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Abstract

Un procédé d'auto-positionnement par réalité augmentée basé sur un ensemble d'aviation est divulgué. Le procédé consiste : à concevoir une structure de système, à construire un scénario d'assemblage, à construire une carte tridimensionnelle à haute précision du scénario d'assemblage, à construire des informations de scénario d'auto-positionnement, à concevoir un système visuel à auto-positionnement, et à effectuer un processus de positionnement temporel. Au moyen de la présente invention, le degré de compréhension d'un opérateur concernant une tâche est efficacement amélioré, un seuil d'intervention de l'opérateur est réduit, l'achèvement efficace et fiable d'une tâche d'assemblage est garanti, et un positionnement précis peut également être obtenu dans une zone vierge avec moins de points caractéristiques.
PCT/CN2020/108443 2020-06-28 2020-08-11 Procédé d'auto-positionnement par réalité augmentée basé sur un ensemble d'aviation WO2022000713A1 (fr)

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CN202010597190.5A CN111968228B (zh) 2020-06-28 2020-06-28 一种基于航空装配的增强现实自定位方法
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