WO2019196478A1 - Positionnement de robot - Google Patents

Positionnement de robot Download PDF

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Publication number
WO2019196478A1
WO2019196478A1 PCT/CN2018/121180 CN2018121180W WO2019196478A1 WO 2019196478 A1 WO2019196478 A1 WO 2019196478A1 CN 2018121180 W CN2018121180 W CN 2018121180W WO 2019196478 A1 WO2019196478 A1 WO 2019196478A1
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WO
WIPO (PCT)
Prior art keywords
image
feature point
coordinate system
image collector
robot
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Application number
PCT/CN2018/121180
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English (en)
Chinese (zh)
Inventor
郝立良
申浩
程保山
Original Assignee
北京三快在线科技有限公司
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Application filed by 北京三快在线科技有限公司 filed Critical 北京三快在线科技有限公司
Publication of WO2019196478A1 publication Critical patent/WO2019196478A1/fr

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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
    • G05D1/0231—Control of position or course in two dimensions specially adapted to land vehicles using optical position detecting means
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/02—Control of position or course in two dimensions
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis

Definitions

  • the present application relates to the field of navigation technology, and in particular to robot positioning.
  • the method of robot navigation by laser can generate a two-dimensional map by the obstacle of the area where the laser scanning robot is located, and the robot can determine the driving route based on the position of the obstacle in the two-dimensional map, thereby realizing navigation.
  • the current robot is capable of determining the surrounding obstacles by laser scanning at the time of starting, it is not possible to determine its position in the coordinate system. Therefore, it is necessary to manually indicate the position of the robot in the coordinate system when the robot is started, so that the robot can perform path planning in the two-dimensional map according to the position to realize navigation.
  • embodiments of the present invention provide a robot positioning method, a robot positioning device, and a computer readable storage medium, and a robot.
  • a robot positioning method includes: acquiring an image by an image collector on the robot, extracting feature points from the image; and determining and storing in a pre-stored feature point library. a target sample feature point matched by the feature point; a coordinate according to the pre-stored target sample feature point in a preset coordinate system, and the target sample feature point and the image collector at the preset coordinate The coordinate mapping relationship in the system determines the coordinates of the image collector in the preset coordinate system.
  • a robot positioning apparatus includes: a feature point extraction module, configured to extract a feature point from an image collected by an image collector on the robot; and a feature point matching module, And determining, by using a pre-stored feature point library, a target sample feature point that matches the feature point; and a first coordinate determining module, configured to use, according to the pre-stored coordinate of the target sample feature point in a preset coordinate system And a coordinate mapping relationship between the target sample feature point and the image collector in the preset coordinate system, and determining coordinates of the image collector in the preset coordinate system.
  • a computer readable storage medium having stored thereon a computer program that, when executed by a processor, performs the above-described robot positioning method.
  • a robot comprising a laser sensor and an image collector, further comprising a processor, wherein the processor is configured to perform the robot positioning method described above.
  • the coordinates of the robot in the preset coordinate system can be determined, and The coordinates are calibrated so that operations can be performed based on the coordinates so that the navigation path can be planned in the navigation map starting from the coordinates of the image collector in the preset coordinate system. Therefore, it is not necessary to manually instruct the coordinates of the robot to realize completely autonomous navigation of the robot.
  • FIG. 1 is a schematic flow chart of a robot positioning method according to an embodiment of the present invention.
  • FIG. 2 is a schematic flow chart of another robot positioning method according to an embodiment of the present invention.
  • FIG. 3 is a schematic flow chart showing determining a positional relationship and a posture relationship of a laser sensor and an image collector, according to an embodiment of the present invention.
  • FIG. 4 is a diagram showing depth information, position information, and angle information of an image collector when acquiring a sample image according to an embodiment of the present invention, determining that a sample feature point and an image collector are preset.
  • FIG. 5 is a schematic flow chart of extracting feature points from an image by acquiring an image by an image collector, according to an embodiment of the invention.
  • FIG. 6 is a schematic flow chart of still another robot positioning method according to an embodiment of the present invention.
  • FIG. 7 is a schematic flow chart showing navigation path planning in a navigation map matching a preset coordinate system according to coordinates of the image collector in a preset coordinate system according to an embodiment of the present invention.
  • FIG. 8 is a schematic diagram showing a hardware configuration of a device in which a robot positioning device is located, according to an embodiment of the present invention.
  • FIG. 9 is a schematic block diagram of a robotic positioning device, in accordance with an embodiment of the present invention.
  • FIG. 10 is a schematic block diagram of another robotic positioning device shown in accordance with an embodiment of the present invention.
  • FIG. 11 is a schematic block diagram of still another robotic positioning device shown in accordance with an embodiment of the present invention.
  • FIG. 12 is a schematic block diagram of a path planning module, shown in accordance with an embodiment of the present invention.
  • first, second, third, etc. may be used to describe various information in this application, such information should not be limited to these terms. These terms are only used to distinguish the same type of information from each other.
  • first information may also be referred to as the second information without departing from the scope of the present application.
  • second information may also be referred to as the first information.
  • word "if” as used herein may be interpreted as "when” or “when” or “in response to a determination.”
  • FIG. 1 is a schematic flow chart of a robot positioning method according to an embodiment of the present invention.
  • the method shown in this embodiment can be applied to a robot, which can include a laser sensor and an image collector.
  • the image capture device can be a monocular camera or a binocular camera.
  • the image acquired by the image collector can be a depth image.
  • the image collector can be rotated, for example, by 360° in a preset plane to capture images in different directions.
  • the laser sensor can emit and receive laser light. Also, the laser sensor can be rotated, for example, by rotating 360° in a predetermined plane to emit laser light in a direction in which it is directed.
  • the laser sensor and image collector may be in the same plane or in different planes.
  • the robot positioning method may include the following steps:
  • step S1 an image is acquired by the image collector, and feature points are extracted from the image.
  • the image acquired by the image collector may be an image or multiple images.
  • the number of sheets specifically collected can be set as needed.
  • the number of feature points extracted from the image may be the same or different, and the number of specifically extracted feature points may be set as needed. For example, for one image, the number of extracted feature points may be greater than or equal to six.
  • Step S2 determining a target sample feature point that matches the feature point in a pre-stored feature point library.
  • the sample feature points may be pre-stored, for example, a feature point library composed of sample feature points is generated in advance, and the feature point library stores coordinates of each sample feature point in a preset coordinate system.
  • the coordinate mapping relationship between the sample feature points and the image collector in the preset coordinate system may also be stored in the feature point library (for example, including a positional relationship and an attitude relationship, which may be represented by a matrix).
  • the coordinate mapping relationship can also be stored in other storage spaces than the feature point library.
  • the description information of the sample feature points may also be stored in the feature point library. For example, if the granularity of the sample feature points is a pixel in the image, the description information may include gray values of several (eg, 8) pixels around the pixel as the sample feature point. The description information may also include information such as the category of the sample feature points, the position of the sample feature points in the image, and the like.
  • the description information of the feature point may be determined, and then the sample feature point whose description information matches the description information of the feature point may be queried in the pre-stored feature point library, that is, the target sample feature point.
  • Step S3 Determine coordinates of the image collector in a preset coordinate system according to a coordinate mapping relationship between the target sample feature point and the image collector in a preset coordinate system.
  • the coordinate mapping relationship between the target sample feature point and the image collector in the preset coordinate system and the coordinates of the target sample feature point in the preset coordinate system may be stored in the feature point library,
  • the coordinates of the target sample feature points in the preset coordinate system are converted according to the coordinate mapping relationship, thereby deriving the coordinates of the image collector in the preset coordinate system.
  • the image collector is disposed on the robot, after determining the coordinates of the image collector in the preset coordinate system, the coordinates of the robot in the preset coordinate system can be determined. Therefore, it is not necessary to manually instruct the coordinates of the robot, and it is convenient to realize the completely autonomous navigation of the robot.
  • FIG. 2 is a schematic flow chart of another robot positioning method according to an embodiment of the present invention. As shown in FIG. 2, the robot positioning method may include the following steps:
  • Step S4 Before acquiring the sample image by the image collector, determining a positional relationship and a posture relationship of the laser sensor with respect to the image collector.
  • the positional relationship may refer to an offset of the image collector relative to the laser sensor along the x-axis in the predetermined coordinate system, an offset along the y-axis, and an offset along the z-axis.
  • the attitude relationship may refer to a rotation angle and an elevation angle of a direction in which the image collector acquires an image with respect to a laser sensor emission direction.
  • Step S5 determining a first coordinate of the laser sensor in a preset coordinate system.
  • the first coordinate of the laser sensor in a preset coordinate system can be determined by SLAM (Simultaneous Localization And Mapping).
  • the coordinates determined by the SLAM may be two-dimensional coordinates, and if the preset coordinate system is three-dimensional coordinates, the two-dimensional coordinates may be converted into three-dimensional coordinates, wherein the z-axis coordinates are zero.
  • Step S6 Convert the first coordinate according to the position relationship and the attitude relationship to obtain a second coordinate of the image collector in a preset coordinate system.
  • Step S7 collecting a sample image by the image collector, and extracting a plurality of sample feature points from the sample image.
  • multiple image images may be acquired by the image collector, and one or more sample feature points may be separately extracted for each sample image, and the number of sample feature points extracted for each sample image may be the same or different.
  • the plurality of sample feature points thus acquired are stored in the feature point library.
  • Step S8 Determine the coordinate mapping relationship according to depth information, position information of the sample feature point in the sample image, and angle information when the image collector collects the sample image.
  • the image acquired by the image collector may be a depth image.
  • the feature point has a granularity of pixels, and the depth image contains depth information for each pixel. Based on the depth information, the distance from the pixel to the image collector, that is, the distance from the feature point to the image collector, can be determined.
  • the distance and the position information of the sample feature point in the sample image (for example, the pixel of the sample feature point corresponding to the first few rows in the sample image), and the angle information when the image collector collects the sample image, Determining the coordinate mapping relationship.
  • the sample feature point is 100 pixels directly above the corresponding center point in the image collected by the image collector, wherein the length of each pixel may be preset, for example, L, then the length of 100 pixels is 100L. Since the sample feature point to the center of the image, the center of the image to the image collector, and the image collector to the sample feature point can form a right triangle, the distance D from the image collector to the sample feature point can be a hypotenuse, at 100L. At the corner, the length of the other right-angled edge is obtained according to the Pythagorean theorem, that is, the distance d from the center of the image to the image collector.
  • the coordinate mapping relationship may be: the sample feature point is located in the direction of the rotation angle ⁇ of the image collector, and to the image collector The distance is the plane of d, and is located 100L away from the center directly above the center of the plane. This content can be represented by a matrix.
  • Step S9 Determine, according to the second coordinate and the coordinate mapping relationship, a third coordinate of the sample feature point in a preset coordinate system.
  • the second coordinate may be further converted according to the coordinate mapping relationship to determine a third coordinate of the sample feature point.
  • the coordinate mapping relationship is represented by a matrix, and then the third coordinate can be obtained by multiplying the second coordinate with the matrix.
  • Step S10 storing the third coordinate and the coordinate mapping relationship.
  • a feature point library may be generated, and the feature point library may include coordinates of the sample feature points in the preset coordinate system for each sample feature point.
  • the coordinate mapping relationship between the sample feature points and the image collector in the preset coordinate system (for example, can be represented by a matrix).
  • steps S4 to S10 may be pre-executed before the robot navigation, and the coordinate mapping relationship between the sample feature points and the image collector in the preset coordinate system is pre-stored through the feature point library, so as to facilitate subsequent determination of the image collector.
  • the coordinates in the preset coordinate system for example, when performing steps S1 to S3, the target sample feature points matching the feature points extracted from the image may be determined in the feature point library, and the pre-stored coordinate mapping relationship is used in advance.
  • the stored target sample feature points are converted in the coordinates in the preset coordinate system, thereby obtaining the coordinates of the image collector in the preset coordinate system.
  • the coordinate accuracy of the feature point in the preset coordinate system is determined to be low only based on the result of the laser sensor scan.
  • the image acquired by the image collector is relatively less susceptible to environmental interference, that is, the coordinate mapping relationship between the feature points in the image and the image collector in the preset coordinate system is relatively accurate, and the feature point library of the embodiment is Combined with the results of the laser sensor scan and the images acquired by the image collector, the coordinates of the feature points can be determined relatively accurately by matching in the feature point library.
  • determining the positional relationship and the attitude relationship of the laser sensor with respect to the image collector may include: step S401, determining a positional relationship of the laser sensor with respect to the image collector according to a nonlinear optimization algorithm. And attitude relationship.
  • the positional relationship and attitude relationship of the laser sensor relative to the image collector can be determined by manual measurement or by a nonlinear optimization algorithm.
  • the nonlinear optimization algorithm adopted is a least square method. Since the position of the laser sensor and the image collector on the robot is relatively fixed, the positional relationship and the attitude relationship of the laser sensor with respect to the image collector are relatively fixed.
  • a plurality of known points may be set in the space, and for a known point, a laser sensor may be used to emit laser light to the known point and receive the reflected laser light, thereby determining the positional relationship of the known point with respect to the laser sensor and
  • the attitude relationship is represented, for example, by matrix A, and the spatial coordinates of the laser sensor are represented as matrix P).
  • the image of the point can be acquired by the image collector, thereby determining the positional relationship and the attitude relationship of the known point with respect to the image collector (for example, represented by a matrix B, and the spatial coordinates of the image collector are represented as a matrix Q).
  • a plurality of sets of matrices P, Q, and C can be separately measured, and a plurality of sets of matrices P, Q, and C can be calculated by using a least squares method to obtain a relatively accurate matrix C to represent the laser sensor relative to image acquisition.
  • the positional relationship and attitude relationship of the device Since the nonlinear optimization algorithm can be executed by software, the positional relationship and the attitude relationship of the laser sensor with respect to the image collector can be determined more accurately with respect to manual measurement.
  • determining the coordinate mapping relationship according to the depth information of the sample feature points in the sample image, the position information, and the angle information when the image collector acquires the sample image includes:
  • Step S801 determining the coordinate mapping relationship according to the depth information, the position information, the angle information, and the imaging model of the image collector of the sample feature point in the sample image.
  • the imaging model of the image collector is different, for example, the image collector is a pinhole camera, and the imaging model is a pinhole model, for example, the image collector is a fisheye camera, then the imaging model is a fisheye model.
  • the coordinate mapping relationship in different imaging models is different from the corresponding relationship between the depth information, the position information, and the angle information of the sample feature points in the sample image. Therefore, when determining the coordinates of the feature points, it is advantageous to determine the coordinates of the feature points more accurately by considering the imaging model of the image collector.
  • the model can be represented by the following relationship:
  • m' is the uv coordinate of the feature point
  • A is the camera internal parameter
  • t] is the relationship between the camera and the preset coordinate system (such as the world coordinate system)
  • M' is the feature point and the preset coordinate system (for example, world coordinates) Relationship
  • s is the z coordinate of the object in the camera coordinate system.
  • the camera internal reference mentioned here refers to the parameter determined only by the camera itself, that is, once a value is calculated for a certain camera, it is not necessary to perform calculation again.
  • FIG. 5 is a schematic flow diagram of extracting an image from the image by acquiring an image by the image collector, according to an embodiment of the invention.
  • the image is collected by the image collector, and extracting feature points from the image includes: step S101, when the robot is started, an image is collected by the image collector, and the image is extracted from the image. Feature points.
  • step S1 may be performed when the robot is started, that is, an image is acquired by the image collector when the robot is started, and feature points are extracted from the image. According to this, it can be ensured that as long as the robot is started, the coordinates in the preset coordinate system can be determined, thereby completing the autonomous navigation.
  • FIG. 6 is a schematic flow chart of still another robot positioning method according to an embodiment of the present invention. As shown in FIG. 6, on the basis of the embodiment shown in FIG. 1, the robot positioning method may further include:
  • Step S11 generating a navigation map by scanning the area where the robot is located by the laser sensor;
  • the navigation map generated by the laser sensor scanning the area where the robot is located may be a two-dimensional map.
  • the laser sensor can scan an obstacle in a range of a predetermined distance from the area where the robot is located to the robot, and the laser reflected by the obstacle can determine the position of the obstacle in the area, and then generate a navigation map according to the position of the obstacle.
  • a navigation map is generated, for example, by SLAM.
  • Step S12 matching the navigation map and the preset coordinate system.
  • the navigation map is a two-dimensional map and the preset coordinate system is a three-dimensional coordinate system
  • only two dimensions in the three-dimensional coordinate system may be matched to the navigation map.
  • a two-dimensional map is a map parallel to a horizontal plane.
  • the x-axis and the y-axis are axes parallel to the horizontal plane, and then the x-axis and the y-axis in the three-dimensional coordinate system can be matched to the navigation map, thereby navigating the map.
  • the x-axis coordinate and the y-axis coordinate can be calibrated.
  • Step S13 Perform navigation path planning on the navigation map matching the preset coordinate system according to the coordinates of the image collector in the preset coordinate system.
  • the coordinates of the robot in the preset coordinate system can be determined, and the calibration is determined in the navigation map.
  • the coordinates can therefore be calculated according to the coordinates so that the navigation path can be planned in the navigation map starting from the coordinates of the image collector in the preset coordinate system. Therefore, it is not necessary to manually instruct the coordinates of the robot to realize completely autonomous navigation of the robot.
  • the navigation path planning can be performed according to the amcl (adaptive Monte Carlo Localization) positioning algorithm, costmap (cost map) and path planning algorithm.
  • FIG. 7 is a schematic flow chart showing navigation path planning in a navigation map matching a preset coordinate system according to coordinates of the image collector in a preset coordinate system according to an embodiment of the present invention.
  • the navigation path planning in the navigation map matching the preset coordinate system according to the coordinates of the image collector in the preset coordinate system includes: Step S1301, according to the image collector Positioning on the robot determines a projection of the contour of the robot in the preset coordinate system; step S1302, according to the projection, performs navigation path planning in a navigation map matching the preset coordinate system.
  • the contour of the robot can be determined according to the position of the image collector on the robot at the preset coordinates. Projection in the system, and according to the projection in the navigation map matching the preset coordinate system for navigation path planning, to ensure that the robot does not touch the obstacles in the path, to ensure that the robot moves smoothly and smoothly. And according to the projection of the robot, the orientation of the robot in the preset coordinate system can also be determined, so that the navigation path can be easily planned.
  • the present application also provides an embodiment of the robot positioning device.
  • the embodiment of the robot positioning device of the present application can be applied to a device such as a robot.
  • the device embodiment may be implemented by software, or may be implemented by hardware or a combination of hardware and software.
  • the processor of the device in which it is located reads the corresponding computer program instructions in the non-volatile memory into the memory.
  • FIG. 8 a hardware structure diagram of the device where the robot positioning device is located, except for the processor 801, the memory 802, the network interface 803, and the non-volatile memory shown in FIG.
  • the device in which the device is located in the embodiment is usually other hardware according to the actual function of the device, and details are not described herein.
  • FIG. 9 is a schematic block diagram of a robotic positioning device, in accordance with an embodiment of the present invention.
  • the robot includes a laser sensor and an image collector, as shown in FIG. 9, the robot positioning device includes:
  • a feature point extraction module 1 for acquiring an image by the image collector, and extracting feature points from the image
  • a feature point matching module 2 configured to determine, in a pre-stored feature point library, a target sample feature point that matches the feature point;
  • a first coordinate determining module 3 configured to: according to pre-stored coordinates of the target sample feature point in a preset coordinate system, and coordinate mapping between the target sample feature point and the image collector in a preset coordinate system And determining coordinates of the image collector in a preset coordinate system.
  • FIG. 10 is a schematic block diagram of another robotic positioning device shown in accordance with an embodiment of the present invention. As shown in FIG. 10, on the basis of the embodiment shown in FIG. 9, the robot positioning device further includes:
  • a relationship determining module 4 configured to determine a positional relationship and a posture relationship of the laser sensor with respect to the image collector;
  • a second coordinate determining module 5 configured to determine a first coordinate of the laser sensor in a preset coordinate system
  • a coordinate conversion module 6 configured to convert the first coordinate according to the positional relationship and the attitude relationship to obtain a second coordinate of the image collector in a preset coordinate system
  • the feature point extraction module 1 is further configured to collect a sample image by using the image collector, and extract a plurality of sample feature points from the sample image;
  • the mapping determination module 7 is configured to determine the coordinate mapping relationship according to the depth information, the position information of the sample feature point in the sample image, and the angle information when the image collector acquires the sample image;
  • the third coordinate determining module 8 is configured to determine, according to the second coordinate and the coordinate mapping relationship, a third coordinate of the sample feature point in a preset coordinate system;
  • the storage module 9 is configured to store a third coordinate of the sample feature point in a preset coordinate system and a coordinate mapping relationship between the sample feature point and the image collector in a preset coordinate system.
  • the relationship determination module 4 is configured to determine a positional relationship and an attitude relationship of the laser sensor relative to the image collector according to a nonlinear optimization algorithm.
  • the mapping determining module 7 is configured to determine, according to the depth information, the position information, the angle information, and the imaging model of the image collector of the sample feature point in the sample image. Coordinate mapping relationship.
  • the feature point extraction module 1 is configured to acquire an image from the image by the image collector when the robot is started, and extract feature points from the image.
  • FIG. 11 is a schematic block diagram of still another robotic positioning device shown in accordance with an embodiment of the present invention. As shown in FIG. 11, on the basis of the embodiment shown in FIG. 9, the robot positioning device further includes:
  • a map generating module 10 configured to generate a navigation map by scanning, by the laser sensor, an area where the robot is located;
  • a map matching module 11 configured to match the navigation map and the preset coordinate system
  • the path planning module 12 is configured to perform navigation path planning in a navigation map matching the preset coordinate system according to coordinates of the image collector in a preset coordinate system.
  • the path planning module 12 is a schematic block diagram of a path planning module, shown in accordance with an embodiment of the present invention. As shown in FIG. 10, on the basis of the embodiment shown in FIG. 11, the path planning module 12 includes:
  • a projection determining sub-module 1201 configured to determine a projection of the contour of the robot in the preset coordinate system according to a position of the image collector on the robot;
  • the path planning sub-module 1202 is configured to perform navigation path planning in the navigation map matching the preset coordinate system according to the projection.
  • Embodiments of the present invention also provide a computer readable storage medium having stored thereon a computer program that, when executed by a processor, performs the robot positioning method of any of the above embodiments.
  • Embodiments of the present invention also provide a robot, including a laser sensor and an image collector, further comprising a processor, wherein the processor is configured to perform the robot positioning method of any of the above embodiments.
  • the device embodiment since it basically corresponds to the method embodiment, reference may be made to the partial description of the method embodiment.
  • the device embodiments described above are merely illustrative, wherein the units described as separate components may or may not be physically separate, and the components displayed as units may or may not be physical units, ie may be located A place, or it can be distributed to multiple network units. Some or all of the modules may be selected according to actual needs to achieve the objectives of the present application. Those of ordinary skill in the art can understand and implement without any creative effort.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Automation & Control Theory (AREA)
  • Electromagnetism (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Control Of Position, Course, Altitude, Or Attitude Of Moving Bodies (AREA)
  • Length Measuring Devices By Optical Means (AREA)

Abstract

L'invention concerne un procédé et un dispositif de positionnement de robot. Le procédé de positionnement de robot comprend les étapes consistant à : acquérir une image au moyen d'une unité d'acquisition d'image sur un robot, et extraire un point caractéristique à partir de l'image (S1) ; déterminer, dans la bibliothèque de points caractéristiques pré-stockés, un point caractéristique d'échantillon cible qui correspond au point caractéristique (S2) ; et déterminer les coordonnées de l'unité d'acquisition d'image dans un système de coordonnées prédéfini en fonction des coordonnées du point caractéristique d'échantillon cible pré-stocké dans le système de coordonnées prédéfini et la relation de mappage de coordonnées entre le point caractéristique d'échantillon cible et l'unité d'acquisition d'image dans le système de coordonnées prédéfini (S3).
PCT/CN2018/121180 2018-04-13 2018-12-14 Positionnement de robot WO2019196478A1 (fr)

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CN201810330896.8A CN110377015B (zh) 2018-04-13 2018-04-13 机器人定位方法和机器人定位装置

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