EP4323965A1 - Verfahren und vorrichtung zum annotieren von mit hilfe einer kamera aufgenommenen bildern eines objektes - Google Patents
Verfahren und vorrichtung zum annotieren von mit hilfe einer kamera aufgenommenen bildern eines objektesInfo
- Publication number
- EP4323965A1 EP4323965A1 EP22718134.4A EP22718134A EP4323965A1 EP 4323965 A1 EP4323965 A1 EP 4323965A1 EP 22718134 A EP22718134 A EP 22718134A EP 4323965 A1 EP4323965 A1 EP 4323965A1
- Authority
- EP
- European Patent Office
- Prior art keywords
- camera
- end effector
- coordinates
- key points
- keypoint
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Program-controlled manipulators
- B25J9/16—Program controls
- B25J9/1679—Program controls characterised by the tasks executed
- B25J9/1692—Calibration of manipulator
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
- G06T7/73—Determining position or orientation of objects or cameras using feature-based methods
- G06T7/75—Determining position or orientation of objects or cameras using feature-based methods involving models
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Program-controlled manipulators
- B25J9/16—Program controls
- B25J9/1602—Program controls characterised by the control system, structure, architecture
- B25J9/161—Hardware, e.g. neural networks, fuzzy logic, interfaces, processor
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/80—Analysis of captured images to determine intrinsic or extrinsic camera parameters, i.e. camera calibration
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
- G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V20/00—Scenes; Scene-specific elements
- G06V20/70—Labelling scene content, e.g. deriving syntactic or semantic representations
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B25—HAND TOOLS; PORTABLE POWER-DRIVEN TOOLS; MANIPULATORS
- B25J—MANIPULATORS; CHAMBERS PROVIDED WITH MANIPULATION DEVICES
- B25J9/00—Program-controlled manipulators
- B25J9/16—Program controls
- B25J9/1679—Program controls characterised by the tasks executed
- B25J9/1687—Assembly, peg and hole, palletising, straight line, weaving pattern movement
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; 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]
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30164—Workpiece; Machine component
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30204—Marker
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30244—Camera pose
Definitions
- the present invention relates to a method and a device for annotating images recorded with the aid of a camera according to the independent claims.
- the object of the present invention is to automate a method for annotating images recorded by a camera.
- the aforementioned object is achieved by a method for annotating images of an object recorded with the aid of a camera, the camera being arranged on a robot arm and the robot arm comprising an end effector.
- the method includes determining a position and an orientation of the camera relative to the end effector.
- the camera includes a camera coordinate system.
- the robot arm which is preferably arranged on a movable robot, has the end effector.
- the end effector is in particular the free end of the robot arm. In other words, it is the last arm link of the robot arm, which in particular can include a gripping arm.
- an end effector can be a hand.
- the world coordinates of the end effector are known, with the relation of the world coordinates of the end effector to the camera being determined by determining a position and an orientation of the camera relative to the end effector. In other words a connection is established between the world coordinate system and the camera coordinate system.
- the determination of a position and an orientation of the camera relative to the end effector can in particular include a hand-eye calibration.
- a calibration object for example an image with a checkerboard pattern
- This calibration object is recorded by the camera from different positions and different orientations of the camera.
- the captured images are saved along with the different positions and orientations. Since the configuration of the calibration object, for example the exact pattern and dimensions of the individual square areas in the case of a chessboard pattern, is known, a connection between the position and orientation of the end effector and the camera perspective, in other words the camera coordinate system, can be determined.
- the world coordinates of the camera can thus be derived in the world coordinate system.
- the camera can therefore be calibrated extrinsically.
- optical errors in the camera can also be determined in the manner described above.
- a lens distortion can be detected, for example, by a distortion of the calibration object shown in a recorded image.
- the camera can therefore also be intrinsically calibrated, since the imaging depends on the optical errors, for example lens distortions.
- the method includes choosing a first keypoint on the object.
- the first key point is chosen freely.
- the method includes the provision of information from further key points relative to the first key point in object coordinates, that is to say in other words in an object coordinate system.
- the key points are thus previously defined points on the object.
- the provision of this information can primarily include measuring the respective position of the further key points in relation to the first key point on the object. This can include, for example, manually measuring the relative positions of the other key points in relation to the first key point.
- the method can include using already existing information about the dimensions of the object. The relative positions can be determined on the basis of the information already available, for example using technical drawings or dimensions from DIN standards.
- the method includes guiding the end effector to the position of the first keypoint. In other words, the end effector is brought to the exact position of the first keypoint.
- guiding to the position of the first keypoint involves manually moving the end effector to the first keypoint.
- the end effector can in particular be a plug, and the object can be a socket.
- the plug can be inserted into the socket and thus the end effector can be guided to the first key point.
- the robot can in particular be a mobile charging robot.
- the method includes determining the position and orientation of the end effector at the first keypoint in world coordinates. From these, the world coordinates of the first key point can be deduced. Since the position and the orientation of the end effector are known in world coordinates, or they can be determined in a simple manner after a movement of the robot and/or the robot arm, the position of the first key point can be read out. In particular, it is previously determined exactly which point of the end effector is guided to the first key point, whereby it is known where this point is located on the end effector, so that its world coordinates are always known, or can be derived after a corresponding movement of the robot arm or the robot . The world coordinates of this point then correspond to the world coordinates of the first keypoint.
- the position of the further keypoints in world coordinates can be determined on the basis of the now determined position of the first keypoint in world coordinates.
- the method includes changing the position and/or the orientation of the end effector and recording an image using the camera.
- the image is in 2D and includes a 2D camera image coordinate system.
- the method can include a conversion of the world coordinates of the key points into the camera coordinate system of the camera. In this way, camera coordinates, in other words 3D camera coordinates, of the key points are determined. In other words, the positions of the Keypoints determined relative to the camera. This step is done using the relationship between the world coordinate system and the camera coordinate system that was determined earlier.
- the method includes the determination of camera image coordinates, in other words 2D camera image coordinates, of all key points by means of a projection from their camera coordinates.
- camera image coordinates of all key points in the recorded image are determined from the 3D camera coordinates using projective geometry.
- the determined positions can be marked on the image. In this way, the captured image is annotated.
- the term "annotation" is to be understood in particular as identifying previously defined points, in other words the key points, in the recorded image.
- the present method thereby provides an automated keypoint annotation method, so that manual identification of keypoints on images is unnecessary. Thus, the method does not include any manual identification of key points on captured images.
- Manual identification means in particular manual annotation of the images.
- the present method has the clear advantage that human errors are avoided and images can be annotated in a large number in a very effective manner.
- the method also does not include the application of markings to the object to identify key points in recorded images.
- the method can include storing the determined camera image coordinates of all key points.
- the steps between changing the position and/or aligning the end effector and taking an image using the camera and determining the camera image coordinates of all key points, especially including the storage of the determined camera image coordinates, are carried out repeatedly in order to create a large number of annotated images generate.
- training data for an artificial network can be created, these training data being the annotated images.
- training data for a mobile charging robot which is intended to charge a vehicle autonomously, for example, can be created in order to train the neural network in such a way that the mobile charging robot can guide the end effector, in other words the plug, fully automatically into the socket of the vehicle.
- a further aspect comprises a device for annotating images of an object recorded with the aid of a camera, the device being designed to carry out the method described above.
- the device primarily has an evaluation unit and a control unit.
- the control unit serves to control the robot arm and/or the robot and/or the camera.
- the evaluation unit is used in particular to determine a position and an alignment of the camera relative to the end effector, to select a first key point on the object, to evaluate information provided on the relative positions of the other key points to the first key point, to determine the position and alignment of the end effector on the first keypoint in world coordinates, to determine the position of the first keypoint and the other keypoints in world coordinates, to determine camera coordinates of the keypoints by converting the world coordinates of the keypoints into a camera coordinate system of the camera and to determine camera image coordinates of all keypoints by means of projection from their camera coordinates and for marking this.
- the device can include a storage unit for storing the image and the determined camera image coordinates of the key points.
- the device comprises the robotic arm, in turn comprising the end effector, and a camera.
- the device may include the robot including the robot arm.
- FIG. 1 shows a process diagram of a process according to the invention
- FIGS 2 to 8 different process steps of the method according to the invention.
- FIG. 1 shows a method sequence of a method 100 according to the invention, which includes the determination 101 of a position and an alignment of the camera 14 relative to the end effector 13 as a first step.
- This step can in particular include a hand-eye calibration 102 .
- the method also includes selecting 103 a first key point 20 on the object 30 of which images are to be recorded.
- the method 100 further includes the Provision 104 of information from further key points relative to the first key point 20 in object coordinates.
- the provision 104 can include measuring 105 the position of the further keypoints relative to the first keypoint 20 on the object 30 or using 106 already existing information about the dimensions of the object 30.
- the method 100 includes guiding the end effector 13 to the position of the first key point.
- this can include a manual movement 109 of the end effector 13 to the first key point 20 .
- the method 100 includes determining 111 the position and alignment of the end effector 13 at the first keypoint 20 and determining 112 the position of the first keypoint in world coordinates and determining 113 the position of the other keypoints in world coordinates.
- the method 100 includes a change 114 in the position and/or alignment of the end effector 13 and a recording of an image 31 by means of the camera 14.
- the camera coordinates of the key points can be determined 115 with the aid of a conversion of the world coordinates of the key points into the camera coordinate system of the camera 14
- camera image coordinates of all key points can be determined from the camera coordinates and marked on the image 116.
- the camera image coordinates of all key points are preferably stored 117. Steps 114 to 117 are carried out repeatedly, so that in this way a large number of annotated images can be used as training data for a cheap network to be created 118.
- FIG. 2 shows the step of determining 101 a position and an alignment of the camera 14 of a device 10 according to the invention relative to the end effector 13.
- the end effector 13 is arranged on a robot arm 12 of a robot 11.
- Figure 3 there is illustrated how a first keypoint 20 is selected 103 in an object coordinate system 21 on an object 30.
- the first keypoint 20 is the center of the top opening of the socket which the object 30 is.
- Figure 4 shows how a step-by-step movement 109 of the robot arm 12 to the object 30 and the first keypoint 20 moves the end effector 13 to the position of the first keypoint 20 to be led.
- the end effector 13 is designed as a plug with two contact pins, the upper contact pin in FIG. 4 being inserted into the upper opening of the socket.
- the central point on the fixed end of the upper contact pin is the point on the end effector that is at the first key point when the plug is in the socket. Since its world coordinates are known, the world coordinates of the first key point 20 are determined in this way.
- Figure 5 shows how the position of the other key points is determined in world coordinates 113.
- a second key point 23 is shown as an example and its conversion into the world coordinate system 22 based on the information about the relative position to the first key point 20.
- Figure 6 shows how the position and orientation of the end effector 13 is changed 114 and an image of the object 30 is recorded by the camera 114.
- FIG. 7 shows purely schematically how the camera coordinates of the key points are determined 115. In other words, the positions of the key points relative to the camera 14 are determined.
- Figure 8 shows how the camera image coordinates of the key points are determined by means of projection from their camera coordinates 116. It is shown purely schematically how, by means of projective geometry, the camera image coordinates of the key points are determined from the camera coordinates of the key points, which are shown on the right, in an image 31, which is of the camera 14 can be determined.
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Robotics (AREA)
- Mechanical Engineering (AREA)
- Automation & Control Theory (AREA)
- Multimedia (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Fuzzy Systems (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Manipulator (AREA)
Abstract
Description
Claims
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| DE102021203779.8A DE102021203779B4 (de) | 2021-04-16 | 2021-04-16 | Verfahren und Vorrichtung zum Annotieren von mit Hilfe einer Kamera aufgenommenen Bildern eines Objektes |
| PCT/EP2022/057763 WO2022218670A1 (de) | 2021-04-16 | 2022-03-24 | Verfahren und vorrichtung zum annotieren von mit hilfe einer kamera aufgenommenen bildern eines objektes |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| EP4323965A1 true EP4323965A1 (de) | 2024-02-21 |
Family
ID=81384938
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| EP22718134.4A Pending EP4323965A1 (de) | 2021-04-16 | 2022-03-24 | Verfahren und vorrichtung zum annotieren von mit hilfe einer kamera aufgenommenen bildern eines objektes |
Country Status (5)
| Country | Link |
|---|---|
| US (1) | US12589500B2 (de) |
| EP (1) | EP4323965A1 (de) |
| CN (1) | CN117136384A (de) |
| DE (1) | DE102021203779B4 (de) |
| WO (1) | WO2022218670A1 (de) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US12544933B2 (en) * | 2024-07-24 | 2026-02-10 | Ruby.AI Robotic Technologies Ltd. | Techniques, machine learning, and mechanisms for enabling supply of energy to devices |
Family Cites Families (22)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP1472052A2 (de) | 2002-01-31 | 2004-11-03 | Braintech Canada, Inc. | Verfahren und vorrichtung für 3d-sicht geführte roboter mit einer kamera |
| CA2369845A1 (en) * | 2002-01-31 | 2003-07-31 | Braintech, Inc. | Method and apparatus for single camera 3d vision guided robotics |
| DE10345743A1 (de) | 2003-10-01 | 2005-05-04 | Kuka Roboter Gmbh | Verfahren und Vorrichtung zum Bestimmen von Position und Orientierung einer Bildempfangseinrichtung |
| DE102005051533B4 (de) | 2005-02-11 | 2015-10-22 | Vmt Vision Machine Technic Bildverarbeitungssysteme Gmbh | Verfahren zur Verbesserung der Positioniergenauigkeit eines Manipulators bezüglich eines Serienwerkstücks |
| AT506865B1 (de) | 2008-05-20 | 2010-02-15 | Siemens Vai Metals Tech Gmbh | Vorrichtung zum verbessern von genauigkeitseigenschaften von handhabungsgeräten |
| US8923602B2 (en) | 2008-07-22 | 2014-12-30 | Comau, Inc. | Automated guidance and recognition system and method of the same |
| JP5383836B2 (ja) | 2012-02-03 | 2014-01-08 | ファナック株式会社 | 検索ウィンドウを自動的に調整する機能を備えた画像処理装置 |
| JP2016221645A (ja) | 2015-06-02 | 2016-12-28 | セイコーエプソン株式会社 | ロボット、ロボット制御装置およびロボットシステム |
| US9916506B1 (en) | 2015-07-25 | 2018-03-13 | X Development Llc | Invisible fiducial markers on a robot to visualize the robot in augmented reality |
| KR101980603B1 (ko) | 2016-05-20 | 2019-05-22 | 구글 엘엘씨 | 오브젝트(들)를 캡처하는 이미지(들)에 기초하는 그리고 환경에서의 미래 로봇 움직임에 대한 파라미터(들)에 기초하여 로봇 환경에서의 오브젝트(들)의 모션(들)을 예측하는 것과 관련된 머신 학습 방법들 및 장치 |
| DE202017106506U1 (de) | 2016-11-15 | 2018-04-03 | Google Llc | Einrichtung für tiefes Maschinenlernen zum Robotergreifen |
| CN106920261B (zh) * | 2017-03-02 | 2019-09-03 | 江南大学 | 一种机器人手眼静态标定方法 |
| CN108597036B (zh) * | 2018-05-03 | 2022-04-12 | 三星电子(中国)研发中心 | 虚拟现实环境危险感知方法及装置 |
| DE102020102350A1 (de) | 2019-01-30 | 2020-07-30 | ese-robotics GmbH | Greifvorrichtung und Verfahren zu deren Ausrichtung und zur Störfall-Erkennung |
| CN109948494B (zh) * | 2019-03-11 | 2020-12-29 | 深圳市商汤科技有限公司 | 图像处理方法及装置、电子设备和存储介质 |
| DE102019106458A1 (de) | 2019-03-13 | 2020-09-17 | ese-robotics GmbH | Verfahren zur Ansteuerung eines Industrieroboters |
| WO2020212776A1 (en) * | 2019-04-18 | 2020-10-22 | Alma Mater Studiorum - Universita' Di Bologna | Creating training data variability in machine learning for object labelling from images |
| US12399567B2 (en) | 2019-09-20 | 2025-08-26 | Nvidia Corporation | Vision-based teleoperation of dexterous robotic system |
| DE102020124285B4 (de) * | 2019-09-20 | 2022-06-09 | Nvidia Corporation | Visionsbasierte Teleoperation eines beweglichen Robotersystems |
| CN111360780A (zh) | 2020-03-20 | 2020-07-03 | 北京工业大学 | 一种基于视觉语义slam的垃圾捡拾机器人 |
| CN111862048B (zh) * | 2020-07-22 | 2021-01-29 | 浙大城市学院 | 基于关键点检测和深度卷积神经网络的鱼体姿态与长度自动分析方法 |
| US11508089B2 (en) * | 2021-03-05 | 2022-11-22 | Black Sesame Technologies Inc. | LiDAR assisted wheel encoder to camera calibration |
-
2021
- 2021-04-16 DE DE102021203779.8A patent/DE102021203779B4/de active Active
-
2022
- 2022-03-24 US US18/555,693 patent/US12589500B2/en active Active
- 2022-03-24 EP EP22718134.4A patent/EP4323965A1/de active Pending
- 2022-03-24 CN CN202280028470.1A patent/CN117136384A/zh active Pending
- 2022-03-24 WO PCT/EP2022/057763 patent/WO2022218670A1/de not_active Ceased
Also Published As
| Publication number | Publication date |
|---|---|
| US12589500B2 (en) | 2026-03-31 |
| CN117136384A (zh) | 2023-11-28 |
| DE102021203779B4 (de) | 2023-12-14 |
| DE102021203779A1 (de) | 2022-10-20 |
| WO2022218670A1 (de) | 2022-10-20 |
| US20240198535A1 (en) | 2024-06-20 |
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