CN113327289A - Method for simultaneously calibrating internal and external parameters of multi-source heterogeneous sensor - Google Patents
Method for simultaneously calibrating internal and external parameters of multi-source heterogeneous sensor Download PDFInfo
- Publication number
- CN113327289A CN113327289A CN202110537517.4A CN202110537517A CN113327289A CN 113327289 A CN113327289 A CN 113327289A CN 202110537517 A CN202110537517 A CN 202110537517A CN 113327289 A CN113327289 A CN 113327289A
- Authority
- CN
- China
- Prior art keywords
- camera
- parameters
- external parameters
- internal
- calibration
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR 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
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10024—Color image
-
- 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
- G06T2207/10—Image acquisition modality
- G06T2207/10032—Satellite or aerial image; Remote sensing
- G06T2207/10044—Radar image
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Manufacturing & Machinery (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The invention relates to the technical field of robot sensing, in particular to a simultaneous internal and external reference calibration method of a multi-source heterogeneous sensor, which comprises the following steps: preprocessing a calibration plate; acquiring image point data of a multi-source heterogeneous sensor consisting of a three-dimensional laser radar, a vision camera, a thermal camera and an inertia measurement unit; calculating internal parameters of a visual camera and a thermal camera; calculating external parameters of the radar, the vision camera and the thermal sensing camera; calculating external parameters of the vision camera, the thermal camera and the inertia measurement unit; and optimizing the graph to obtain the final external parameters. Compared with the prior art, the invention has the following advantages: the use complexity is low, in order to realize the calibration of the sensor suite, various different calibration plates are often needed to be used in the prior art, and the process is complicated. The accuracy is high, and weights are given according to the uncertainty of parameters among different sensors so as to acquire the optimal parameters. The user-friendly calibration plate is user-friendly, excessive professional knowledge is not needed, and an ordinary user only needs to hold the sensor suite to acquire calibration plate data.
Description
Technical Field
The invention relates to the technical field of robot sensing, in particular to a simultaneous internal and external reference calibration method of a multi-source heterogeneous sensor.
Background
With the advent of the intelligent era, particularly the development of mobile robots, multi-source heterogeneous sensors are being increasingly applied to various intelligent systems. This is mainly due to the complementary properties of the different sensors, in particular: three-dimensional lidar can provide accurate distance information, but three-dimensional point clouds are often sparse and lack texture information. Visual cameras can just provide rich, dense texture information, but they are sensitive to light and do not work well in low light conditions. The thermal camera is insensitive to illumination and can robustly work under different illumination conditions.
Although the three sensors have advantages, the three sensors have a common defect that the frame rate is low, and the three sensors cannot be applied to high-speed dynamic scenes. The inertial measurement unit IMU generally has a higher frame rate, so that this disadvantage can be compensated for. In order to fully utilize the complementary information of different sensors, accurate internal and external parameter calibration is indispensable. However, the current calibration technology cannot simultaneously realize internal and external reference calibration of the three-dimensional laser radar, the vision camera, the thermal camera and the inertia measurement unit. The current practice is often realized by calibration of two sensors. Therefore, the calibration process is complicated, and a plurality of different calibration plates are required. Due to the use of multiple calibration plates, calibration errors are also large as errors are transferred. What is worse, the existing calibration method is not user-friendly, and requires the user to have related professional knowledge, which limits the use scene of the sensor suite. Therefore, a convenient, robust and accurate simultaneous internal and external parameter calibration technology is needed at present, the internal and external parameter calibration of the three-dimensional laser radar, the vision camera, the thermal camera and the inertia measurement unit is realized, the problems of large calibration error, complicated calibration process and unfriendliness of users of the conventional multi-source heterogeneous sensor are solved, and the application field of the sensor suite is further expanded.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provides a simultaneous internal and external reference calibration method of a multi-source heterogeneous sensor.
In order to achieve the purpose, the invention adopts the following scheme:
a simultaneous internal and external reference calibration method for a multi-source heterogeneous sensor comprises the following steps:
s10 calibrating board pretreatment;
s11, acquiring image point data of a multi-source heterogeneous sensor consisting of a three-dimensional laser radar, a vision camera, a thermal camera and an inertia measurement unit;
s12 calculating internal parameters of the vision camera and the thermal camera;
s13 calculating external parameters of the radar, the vision camera and the thermal camera;
s14 calculating external parameters of the vision camera, the thermal camera and the inertia measurement unit;
and S15, according to the external parameters obtained in the steps S13 and S14 and the uncertainties of different sensor pairs, giving weights to perform graph optimization once, and further obtaining the final external parameters.
Further, the calibration plate pre-treatment of the S10 step includes heating or cooling the calibration plate, and then aligning the sensor assembly with the calibration plate.
Further, the multi-source heterogeneous sensor image point data acquisition comprising the three-dimensional laser radar, the vision camera, the thermal camera and the inertial measurement unit in the step S11 includes: and moving the three-dimensional laser radar, the vision camera, the thermal sensing camera and the inertia measurement unit to different poses, storing corresponding three-dimensional point cloud, color image and thermal sensing image, and simultaneously recording pose information of the inertia measurement unit in the running process.
Further, the step of S12 calculating the internal parameters of the vision camera and the thermal camera includes: according to the circle centers detected by the vision camera and the thermal camera, initial internal parameters are calculated through a homography matrix, and then fine internal parameters are calculated through a nonlinear optimization method.
Further, the step S13 of calculating external parameters of the radar, the vision camera and the thermal camera includes: the characteristics of points, lines and surfaces are respectively detected in the point cloud, the color image and the thermal image, and then the external parameters are solved through nonlinear optimization, so that the characteristic matching error is minimized.
Further, the step S14 of calculating the external parameters of the vision camera, the thermal camera and the inertial measurement unit includes: and calculating a vision camera, a thermal camera and an inertia measuring unit odometer, and further, using an eye-hand calibration method AX (X) and XB (X).
An intelligent robot comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of simultaneous internal and external parameter calibration of a multi-source heterogeneous sensor according to any one of claims 1 to 7 when executing the computer program.
Compared with the prior art, the invention has the following advantages: 1. the use complexity is low, in order to realize the calibration of the sensor suite, various different calibration plates are often needed to be used in the prior art, and the process is complicated. The simultaneous internal and external reference calibration method of the multisource heterogeneous sensor only needs one calibration plate, and simultaneously realizes the internal and external reference calibration. 2. The method for calibrating the internal and external parameters of the multisource heterogeneous sensor at the same time has high precision, continuously refines the calibration parameters through calibration from coarse to fine, and gives weights according to the uncertainty of the parameters among different sensors so as to obtain the optimal parameters. 3. The method is user-friendly, does not need excessive professional knowledge, and a common user only needs to hold the sensor suite to collect calibration board data, so that the algorithm can automatically select reliable data and complete calibration, and the method is simple to operate and easy to operate.
Drawings
The present application will be described in further detail with reference to the following drawings and detailed description.
FIG. 1 is a schematic step diagram of a simultaneous internal and external reference calibration method of a multi-source heterogeneous sensor according to the present invention.
Detailed Description
The following detailed description of embodiments of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
As shown in fig. 1, a simultaneous internal and external reference calibration method for a multi-source heterogeneous sensor includes the following steps:
s10 calibrating board pretreatment;
s11, acquiring image point data of a multi-source heterogeneous sensor consisting of a three-dimensional laser radar, a vision camera, a thermal camera and an inertia measurement unit;
s12 calculating internal parameters of the vision camera and the thermal camera;
s13 calculating external parameters of the radar, the vision camera and the thermal camera;
s14 calculating external parameters of the vision camera, the thermal camera and the inertia measurement unit;
and S15, according to the external parameters obtained in the steps S13 and S14 and the uncertainties of different sensor pairs, giving weights to perform graph optimization once, and further obtaining the final external parameters. The method for calibrating the internal and external parameters of the multisource heterogeneous sensor at the same time has high precision, continuously refines the calibration parameters through calibration from coarse to fine, and gives weights according to the uncertainty of the parameters among different sensors so as to obtain the optimal parameters.
The use complexity is low, in order to realize the calibration of the sensor suite, various different calibration plates are often needed to be used in the prior art, and the process is complicated. The simultaneous internal and external reference calibration method of the multisource heterogeneous sensor only needs one calibration plate, and simultaneously realizes the internal and external reference calibration.
Preferably, the calibration plate preprocessing of the S10 step includes heating or cooling the calibration plate, and then aligning the sensor assembly with the calibration plate.
Preferably, the image point data acquisition of the multi-source heterogeneous sensor composed of the three-dimensional laser radar, the vision camera, the thermal camera and the inertial measurement unit in the step S11 includes: and moving the three-dimensional laser radar, the vision camera, the thermal sensing camera and the inertia measurement unit to different poses, storing corresponding three-dimensional point cloud, color image and thermal sensing image, and simultaneously recording pose information of the inertia measurement unit in the running process.
Preferably, the step of S12 calculating the internal reference of the vision camera and the thermal camera includes: according to the circle centers detected by the vision camera and the thermal camera, initial internal parameters are calculated through a homography matrix, and then fine internal parameters are calculated through a nonlinear optimization method.
Preferably, the step S13 of calculating the external parameters of the radar, the vision camera and the thermal camera includes: the characteristics of points, lines and surfaces are respectively detected in the point cloud, the color image and the thermal image, and then the external parameters are solved through nonlinear optimization, so that the characteristic matching error is minimized.
Preferably, the step S14 of calculating the external parameters of the vision camera, the thermal camera and the inertial measurement unit includes: and (3) calculating a vision camera, a thermal camera and an inertia measurement unit odometer, and preferably, using an eye-hand calibration method AX (XB).
An intelligent robot comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the method of simultaneous internal and external parameter calibration of a multi-source heterogeneous sensor according to any one of claims 1 to 7 when executing the computer program.
Compared with the prior art, the invention has the following advantages: 1. the use complexity is low, in order to realize the calibration of the sensor suite, various different calibration plates are often needed to be used in the prior art, and the process is complicated. The simultaneous internal and external reference calibration method of the multisource heterogeneous sensor only needs one calibration plate, and simultaneously realizes the internal and external reference calibration. 2. The method for calibrating the internal and external parameters of the multisource heterogeneous sensor at the same time has high precision, continuously refines the calibration parameters through calibration from coarse to fine, and gives weights according to the uncertainty of the parameters among different sensors so as to obtain the optimal parameters. 3. The method is user-friendly, does not need excessive professional knowledge, and a common user only needs to hold the sensor suite to collect calibration board data, so that the algorithm can automatically select reliable data and complete calibration, and the method is simple to operate and easy to operate.
The foregoing is only a preferred embodiment of the present application, and it should be noted that, for those skilled in the art, several modifications and substitutions can be made without departing from the technical principle of the present application, and these modifications and substitutions should also be regarded as the protection scope of the present application.
Claims (8)
1. A simultaneous internal and external reference calibration method of a multi-source heterogeneous sensor is characterized by comprising the following steps:
s10 calibrating board pretreatment;
s11, acquiring image point data of a multi-source heterogeneous sensor consisting of a three-dimensional laser radar, a vision camera, a thermal camera and an inertia measurement unit;
s12 calculating internal parameters of the vision camera and the thermal camera;
s13 calculating external parameters of the radar, the vision camera and the thermal camera;
s14 calculating external parameters of the vision camera, the thermal camera and the inertia measurement unit;
and S15, according to the external parameters obtained in the steps S13 and S14 and the uncertainties of different sensor pairs, giving weights to perform graph optimization once, and further obtaining the final external parameters.
2. The method for simultaneously calibrating internal and external parameters of a multi-source heterogeneous sensor according to claim 1, wherein the calibration plate pretreatment of the S10 step comprises heating or cooling the calibration plate, and then aligning the sensor suite with the calibration plate.
3. The method for simultaneously calibrating internal and external parameters of a multi-source heterogeneous sensor according to claim 1, wherein the step S11 of acquiring the image point data of the multi-source heterogeneous sensor consisting of the three-dimensional laser radar, the vision camera, the thermal camera and the inertial measurement unit comprises: and moving the three-dimensional laser radar, the vision camera, the thermal sensing camera and the inertia measurement unit to different poses, storing corresponding three-dimensional point cloud, color image and thermal sensing image, and simultaneously recording pose information of the inertia measurement unit in the running process.
4. The method for simultaneously calibrating internal and external parameters of a multi-source heterogeneous sensor according to claim 1, wherein the step of calculating internal parameters of a vision camera and a thermal camera in step S12 comprises: according to the circle centers detected by the vision camera and the thermal camera, initial internal parameters are calculated through a homography matrix, and then fine internal parameters are calculated through a nonlinear optimization method.
5. The method for simultaneously calibrating internal and external parameters of a multi-source heterogeneous sensor according to claim 1, wherein the step of S13 for calculating the external parameters of the radar, the vision camera and the thermal camera comprises: the characteristics of points, lines and surfaces are respectively detected in the point cloud, the color image and the thermal image, and then the external parameters are solved through nonlinear optimization, so that the characteristic matching error is minimized.
6. The method for simultaneously calibrating internal and external parameters of a multi-source heterogeneous sensor according to claim 1, wherein the step of calculating the external parameters of the vision camera, the thermal camera and the inertial measurement unit in step S14 comprises: and calculating by a vision camera, a thermal camera and an inertia measurement unit odometer, and solving the external parameters by a calibration method.
7. The method for simultaneously calibrating the internal parameters and the external parameters of the multisource heterogeneous sensor according to claim 6, wherein the calibration method is an eye-hand calibration method AX (XB).
8. An intelligent robot comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method for simultaneous internal and external parameter calibration of a multi-source heterogeneous sensor according to any one of claims 1 to 7 when executing the computer program.
Priority Applications (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110537517.4A CN113327289A (en) | 2021-05-18 | 2021-05-18 | Method for simultaneously calibrating internal and external parameters of multi-source heterogeneous sensor |
Applications Claiming Priority (1)
Application Number | Priority Date | Filing Date | Title |
---|---|---|---|
CN202110537517.4A CN113327289A (en) | 2021-05-18 | 2021-05-18 | Method for simultaneously calibrating internal and external parameters of multi-source heterogeneous sensor |
Publications (1)
Publication Number | Publication Date |
---|---|
CN113327289A true CN113327289A (en) | 2021-08-31 |
Family
ID=77415918
Family Applications (1)
Application Number | Title | Priority Date | Filing Date |
---|---|---|---|
CN202110537517.4A Pending CN113327289A (en) | 2021-05-18 | 2021-05-18 | Method for simultaneously calibrating internal and external parameters of multi-source heterogeneous sensor |
Country Status (1)
Country | Link |
---|---|
CN (1) | CN113327289A (en) |
Cited By (2)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113888652A (en) * | 2021-10-22 | 2022-01-04 | 智能移动机器人(中山)研究院 | Internal and external parameter automatic calibration technology for 4D millimeter wave radar and thermal sensor camera |
CN114413887A (en) * | 2021-12-24 | 2022-04-29 | 北京理工大学前沿技术研究院 | Method, equipment and medium for calibrating external parameters of sensor |
Citations (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104933708A (en) * | 2015-06-07 | 2015-09-23 | 浙江大学 | Barrier detection method in vegetation environment based on multispectral and 3D feature fusion |
CN108051837A (en) * | 2017-11-30 | 2018-05-18 | 武汉大学 | Multiple-sensor integration indoor and outdoor mobile mapping device and automatic three-dimensional modeling method |
CN108364014A (en) * | 2018-01-08 | 2018-08-03 | 东南大学 | A kind of multi-sources Information Fusion Method based on factor graph |
US10096129B2 (en) * | 2017-01-13 | 2018-10-09 | Otsaw Digital Pte. Ltd. | Three-dimensional mapping of an environment |
CN108776474A (en) * | 2018-05-24 | 2018-11-09 | 中山赛伯坦智能科技有限公司 | Robot embedded computing terminal integrating high-precision navigation positioning and deep learning |
CN108846867A (en) * | 2018-08-29 | 2018-11-20 | 安徽云能天智能科技有限责任公司 | A kind of SLAM system based on more mesh panorama inertial navigations |
CN109345596A (en) * | 2018-09-19 | 2019-02-15 | 百度在线网络技术(北京)有限公司 | Multisensor scaling method, device, computer equipment, medium and vehicle |
CN109900265A (en) * | 2019-03-15 | 2019-06-18 | 武汉大学 | A kind of robot localization algorithm of camera/mems auxiliary Beidou |
CN109900280A (en) * | 2019-03-27 | 2019-06-18 | 浙江大学 | A kind of livestock and poultry information Perception robot and map constructing method based on independent navigation |
CN110174136A (en) * | 2019-05-07 | 2019-08-27 | 武汉大学 | A kind of underground piping intelligent measurement robot and intelligent detecting method |
CN110393533A (en) * | 2019-07-25 | 2019-11-01 | 森博迪(深圳)科技有限公司 | A kind of combination inertia and infrared wearing-type motion capture system and method |
US20200158840A1 (en) * | 2018-11-21 | 2020-05-21 | Texas Instruments Incorporated | Multi-mode multi-sensor calibration |
CN111652261A (en) * | 2020-02-26 | 2020-09-11 | 南开大学 | Multi-modal perception fusion system |
CN112254729A (en) * | 2020-10-09 | 2021-01-22 | 北京理工大学 | Mobile robot positioning method based on multi-sensor fusion |
US10911747B1 (en) * | 2019-12-02 | 2021-02-02 | Verizon Patent And Licensing Inc. | Systems and methods for utilizing modeling to automatically determine configuration parameters for cameras |
CN112634375A (en) * | 2020-12-21 | 2021-04-09 | 杭州东信北邮信息技术有限公司 | Plane calibration and three-dimensional reconstruction method in AI intelligent detection |
-
2021
- 2021-05-18 CN CN202110537517.4A patent/CN113327289A/en active Pending
Patent Citations (16)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN104933708A (en) * | 2015-06-07 | 2015-09-23 | 浙江大学 | Barrier detection method in vegetation environment based on multispectral and 3D feature fusion |
US10096129B2 (en) * | 2017-01-13 | 2018-10-09 | Otsaw Digital Pte. Ltd. | Three-dimensional mapping of an environment |
CN108051837A (en) * | 2017-11-30 | 2018-05-18 | 武汉大学 | Multiple-sensor integration indoor and outdoor mobile mapping device and automatic three-dimensional modeling method |
CN108364014A (en) * | 2018-01-08 | 2018-08-03 | 东南大学 | A kind of multi-sources Information Fusion Method based on factor graph |
CN108776474A (en) * | 2018-05-24 | 2018-11-09 | 中山赛伯坦智能科技有限公司 | Robot embedded computing terminal integrating high-precision navigation positioning and deep learning |
CN108846867A (en) * | 2018-08-29 | 2018-11-20 | 安徽云能天智能科技有限责任公司 | A kind of SLAM system based on more mesh panorama inertial navigations |
CN109345596A (en) * | 2018-09-19 | 2019-02-15 | 百度在线网络技术(北京)有限公司 | Multisensor scaling method, device, computer equipment, medium and vehicle |
US20200158840A1 (en) * | 2018-11-21 | 2020-05-21 | Texas Instruments Incorporated | Multi-mode multi-sensor calibration |
CN109900265A (en) * | 2019-03-15 | 2019-06-18 | 武汉大学 | A kind of robot localization algorithm of camera/mems auxiliary Beidou |
CN109900280A (en) * | 2019-03-27 | 2019-06-18 | 浙江大学 | A kind of livestock and poultry information Perception robot and map constructing method based on independent navigation |
CN110174136A (en) * | 2019-05-07 | 2019-08-27 | 武汉大学 | A kind of underground piping intelligent measurement robot and intelligent detecting method |
CN110393533A (en) * | 2019-07-25 | 2019-11-01 | 森博迪(深圳)科技有限公司 | A kind of combination inertia and infrared wearing-type motion capture system and method |
US10911747B1 (en) * | 2019-12-02 | 2021-02-02 | Verizon Patent And Licensing Inc. | Systems and methods for utilizing modeling to automatically determine configuration parameters for cameras |
CN111652261A (en) * | 2020-02-26 | 2020-09-11 | 南开大学 | Multi-modal perception fusion system |
CN112254729A (en) * | 2020-10-09 | 2021-01-22 | 北京理工大学 | Mobile robot positioning method based on multi-sensor fusion |
CN112634375A (en) * | 2020-12-21 | 2021-04-09 | 杭州东信北邮信息技术有限公司 | Plane calibration and three-dimensional reconstruction method in AI intelligent detection |
Non-Patent Citations (8)
Title |
---|
亓琳 等: "高分辨雷达/红外成像复合系统的空间配准方法", 《红外与激光工程》 * |
庞成林: "基于局部地图联合优化的LiDAR-IMU紧耦合SLAM算法研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
彭洋 等: "基于视觉和激光融合的林区采育目标识别方法", 《林业机械与木工设备》 * |
李迪龙: "移动测量系统的多传感器标定技术研究", 《中国优秀博硕士学位论文全文数据库(硕士)信息科技辑》 * |
杨照金 等: "《工程光学计量测试技术概论》", 29 February 2016, 国防工业出版社 * |
熊有伦 等: "《机器人学建模、控制与视觉》", 31 March 2018, 华中科技大学出版社 * |
王鑫 等: "热像仪-RGB相机-IMU传感器的空间联合标定方法", 《仪器仪表学报》 * |
范大昭 等: "《多基线立体匹配理论、方法与应用》", 31 January 2017, 测绘出版社 * |
Cited By (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN113888652A (en) * | 2021-10-22 | 2022-01-04 | 智能移动机器人(中山)研究院 | Internal and external parameter automatic calibration technology for 4D millimeter wave radar and thermal sensor camera |
CN114413887A (en) * | 2021-12-24 | 2022-04-29 | 北京理工大学前沿技术研究院 | Method, equipment and medium for calibrating external parameters of sensor |
CN114413887B (en) * | 2021-12-24 | 2024-04-02 | 北京理工大学前沿技术研究院 | Sensor external parameter calibration method, device and medium |
Similar Documents
Publication | Publication Date | Title |
---|---|---|
US11704833B2 (en) | Monocular vision tracking method, apparatus and non-transitory computer-readable storage medium | |
CN111354042B (en) | Feature extraction method and device of robot visual image, robot and medium | |
US10764487B2 (en) | Distance image acquisition apparatus and application thereof | |
CN108362266B (en) | Auxiliary monocular vision measurement method and system based on EKF laser ranging | |
KR102134688B1 (en) | Optical distance measuring method and optical distance measuring device | |
JP2021120844A (en) | Method, device, electronic device and recording medium utilized for determining position of vehicle | |
CN108717715A (en) | A kind of line-structured light vision system automatic calibration method for arc welding robot | |
CN109737913B (en) | Laser tracking attitude angle measurement system and method | |
CN106548489A (en) | The method for registering of a kind of depth image and coloured image, three-dimensional image acquisition apparatus | |
CN111191625A (en) | Object identification and positioning method based on laser-monocular vision fusion | |
CN102980556A (en) | Distance measuring method and device | |
CN113327289A (en) | Method for simultaneously calibrating internal and external parameters of multi-source heterogeneous sensor | |
Mi et al. | A vision-based displacement measurement system for foundation pit | |
CN112509065B (en) | Visual guidance method applied to deep sea mechanical arm operation | |
CN114714356A (en) | Method for accurately detecting calibration error of hand eye of industrial robot based on binocular vision | |
CN104463833A (en) | Method and system for calibrating camera parameters of one-dimensional area array camera set | |
CN108007426A (en) | A kind of camera distance measuring method and system | |
US12026299B2 (en) | Adaptive intelligent head-hand VR system and method | |
CN103475820A (en) | Correcting method and system for PI position in camera | |
CN111971956B (en) | Method and system for dynamic stereo calibration | |
CN110044266B (en) | Photogrammetry system based on speckle projection | |
CN113822920B (en) | Method for acquiring depth information by structured light camera, electronic equipment and storage medium | |
WO2017117750A1 (en) | Simple follow focus system based on multiple ranging approaches, and photographing system | |
CN115100026B (en) | Label coordinate conversion method, device, equipment and storage medium based on target object | |
CN112685860B (en) | Curved surface attitude detection method and device, terminal equipment and storage medium |
Legal Events
Date | Code | Title | Description |
---|---|---|---|
PB01 | Publication | ||
PB01 | Publication | ||
SE01 | Entry into force of request for substantive examination | ||
SE01 | Entry into force of request for substantive examination | ||
RJ01 | Rejection of invention patent application after publication | ||
RJ01 | Rejection of invention patent application after publication |
Application publication date: 20210831 |