CN109579898A - A kind of intelligence manufacture sensing data spatial calibration method and device - Google Patents
A kind of intelligence manufacture sensing data spatial calibration method and device Download PDFInfo
- Publication number
- CN109579898A CN109579898A CN201811586953.5A CN201811586953A CN109579898A CN 109579898 A CN109579898 A CN 109579898A CN 201811586953 A CN201811586953 A CN 201811586953A CN 109579898 A CN109579898 A CN 109579898A
- Authority
- CN
- China
- Prior art keywords
- data
- sensor
- spatial
- acquisition
- 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
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
- G01D18/00—Testing or calibrating apparatus or arrangements provided for in groups G01D1/00 - G01D15/00
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The disclosure provides a kind of intelligence manufacture sensing data spatial calibration method and device, this disclosure relates to sensor network technique field, during the space data collection of sensor, by the way that the spatial data of collection point is carried out coordinate conversion, remove the data noise of spatial data, data after removal Acquisition Error are subjected to vector deviation calibration, spatial data is subjected to spatial calibration, the spatial data of collection point is subjected to coordinate conversion, eliminate the influence of space bias pair with multi-sensor data precision, it is synchronized acquisition spatial data and eliminates space error deviation, the data of acquisition are made to have carried out the unification in reference axis in the dimension in space, and it is more accurate, so that the spatial data of acquisition is more accurate and perfect, data precision is higher.
Description
Technical field
This disclosure relates to sensor network technique field, in particular to a kind of intelligence manufacture sensing data spatial calibration side
Method and device.
Background technique
In the environment that a variety of different sensors carry out data collection task collaborative works, often multiple and different types
The collected different types of data of sensor, in Distributed Multi-sensor System, various industrial sensors such as grating
Rule displacement sensor, gyro sensor, angular transducer, angular-rate sensor, GPS sensor, position sensor, ultrasonic wave
Range sensor, rotary torque sensor, inductosyn, acceleration transducer etc. are surveyed, there is the data collection system of oneself,
It is difficult to handle and be merged, the acquisition and data processing of target are completed in the same coordinate system.In these sensors
In network, it and is under rectangular coordinate system to the processing of data that some sensors of the acquisition of data, which are completed under polar coordinate system,
It completes.Therefore just need to carry out space conversion, by under Data Format Transform to the same coordinate system and carry out spatial calibration into
Row processing.
Summary of the invention
In view of the above technical problems, the disclosure provides a kind of intelligence manufacture sensing data spatial calibration method and device,
During the space data collection of sensor, two sensors a and b therein, oblique distance and azimuth angle deviation are respectively as follows: Δ
ra,Δθa,Δrb,Δθb, ra,θaAnd rb,θbThe oblique distance and azimuth collection value for respectively representing sensor a and b, by collection point
Spatial data carries out coordinate conversion.
A kind of intelligence manufacture sensing data spatial calibration method specifically includes the following steps:
Step 1, sensor carries out space data collection；
Step 2, the spatial data of collection point is subjected to coordinate conversion；
Step 3, the data noise of spatial data is removed；
Step 4, the data after removal Acquisition Error are subjected to vector deviation calibration；
Step 5, spatial data is subjected to spatial calibration.
Further, in step 1, the sensor includes grating rule displacement sensor, gyro sensor, angle biography
Sensor, angular-rate sensor, GPS sensor, position sensor, ultrasound are apart from sensor, rotary torque sensor, induction
The spatial data of synchronizer, acceleration transducer, sensor acquisition includes position coordinates, straight-line displacement and the angle of sensor acquisition
Displacement, speed.
Further, in step 2, it is by the method that the spatial data of collection point carries out coordinate conversion,
Collection point, that is, sensor spatial position, the spatial data that sensor is acquired, i.e. position coordinates, straight-line displacement are sat
Mark and angular displacement coordinate, establishing new coordinate origin relative to old coordinate system coordinate is (a, b, c), has a point p in new, old coordinate
Coordinate is respectively (x, y, z) and (x', y', z') in system, it can be deduced that:
Rotation transformation does not change origin position and only changes the direction of reference axis, and X-axis and Y-axis rotate the angle θ about the z axis and obtain
OX' and OY', i.e. OXYZ become OX'Y'Z' through rotation counterclockwise, and the position of collection point p coordinate in old new coordinate system is respectively
(x, y, z) and (x', y', z'), it can be deduced that:
If being rotated alone about X-axis and Y-axis, respectively obtain with down space conversion formula:
In the processing of sensing data, in order to which the data and processing data that allow sensor to acquire easily are converted,
Commonly use the mutual conversion of rectangular coordinate system and space polar coordinate system.
According to the transformational relation between following rectangular coordinate system and polar coordinate system, if any one point P in rectangular coordinate system
Position coordinates be (x, y, z), corresponding position coordinate is (r, φ, θ), then rectangular coordinate system and polar coordinate system in polar coordinate system
Between Conversion Relations are as follows:
(xa,ya) and (xb,yb) indicate the collection value fastened in world coordinates, (xsa,ysa)
(xsb,ysb) indicate position of the sensor in global coordinate system.
Further, in step 2, the method for removing the data noise of spatial data is,
It is assumed that sensor acquisition noise vector are as follows:
WithRespectively indicate sensor a and b acquisition
The oblique distance of spatial data and azimuthal acquisition noise, v Gaussian distributed, it may be assumed that
The fundamental equation of the spatial data of sensor acquisition are as follows:
Consider acquisition spatial data acquisition noise, then between data oblique distance and azimuthal acquisition noise be with lower section
Journey group:
Wherein ra',θa' and rb',θb' indicate sensor acquisition spatial data value, Δ ra,ΔθaWith Δ rb,ΔθbTable
Show the Acquisition Error of sensor.
Further, in step 4, the method for the data after removal Acquisition Error being carried out vector deviation calibration is root
According to first order Taylor series expansion, Δ r is obtaineda,Δθa,Δrb,ΔθbTaylor series expansion is,
According to the different moments of sensor data acquisition, that is to say, that when the time of acquisition be k=1, when 2 ... N, obtain
Vector deviation calibration formula below are as follows:
After N number of sensor acquires data, then there is 2N equation, as N >=2,4 space coordinate solutions can be solved.
It further, in steps of 5, is to linearize N number of sensor to acquire by the method that spatial data carries out spatial calibration
Noise vector v and bias vector x, obtain n times acquisition after linear equation be calibration formula are as follows:
Z=A (x+v)=Ax+Av,
Wherein:
Further, calibration formula is reduced to
The then estimation of the sensor bias vector x based on maximum likelihood method are as follows:
The present invention also provides a kind of intelligence manufacture sensing data spatial calibration device, described device include: memory,
Processor and storage in the memory and the computer program that can run on the processor, the processor execution
The computer program operates in the unit of following device:
Space data collection unit carries out space data collection for sensor；
Space coordinate conversion unit, for the spatial data of collection point to be carried out coordinate conversion；
Data noise removal unit, for removing the data noise of spatial data；
Vector deviation calibration unit carries out vector deviation calibration for that will remove the data after Acquisition Error；
Spatial data calibration unit, for spatial data to be carried out spatial calibration.
The disclosure has the beneficial effect that the disclosure provides a kind of intelligence manufacture sensing data spatial calibration method and dress
It sets, eliminates the influence of space bias pair with multi-sensor data precision, be synchronized acquisition spatial data and eliminate space
Error deviation makes the data of acquisition carry out the unification in reference axis in the dimension in space, and more accurate, so that acquisition
Spatial data it is more accurate and perfect, data precision is higher.
Detailed description of the invention
By the way that the embodiment in conjunction with shown by attached drawing is described in detail, above-mentioned and other features of the disclosure will
More obvious, identical reference label indicates the same or similar element in disclosure attached drawing, it should be apparent that, it is described below
Attached drawing be only some embodiments of the present disclosure, for those of ordinary skill in the art, do not making the creative labor
Under the premise of, it is also possible to obtain other drawings based on these drawings, in the accompanying drawings:
Fig. 1 show a kind of intelligence manufacture sensing data spatial calibration method work flow diagram of the disclosure；
Fig. 2 show a kind of intelligence manufacture sensing data spatial calibration apparatus module architecture diagram of the disclosure.
Specific embodiment
It is carried out below with reference to technical effect of the embodiment and attached drawing to the design of the disclosure, specific structure and generation clear
Chu, complete description, to be completely understood by the purpose, scheme and effect of the disclosure.It should be noted that the case where not conflicting
Under, the features in the embodiments and the embodiments of the present application can be combined with each other.
As shown in Figure 1 for according to a kind of intelligence manufacture sensing data spatial calibration method and device workflow of the disclosure
Cheng Tu illustrates a kind of intelligence manufacture sensing data spatial calibration method according to the disclosure below with reference to Fig. 1.
The disclosure proposes a kind of intelligence manufacture sensing data spatial calibration method, specifically includes the following steps:
During the space data collection of sensor, two sensors a and b therein, oblique distance and azimuth angle deviation are divided
Not are as follows: Δ ra,Δθa,Δrb,Δθb, ra,θaAnd rb,θbThe oblique distance and azimuth collection value for respectively representing sensor a and b, will adopt
The spatial data of collection point carries out coordinate conversion.
Step 1, sensor carries out space data collection；
Step 2, the spatial data of collection point is subjected to coordinate conversion；
Step 3, the data noise of spatial data is removed；
Step 4, the data after removal Acquisition Error are subjected to vector deviation calibration；
Step 5, spatial data is subjected to spatial calibration.
Further, in step 1, the sensor includes grating rule displacement sensor, gyro sensor, angle biography
Sensor, angular-rate sensor, GPS sensor, position sensor, ultrasound are apart from sensor, rotary torque sensor, induction
The spatial data of synchronizer, acceleration transducer, sensor acquisition includes position coordinates, straight-line displacement and the angle of sensor acquisition
Displacement, speed.
Further, in step 2, it is by the method that the spatial data of collection point carries out coordinate conversion,
Collection point, that is, sensor spatial position, the spatial data that sensor is acquired, i.e. position coordinates, straight-line displacement are sat
Mark and angular displacement coordinate, establishing new coordinate origin relative to old coordinate system coordinate is (a, b, c), has a point p in new, old coordinate
Coordinate is respectively (x, y, z) and (x', y', z') in system, it can be deduced that:
Rotation transformation does not change origin position and only changes the direction of reference axis, and X-axis and Y-axis rotate the angle θ about the z axis and obtain
OX' and OY', i.e. OXYZ become OX'Y'Z' through rotation counterclockwise, and the position of collection point p coordinate in old new coordinate system is respectively
(x, y, z) and (x', y', z'), it can be deduced that:
If being rotated alone about X-axis and Y-axis, respectively obtain with down space conversion formula:
In the processing of sensing data, in order to which the data and processing data that allow sensor to acquire easily are converted,
Commonly use the mutual conversion of rectangular coordinate system and space polar coordinate system.
According to the transformational relation between following rectangular coordinate system and polar coordinate system, if any one point P in rectangular coordinate system
Position coordinates be (x, y, z), corresponding position coordinate is (r, φ, θ), then rectangular coordinate system and polar coordinate system in polar coordinate system
Between Conversion Relations are as follows:
(xa,ya) and (xb,yb) indicate the collection value fastened in world coordinates, (xsa,ysa)
(xsb,ysb) indicate position of the sensor in global coordinate system.
Further, in step 2, the method for removing the data noise of spatial data is,
It is assumed that sensor acquisition noise vector are as follows:
WithRespectively indicate sensor a and b acquisition
The oblique distance of spatial data and azimuthal acquisition noise, v Gaussian distributed, it may be assumed that
The fundamental equation of the spatial data of sensor acquisition are as follows:
Consider acquisition spatial data acquisition noise, then between data oblique distance and azimuthal acquisition noise be with lower section
Journey group:
Wherein ra',θa' and rb',θb' indicate sensor acquisition spatial data value, Δ ra,ΔθaWith Δ rb,ΔθbTable
Show the Acquisition Error of sensor.
Further, in step 4, the method for the data after removal Acquisition Error being carried out vector deviation calibration is root
According to first order Taylor series expansion, Δ r is obtaineda,Δθa,Δrb,ΔθbTaylor series expansion is,
According to the different moments of sensor data acquisition, that is to say, that when the time of acquisition be k=1, when 2 ... N, obtain
Vector deviation calibration formula below are as follows:
After N number of sensor acquires data, then there is 2N equation, as N >=2,4 space coordinate solutions can be solved.
It further, in steps of 5, is to linearize N number of sensor to acquire by the method that spatial data carries out spatial calibration
Noise vector v and bias vector x, obtain n times acquisition after linear equation be calibration formula are as follows:
Z=A (x+v)=Ax+Av,
Wherein:
Further, calibration formula is reduced toLinear equation after being acquired according to n times is calibration
The spatial data after calibration can be obtained in formula.
The estimation of its sensor bias vector x based on maximum likelihood method are as follows:
A kind of intelligence manufacture sensing data spatial calibration device that embodiment of the disclosure provides is illustrated in figure 2 this
A kind of intelligence manufacture sensing data of a kind of disclosed intelligence manufacture sensing data spatial calibration device figure, the embodiment is empty
Between calibrating installation include: processor, memory and storage in the memory and the meter that can run on the processor
Calculation machine program, the processor realize a kind of above-mentioned intelligence manufacture sensing data spatial calibration when executing the computer program
Step in Installation practice.
Described device includes: memory, processor and storage in the memory and can transport on the processor
Capable computer program, the processor execute the computer program and operate in the unit of following device:
Space data collection unit carries out space data collection for sensor；
Space coordinate conversion unit, for the spatial data of collection point to be carried out coordinate conversion；
Data noise removal unit, for removing the data noise of spatial data；
Vector deviation calibration unit carries out vector deviation calibration for that will remove the data after Acquisition Error；
Spatial data calibration unit, for spatial data to be carried out spatial calibration.
A kind of intelligence manufacture sensing data spatial calibration device can run on desktop PC, notebook,
Palm PC and cloud server etc. calculate in equipment.A kind of intelligence manufacture sensing data spatial calibration device can be run
Device may include but being not limited only to, processor, memory.It will be understood by those skilled in the art that the example is only one
The example of kind intelligence manufacture sensing data spatial calibration device, is not constituted to a kind of intelligence manufacture sensing data space school
The restriction of standard apparatus may include component more more or fewer than example, perhaps combine certain components or different components,
Such as a kind of intelligence manufacture sensing data spatial calibration device can also be set including input-output equipment, network insertion
Standby, bus etc..Alleged processor can be central processing unit (Central Processing Unit, CPU), can also be
Other general processors, digital signal processor (Digital Signal Processor, DSP), specific integrated circuit
(Application Specific Integrated Circuit, ASIC), ready-made programmable gate array (Field-
Programmable Gate Array, FPGA) either other programmable logic device, discrete gate or transistor logic,
Discrete hardware components etc..General processor can be microprocessor or the processor is also possible to any conventional processor
Deng, the processor is a kind of control centre of intelligence manufacture sensing data spatial calibration device running gear, utilize
Various interfaces and connection entirely a kind of intelligence manufacture sensing data spatial calibration device can running gear various pieces.
The memory can be used for storing the computer program and/or module, and the processor is by operation or executes
Computer program in the memory and/or module are stored, and calls the data being stored in memory, described in realization
A kind of various functions of intelligence manufacture sensing data spatial calibration device.The memory can mainly include storing program area and
Storage data area, wherein storing program area can (such as the sound of application program needed for storage program area, at least one function
Playing function, image player function etc.) etc.；Storage data area, which can be stored, uses created data (such as sound according to mobile phone
Frequency evidence, phone directory etc.) etc..In addition, memory may include high-speed random access memory, it can also include non-volatile deposit
Reservoir, such as hard disk, memory, plug-in type hard disk, intelligent memory card (Smart Media Card, SMC), secure digital
(Secure Digital, SD) card, flash card (Flash Card), at least one disk memory, flush memory device or other
Volatile solid-state part.
Although the description of the disclosure is quite detailed and especially several embodiments are described, it is not
Any of these details or embodiment or any specific embodiments are intended to be limited to, but should be considered as is by reference to appended
A possibility that claim provides broad sense in view of the prior art for these claims explanation, to effectively cover the disclosure
Preset range.In addition, the disclosure is described with inventor's foreseeable embodiment above, its purpose is to be provided with
Description, and those equivalent modifications that the disclosure can be still represented to the unsubstantiality change of the disclosure still unforeseen at present.
Claims (7)
1. a kind of intelligence manufacture sensing data spatial calibration method, which is characterized in that the described method includes:
Step 1, sensor carries out space data collection；
Step 2, the spatial data of collection point is subjected to coordinate conversion；
Step 3, the data noise of spatial data is removed；
Step 4, the data after removal Acquisition Error are subjected to vector deviation calibration；
Step 5, spatial data is subjected to spatial calibration.
2. a kind of intelligence manufacture sensing data spatial calibration method according to claim 1, which is characterized in that in step
In 1, the sensor includes grating rule displacement sensor, gyro sensor, angular transducer, angular-rate sensor, GPS biography
Sensor, position sensor, ultrasound are passed apart from sensor, rotary torque sensor, inductosyn, acceleration transducer
The spatial data of sensor acquisition includes position coordinates, straight-line displacement and the angular displacement of sensor acquisition, speed.
3. a kind of intelligence manufacture sensing data spatial calibration method according to claim 1, which is characterized in that in step
In 2, it is by the method that the spatial data of collection point carries out coordinate conversion,
Collection point, that is, sensor spatial position, by sensor acquire spatial data, i.e., position coordinates, straight-line displacement coordinate and
Angular displacement coordinate, establishing new coordinate origin relative to old coordinate system coordinate is (a, b, c), has a point p in new, old coordinate system
Coordinate is respectively (x, y, z) and (x', y', z'), it can be deduced that:
Rotation transformation does not change origin position and only changes the direction of reference axis, X-axis and Y-axis rotate about the z axis the angle θ obtain OX' and
OY', i.e. OXYZ become OX'Y'Z' through rotation counterclockwise, the position of collection point p coordinate in old new coordinate system be respectively (x, y,
And (x', y', z') z), it can be deduced that:
If being rotated alone about X-axis and Y-axis, respectively obtain with down space conversion formula:
According to the transformational relation between following rectangular coordinate system and polar coordinate system, if in rectangular coordinate system any one point P position
Setting coordinate is (x, y, z), and corresponding position coordinate is (r, φ, θ) in polar coordinate system, then between rectangular coordinate system and polar coordinate system
Conversion Relations are as follows:
(xa,ya) and (xb,yb) indicate the collection value fastened in world coordinates, (xsa,ysa) and
(xsb,ysb) indicate position of the sensor in global coordinate system.
4. a kind of intelligence manufacture sensing data spatial calibration method according to claim 1, which is characterized in that in step
In 2, the method for removing the data noise of spatial data is,
It is assumed that sensor acquisition noise vector are as follows:
WithRespectively indicate the space of sensor a and b acquisition
The oblique distance of data and azimuthal acquisition noise, v Gaussian distributed, it may be assumed that
The fundamental equation of the spatial data of sensor acquisition are as follows:
Consider acquisition spatial data acquisition noise, then between data oblique distance and azimuthal acquisition noise be following equation
Group:
Wherein ra',θa' and rb',θb' indicate sensor acquisition spatial data value, Δ ra,ΔθaWith Δ rb,ΔθbIt indicates to pass
The Acquisition Error of sensor.
5. a kind of intelligence manufacture sensing data spatial calibration method according to claim 1, which is characterized in that in step
In 4, the method that the data after removal Acquisition Error are carried out vector deviation calibration is, according to first order Taylor series expansion, to obtain
Δra,Δθa,Δrb,ΔθbTaylor series expansion is,
According to the different moments of sensor data acquisition, that is to say, that when the time of acquisition be k=1, when 2 ... N, obtain following
Vector deviation calibration formula are as follows:
6. a kind of intelligence manufacture sensing data spatial calibration method according to claim 1, which is characterized in that in step
It is the noise vector v and bias vector x for linearizing N number of sensor acquisition by the method that spatial data carries out spatial calibration in 5,
Linear equation after obtaining n times acquisition is calibration formula are as follows:
Z=A (x+v)=Ax+A v,
Wherein:
Calibration formula is reduced to
The then estimation of the sensor bias vector x based on maximum likelihood method are as follows:
7. a kind of intelligence manufacture sensing data spatial calibration device, which is characterized in that described device includes: memory, processing
Device and storage in the memory and the computer program that can run on the processor, described in the processor execution
Computer program operates in the unit of following device:
Space data collection unit carries out space data collection for sensor；
Space coordinate conversion unit, for the spatial data of collection point to be carried out coordinate conversion；
Data noise removal unit, for removing the data noise of spatial data；
Vector deviation calibration unit carries out vector deviation calibration for that will remove the data after Acquisition Error；
Spatial data calibration unit, for spatial data to be carried out spatial calibration.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811586953.5A CN109579898A (en) | 2018-12-25 | 2018-12-25 | A kind of intelligence manufacture sensing data spatial calibration method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811586953.5A CN109579898A (en) | 2018-12-25 | 2018-12-25 | A kind of intelligence manufacture sensing data spatial calibration method and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109579898A true CN109579898A (en) | 2019-04-05 |
Family
ID=65931614
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811586953.5A Pending CN109579898A (en) | 2018-12-25 | 2018-12-25 | A kind of intelligence manufacture sensing data spatial calibration method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109579898A (en) |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110108984A (en) * | 2019-05-24 | 2019-08-09 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | The spatial relationship synchronous method of power-line patrolling laser radar system multisensor |
| CN110946699A (en) * | 2019-12-31 | 2020-04-03 | 重庆渝辽机械设备有限责任公司 | Urine and excrement recognition alarm system, urine and excrement recognition reminder and nursing product |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101126806A (en) * | 2007-09-20 | 2008-02-20 | 上海交通大学 | Method for revising maximum likelihood registration based information infusion |
| CN106370186A (en) * | 2016-09-18 | 2017-02-01 | 时瑞科技（深圳）有限公司 | Rapid low-power-consumption fusion system and method for sensor |
| CN107144400A (en) * | 2017-05-05 | 2017-09-08 | 深圳市美好创亿医疗科技有限公司 | Differential pressure pickup and its calibration method, calibration check method |
| US20180114680A1 (en) * | 2016-10-26 | 2018-04-26 | Asm Ip Holding B.V. | Methods for thermally calibrating reaction chambers |
| CN108346165A (en) * | 2018-01-30 | 2018-07-31 | 深圳市易尚展示股份有限公司 | Robot and three-dimensional sensing components in combination scaling method and device |
-
2018
- 2018-12-25 CN CN201811586953.5A patent/CN109579898A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101126806A (en) * | 2007-09-20 | 2008-02-20 | 上海交通大学 | Method for revising maximum likelihood registration based information infusion |
| CN106370186A (en) * | 2016-09-18 | 2017-02-01 | 时瑞科技（深圳）有限公司 | Rapid low-power-consumption fusion system and method for sensor |
| US20180114680A1 (en) * | 2016-10-26 | 2018-04-26 | Asm Ip Holding B.V. | Methods for thermally calibrating reaction chambers |
| CN107144400A (en) * | 2017-05-05 | 2017-09-08 | 深圳市美好创亿医疗科技有限公司 | Differential pressure pickup and its calibration method, calibration check method |
| CN108346165A (en) * | 2018-01-30 | 2018-07-31 | 深圳市易尚展示股份有限公司 | Robot and three-dimensional sensing components in combination scaling method and device |
Non-Patent Citations (2)
| Title |
|---|
| 张小红 等: "《导航学》", 31 May 2017, 武汉大学出版社 * |
| 王琪龙: "基于异类传感器信息融合的目标跟踪理论与方法研究", 《中国博士学位论文全文数据库 信息科技辑》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110108984A (en) * | 2019-05-24 | 2019-08-09 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | The spatial relationship synchronous method of power-line patrolling laser radar system multisensor |
| CN110108984B (en) * | 2019-05-24 | 2021-07-16 | 中国南方电网有限责任公司超高压输电公司检修试验中心 | Spatial relationship synchronization method for multiple sensors of power line patrol laser radar system |
| CN110946699A (en) * | 2019-12-31 | 2020-04-03 | 重庆渝辽机械设备有限责任公司 | Urine and excrement recognition alarm system, urine and excrement recognition reminder and nursing product |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109959381B (en) | Positioning method, positioning device, robot and computer readable storage medium | |
| CN108957466B (en) | Radar data compensation method, device, equipment and storage medium for mobile robot | |
| US9243916B2 (en) | Observability-constrained vision-aided inertial navigation | |
| WO2018098811A1 (en) | Localization method and device | |
| CN110579738B (en) | Moving target direction angle obtaining method and terminal equipment | |
| JP2020172401A (en) | High speed establishing method of warehouse storage map, machine, memory medium and robot | |
| CN112379352B (en) | Laser radar calibration method, device, equipment and storage medium | |
| US10670689B2 (en) | System and method for determining geo location of a target using a cone coordinate system | |
| CN110986988B (en) | Track calculation method, medium, terminal and device integrating multi-sensor data | |
| CN103033185B (en) | For generating the improved Kalman filter of attitude error correction | |
| CN110471409B (en) | Robot inspection method and device, computer readable storage medium and robot | |
| CN106197410A (en) | For the method and apparatus accurately capturing inertial sensor data | |
| CN112744313B (en) | Robot state estimation method and device, readable storage medium and robot | |
| CN109579898A (en) | A kind of intelligence manufacture sensing data spatial calibration method and device | |
| Gu et al. | Optimal cooperative sensing using a team of UAVs | |
| CN102519463A (en) | Navigation method and device based on extended Kalman filter | |
| CN108122280A (en) | The method for reconstructing and device of a kind of three-dimensional point cloud | |
| CN114897669A (en) | Labeling method and device and electronic equipment | |
| CN114926547A (en) | Calibration method of camera and IMU, electronic device and system | |
| WO2022205750A1 (en) | Point cloud data generation method and apparatus, electronic device, and storage medium | |
| CN114063622A (en) | Unmanned ship autonomous berthing positioning method and device and related components | |
| CN101196532B (en) | Velocity detection apparatus | |
| CN109459769A (en) | A kind of autonomic positioning method and system | |
| CN111862141B (en) | AGV device motion precision evaluation method and device, medium and electronic equipment | |
| CN108444466A (en) | A kind of positioning navigation method, device and terminal device |
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 |
Application publication date: 20190405 |
|
| RJ01 | Rejection of invention patent application after publication |