WO2022264342A1 - Dispositif d'estimation de profondeur, procédé d'estimation de profondeur et programme d'estimation de profondeur - Google Patents

Dispositif d'estimation de profondeur, procédé d'estimation de profondeur et programme d'estimation de profondeur Download PDF

Info

Publication number
WO2022264342A1
WO2022264342A1 PCT/JP2021/022937 JP2021022937W WO2022264342A1 WO 2022264342 A1 WO2022264342 A1 WO 2022264342A1 JP 2021022937 W JP2021022937 W JP 2021022937W WO 2022264342 A1 WO2022264342 A1 WO 2022264342A1
Authority
WO
WIPO (PCT)
Prior art keywords
coordinates
local
relative permittivity
point
buried object
Prior art date
Application number
PCT/JP2021/022937
Other languages
English (en)
Japanese (ja)
Inventor
和彦 村崎
慎吾 安藤
潤 島村
Original Assignee
日本電信電話株式会社
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by 日本電信電話株式会社 filed Critical 日本電信電話株式会社
Priority to JP2023528867A priority Critical patent/JPWO2022264342A1/ja
Priority to PCT/JP2021/022937 priority patent/WO2022264342A1/fr
Publication of WO2022264342A1 publication Critical patent/WO2022264342A1/fr

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications

Definitions

  • the disclosed technique relates to a depth estimation device, a depth estimation method, and a depth estimation program.
  • Non-Patent Literature 1 discloses that machine learning can detect a location with a buried object reaction from ground penetrating radar data.
  • the propagation speed of the electromagnetic wave or the relative dielectric constant in the ground is used to know how deep the reaction site actually occurs in the ground. calculation is required. Since it is generally difficult to grasp the relative permittivity of the object to be measured in detail, either a rough estimate is used or an engineer sets the relative permittivity visually.
  • Non-Patent Document 2 discloses that the relative permittivity can be automatically estimated by evaluating the result of migration processing on GPR data using a numerical index. It has been shown that the relative permittivity can be estimated with particularly high accuracy in a low-noise environment.
  • Non-Patent Document 2 assumes that the entire measurement target has a uniform dielectric constant, and that the reaction of buried objects is small. In a general road environment, the properties of the soil are not uniform and many buried objects are mixed. It's becoming
  • An object of the present invention is to provide a depth estimation device, a depth estimation method, and a depth estimation program.
  • a first aspect of the present disclosure is a depth estimation device based on ground penetrating radar data, which is two-dimensional data obtained by measuring an embedded object buried in the ground, and a previously obtained range of relative permittivity.
  • a migration unit that performs a predetermined migration process while changing the relative dielectric constant according to the range, and obtains a migration result for each changed relative dielectric constant;
  • Each local point that is either a local maximum point or a local minimum point is extracted, and with respect to the local point, the relative permittivity in the migration result, the traveling direction of the antenna that measured the ground penetrating radar data an extremity information extraction unit for extracting three-dimensional data of coordinates and reflection time coordinates;
  • a buried object detection unit for detecting the coordinates of the buried object using a pre-learned detector; Refer to the coordinates of the local points of the three-dimensional data in the peripheral coordinates based on the relative permittivity corresponding to the local points, adopt the relative permittivity, the reflection time and and and a depth
  • a second aspect of the present disclosure is a depth estimation method based on ground penetrating radar data, which is two-dimensional data obtained by measuring an object buried underground, and a previously obtained range of relative permittivity. Then, a predetermined migration process is performed while changing the relative dielectric constant according to the range, the migration result for each changed relative dielectric constant is obtained, and from the migration result, the maximum point of the migrated value or the maximum point and Each of the local points that are any of the local minimum points is extracted, and with respect to the local points, the relative permittivity in the migration result, the coordinates of the traveling direction of the antenna that measured the ground penetrating radar data, and 3D data of the coordinates of the reflection time are extracted, the coordinates of the buried object are detected using a pre-trained detector, and the local coordinates of the 3D data in the peripheral coordinates based on the coordinates of the buried object are detected. referring to the coordinates of the point, adopting the dielectric constant corresponding to said local point, and estimating the depth of the
  • a third aspect of the present disclosure is a depth estimation program based on ground penetrating radar data, which is two-dimensional data obtained by measuring an embedded object buried in the ground, and a previously obtained range of relative permittivity. Then, a predetermined migration process is performed while changing the relative dielectric constant according to the range, the migration result for each changed relative dielectric constant is obtained, and from the migration result, the maximum point of the migrated value or the maximum point and Each of the local points that are any of the local minimum points is extracted, and with respect to the local points, the relative permittivity in the migration result, the coordinates of the traveling direction of the antenna that measured the ground penetrating radar data, and 3D data of the coordinates of the reflection time are extracted, the coordinates of the buried object are detected using a pre-trained detector, and the local coordinates of the 3D data in the peripheral coordinates based on the coordinates of the buried object are detected. referring to the coordinates of the point, adopting the dielectric constant corresponding to said local point, and estimating the
  • FIG. 2 is a block diagram showing the hardware configuration of a depth estimation device according to an embodiment of the present disclosure
  • FIG. 1 is a block diagram showing a functional configuration of a depth estimation device according to an embodiment of the present disclosure
  • FIG. It is an example of a processing result obtained by migration. It is an example of the migration result according to the dielectric constant. It is an example of three-dimensional data in the dielectric constant direction, the antenna traveling direction, and the reflection time direction.
  • FIG. 4 is a diagram showing a configuration related to an embedded object detection unit;
  • the depth to which each buried object is buried can be obtained in a realistic scene where the soil to be measured is not uniform and multiple buried objects are buried. is the subject.
  • an appropriate dielectric constant is estimated for each buried object. Assuming that the average relative permittivity of each buried object is constant and estimating it, we can obtain It realizes buried depth estimation in complex environment. Specifically, migration processing is applied to the entire data while changing the dielectric constant within an appropriate range, and the coordinate value and dielectric constant that maximize the migrated value are extracted. The relative permittivity obtained at the point of this maximum (maximum point) can be regarded as an appropriate average relative permittivity at that coordinate value. Next, the approximate position of the buried object is detected from the GPR data using a previously machine-learned buried object detector.
  • GPR data is two-dimensional data obtained by measuring a buried object buried in the ground.
  • GPR data is an example of ground penetrating radar data of this disclosure.
  • the method of the embodiment of the present disclosure it is possible to automatically estimate the depth of a buried object without requiring the know-how of an engineer for GPR data measured on a general road where the soil environment is not maintained. .
  • the buried object can be maintained and managed based on the three-dimensional position, contributing to a reduction in maintenance costs such as construction planning.
  • FIG. 1 is a block diagram showing the hardware configuration of the depth estimation device 100 according to the embodiment of the present disclosure.
  • the depth estimation device 100 includes a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage 14, an input unit 15, a display unit 16, and communication It has an interface (I/F) 17 .
  • Each component is communicatively connected to each other via a bus 19 .
  • the CPU 11 is a central processing unit that executes various programs and controls each part. That is, the CPU 11 reads a program from the ROM 12 or the storage 14 and executes the program using the RAM 13 as a work area. The CPU 11 performs control of each configuration and various arithmetic processing according to programs stored in the ROM 12 or the storage 14 . In this embodiment, the ROM 12 or storage 14 stores a depth estimation program.
  • the ROM 12 stores various programs and various data.
  • the RAM 13 temporarily stores programs or data as a work area.
  • the storage 14 is configured by a storage device such as a HDD (Hard Disk Drive) or SSD (Solid State Drive), and stores various programs including an operating system and various data.
  • HDD Hard Disk Drive
  • SSD Solid State Drive
  • the input unit 15 includes a pointing device such as a mouse and a keyboard, and is used for various inputs.
  • the display unit 16 is, for example, a liquid crystal display, and displays various information.
  • the display unit 16 may employ a touch panel system and function as the input unit 15 .
  • the communication interface 17 is an interface for communicating with other devices such as terminals.
  • the communication uses, for example, a wired communication standard such as Ethernet (registered trademark) or FDDI, or a wireless communication standard such as 4G, 5G, or Wi-Fi (registered trademark).
  • FIG. 2 is a block diagram showing the functional configuration of the depth estimation device 100 according to the embodiment of the present disclosure.
  • Each functional configuration is realized by the CPU 11 reading a depth estimation program stored in the ROM 12 or the storage 14, developing it in the RAM 13, and executing it.
  • the depth estimation device 100 includes a detector storage unit 102, a migration unit 110, an extremity information extraction unit 112, a buried object detection unit 114, and a depth estimation unit 116. It is configured. Migration processing is applied to the input GPR data in the migration unit 110, and the local information extraction unit 112 calculates the maximum coordinate value and relative dielectric constant for the output.
  • the embedded object detection unit 114 applies an embedded object detector to the GPR data, specifies the reaction site of the embedded object, and detects the position.
  • the depth estimating unit 116 picks up local maximum points around the position of the buried object reaction site, and picks up the dielectric constant of the local maximum point that maximizes the result of migration. The depth is calculated using this dielectric constant and output as the depth of the buried object.
  • the migration unit 110 performs migration processing while changing the dielectric constant according to the range based on the GPR data and the range of the dielectric constant obtained in advance, and outputs the migration result for each changed dielectric constant. demand.
  • the range of the relative permittivity is, for example, if the target is general soil, the relative permittivity changes from about 2 to 30 depending on the degree of moisture.
  • the range of dielectric constants that the object can take is set in advance.
  • a specific dielectric constant is taken out from this set range at regular intervals, and a migration process is applied.
  • the migration process is a process of correcting the parabolic reflected waveform that appears due to the directivity of the antenna based on the relative permittivity so that only the reflection directly below the antenna can be captured.
  • Kirchhoff migration method there is a Kirchhoff migration method as a representative migration processing method, other migration methods may be used.
  • FIG. 3 shows an example of processing results obtained by Kirchhoff migration.
  • the local information extraction unit 112 extracts each local maximum point from the migration result, and for each of the extracted local maximum points, the relative permittivity of the migration result, the coordinates of the traveling direction of the antenna for which the GPR data was measured, and the reflection time. Extract the three-dimensional data of the coordinates.
  • a local maximum is an example of a local point of this disclosure.
  • the local information extraction unit 112 assumes that the reaction is maximum at the center point of the reflection location for the migration result, and extracts the point where the migrated value is maximum (maximum value) as the local maximum point.
  • the migration results corresponding to the changing relative permittivity obtained in FIG. 4 are accumulated to form three-dimensional data as shown in FIG.
  • the three-dimensional data values are obtained for each local maximum point in each of the dielectric constant direction, the antenna traveling direction, and the reflection time direction.
  • the relative permittivity direction the relative permittivity in the migration result with the relative permittivity changed is obtained, in the antenna traveling direction, the coordinates of the antenna are obtained, and in the reflection time advancing direction, the coordinates of the reflection time are obtained.
  • the coordinates that take the maximum value are calculated based on this three-dimensional data, and the dielectric constant that maximizes the aggregated reaction for each buried object is obtained.
  • Antenna coordinates and reflection times are obtained from GPR data. Since the obtained local maximum coordinates are three-dimensional coordinates (relative permittivity, antenna traveling direction coordinates, reflection time coordinates), three values are obtained for each local maximum point.
  • the maximum value should be used, but when the GPR data is represented by real waves, negative values are aggregated. Since extreme values also occur, local minimum values may also be used at the same time. If the GPR data were complex waves, only maxima would be obtained, and if the GPR data were real waves, maxima and minima would be obtained.
  • the buried object detection unit 114 uses the detector of the detector storage unit 102 to detect the coordinates of the buried object in the GPR data.
  • the detector is trained to detect the position of the buried object by applying a machine learning technique used for image recognition with the original GPR data as input.
  • the detector storage unit 102 stores detector parameters learned in advance by a machine learning technique.
  • the configuration related to the buried object detection unit 114 is shown in FIG. Since detector parameters learned in advance are required for detection, GPR data for learning and learning data of annotation data of the corresponding buried object positions are prepared in advance, and the detector learning unit 104 learns the learning data. Train the detector to minimize the error between the correct answer and the output. Regarding the machine learning model and learning method used for learning and detection, for example, high detection accuracy can be expected when learning by error backpropagation using a detection model based on a convolutional neural network as used in Non-Patent Document 1. . In addition, even if it is a method other than this, you may use it. By applying the detector thus obtained, it is possible to obtain coordinate values indicating the rough position of each buried object appearing in the GPR data.
  • the depth estimator 116 is composed of a dielectric constant estimator 120 and a depth calculator 122, as shown in FIG.
  • the depth estimating unit 116 refers to the coordinates of the maximum point in the peripheral coordinates based on the coordinates of the buried object, adopts the relative permittivity corresponding to the maximum point, and adopts the relative permittivity and Estimate the depth of the buried object based on the reflection time.
  • the depth estimating unit 116 receives as input the coordinates of the maximum point and its maximum value output from the extremity information extraction unit 112 and the coordinates of the buried object output from the buried object detection unit 114 .
  • Relative permittivity estimator 120 limits the search range for the maximum value of the migration result to the surrounding coordinates for the coordinates of each buried object.
  • Peripheral coordinates shall be defined within ⁇ T X meters in the antenna traveling direction and within ⁇ TT seconds in the reflection time direction based on the coordinates of the buried object.
  • Search for local maxima in peripheral coordinates Refer to the coordinates of the searched maximal point and extract the maximal value. The coordinate at which the maximum value is maximum among the extracted maximum values is referred to, and the dielectric constant at the maximum point corresponding to the coordinate is adopted as the dielectric constant corresponding to the buried object.
  • the GPR data is a wave of real numbers, since the maximum and minimum values are obtained, the maximum and minimum points are searched for in the peripheral coordinates.
  • the maximum value and the minimum value are determined, respectively, and the average value of the relative permittivity corresponding to the maximum point and the relative permittivity corresponding to the minimum point is adopted. Also, the corresponding reflection time is adopted in the three-dimensional data including the adopted dielectric constant.
  • the depth calculator 122 calculates the detailed depth of the buried object using the estimated dielectric constant corresponding to each buried object. Using the reflection time t and the dielectric constant ⁇ r, the depth d is obtained by the following equation (1). ... (1)
  • FIG. 8 is a flowchart showing the flow of depth estimation processing by the depth estimation device 100 according to the embodiment of the present disclosure.
  • Depth estimation processing is performed by the CPU 11 reading a depth estimation program from the ROM 12 or the storage 14, developing it in the RAM 13, and executing it. The following processes are executed by the CPU 11 functioning as each part of the depth estimation device 100 .
  • step S100 the CPU 11 performs the migration process while changing the relative permittivity according to the range based on the GPR data and the range of the relative permittivity obtained in advance. Seek results.
  • step S102 the CPU 11 extracts each local maximum point from the migration result.
  • step S104 the CPU 11 extracts the three-dimensional data of the relative permittivity of the migration result, the coordinates of the traveling direction of the antenna that measured the GPR data, and the coordinates of the reflection time for each of the extracted local maximum points.
  • step S106 the CPU 11 uses the detector of the detector storage unit 102 to detect the coordinates of the buried object in the GPR data.
  • step S108 the CPU 11 refers to the coordinates of the local maximum point in the peripheral coordinates based on the coordinates of the buried object, and adopts the dielectric constant corresponding to the local maximum point.
  • step S110 the CPU 11 estimates the depth of the buried object based on the adopted dielectric constant and reflection time.
  • the depth at which the buried object is buried can be obtained using the relative permittivity.
  • the technique of the present disclosure can be said to be a method of automatically estimating the dielectric constant using the migration results.
  • the technique of the present disclosure can be said to be a method of automatically estimating the dielectric constant using the migration results.
  • the depth estimation processing executed by the CPU reading the software (program) in the above embodiment may be executed by various processors other than the CPU.
  • the processor is a PLD (Programmable Logic Device) whose circuit configuration can be changed after manufacturing, such as an FPGA (Field-Programmable Gate Array), and an ASIC (Application Specific Integrated Circuit) to execute specific processing.
  • a dedicated electric circuit or the like which is a processor having a specially designed circuit configuration, is exemplified.
  • the depth estimation process may be performed by one of these various processors, or by a combination of two or more processors of the same or different type (e.g., multiple FPGAs and a combination of CPU and FPGA). combination, etc.). More specifically, the hardware structure of these various processors is an electric circuit in which circuit elements such as semiconductor elements are combined.
  • the depth estimation program has been pre-stored (installed) in the storage 14, but the present invention is not limited to this.
  • Programs are stored in non-transitory storage media such as CD-ROM (Compact Disk Read Only Memory), DVD-ROM (Digital Versatile Disk Read Only Memory), and USB (Universal Serial Bus) memory.
  • CD-ROM Compact Disk Read Only Memory
  • DVD-ROM Digital Versatile Disk Read Only Memory
  • USB Universal Serial Bus
  • (Appendix 1) memory at least one processor connected to the memory; including The processor Based on the ground penetrating radar data, which is two-dimensional data obtained by measuring the buried object buried in the ground, and the range of relative permittivity obtained in advance, a predetermined migration process is performed according to the range to change the relative permittivity.
  • a migration unit that performs while changing and obtains the migration result for each changed dielectric constant; From the migration result, extract each local point that is either a local maximum point or a local maximum point and a local minimum point of the migrated value, and with respect to the local point, relative permittivity in the migration result, Extracting three-dimensional data of the coordinates of the direction of travel of the antenna that measured the ground penetrating radar data and the coordinates of the reflection time, detecting the coordinates of the buried object using a pre-learned detector; referring to the coordinates of the local point of the three-dimensional data in the peripheral coordinates based on the coordinates of the buried object, adopting the relative permittivity corresponding to the local point, and adopting the relative permittivity and the reflection time, estimating the depth of the buried object;
  • a depth estimator configured to:
  • Appendix 2 A non-transitory storage medium storing a program executable by a computer to perform a depth estimation process, Based on the ground penetrating radar data, which is two-dimensional data obtained by measuring the buried object buried in the ground, and the range of relative permittivity obtained in advance, a predetermined migration process is performed according to the range to change the relative permittivity.
  • a migration unit that performs while changing and obtains the migration result for each changed dielectric constant; From the migration result, extract each local point that is either a local maximum point or a local maximum point and a local minimum point of the migrated value, and with respect to the local point, relative permittivity in the migration result, Extracting three-dimensional data of the coordinates of the direction of travel of the antenna that measured the ground penetrating radar data and the coordinates of the reflection time, detecting the coordinates of the buried object using a pre-learned detector; referring to the coordinates of the local point of the three-dimensional data in the peripheral coordinates based on the coordinates of the buried object, adopting the relative permittivity corresponding to the local point, and adopting the relative permittivity and the reflection time, estimating the depth of the buried object; Non-transitory storage media.
  • Depth estimation device 100 Depth estimation device 102 Detector storage unit 104 Detector learning unit 110 Migration unit 112 Local information extraction unit 114 Buried object detection unit 116 Depth estimation unit 120 Relative permittivity estimation unit 122 Depth calculation unit

Landscapes

  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • Electromagnetism (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Radar Systems Or Details Thereof (AREA)
  • Geophysics And Detection Of Objects (AREA)

Abstract

La présente invention peut utiliser la constante diélectrique pour déterminer la profondeur à laquelle un objet enfoui est enfoui.　Dans ce dispositif d'estimation de profondeur, une unité de migration effectue une migration tout en changeant une constante diélectrique en fonction d'une plage de constante diélectrique et détermine des résultats de migration pour chaque constante diélectrique modifiée. Une unité d'extraction d'informations d'extrémité extrait des données tridimensionnelles pour un point extrême. Une unité de détection d'objet enfoui fait intervenir un dispositif de détection qui a été entraîné à l'avance pour détecter les coordonnées d'un objet enfoui. En se référant aux coordonnées du point extrême dans des données tridimensionnelles pour des coordonnées proches sur la base des coordonnées de l'objet enfoui, une unité d'estimation de profondeur adopte la constante diélectrique correspondant au point extrême et estime la profondeur de l'objet enfoui sur la base de la constante diélectrique adoptée et d'un temps de réflexion.
PCT/JP2021/022937 2021-06-16 2021-06-16 Dispositif d'estimation de profondeur, procédé d'estimation de profondeur et programme d'estimation de profondeur WO2022264342A1 (fr)

Priority Applications (2)

Application Number Priority Date Filing Date Title
JP2023528867A JPWO2022264342A1 (fr) 2021-06-16 2021-06-16
PCT/JP2021/022937 WO2022264342A1 (fr) 2021-06-16 2021-06-16 Dispositif d'estimation de profondeur, procédé d'estimation de profondeur et programme d'estimation de profondeur

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2021/022937 WO2022264342A1 (fr) 2021-06-16 2021-06-16 Dispositif d'estimation de profondeur, procédé d'estimation de profondeur et programme d'estimation de profondeur

Publications (1)

Publication Number Publication Date
WO2022264342A1 true WO2022264342A1 (fr) 2022-12-22

Family

ID=84527319

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/JP2021/022937 WO2022264342A1 (fr) 2021-06-16 2021-06-16 Dispositif d'estimation de profondeur, procédé d'estimation de profondeur et programme d'estimation de profondeur

Country Status (2)

Country Link
JP (1) JPWO2022264342A1 (fr)
WO (1) WO2022264342A1 (fr)

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH05232220A (ja) * 1992-02-20 1993-09-07 Osaka Gas Co Ltd 比誘電率の測定方法および装置ならびに埋設物の探査装置
JPH07270528A (ja) * 1994-03-28 1995-10-20 Osaka Gas Co Ltd 比誘電率の測定方法および装置ならびに埋設物の探査装置
JPH11271440A (ja) * 1998-03-25 1999-10-08 Osaka Gas Co Ltd ３次元探査方法及び装置

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH05232220A (ja) * 1992-02-20 1993-09-07 Osaka Gas Co Ltd 比誘電率の測定方法および装置ならびに埋設物の探査装置
JPH07270528A (ja) * 1994-03-28 1995-10-20 Osaka Gas Co Ltd 比誘電率の測定方法および装置ならびに埋設物の探査装置
JPH11271440A (ja) * 1998-03-25 1999-10-08 Osaka Gas Co Ltd ３次元探査方法及び装置

Also Published As

Publication number Publication date
JPWO2022264342A1 (fr) 2022-12-22

Similar Documents

Publication Publication Date Title
KR101829560B1 (ko) 칼만 필터를 기반으로 하는 용량 예측 방법, 시스템 및 컴퓨터 장치
JP6239664B2 (ja) 周辺環境推定装置及び周辺環境推定方法
KR101912195B1 (ko) 목표의 실시간 위치 측정과 지도 구축 방법 및 장치
CN105737826B (zh) 行人室内定位方法
CN110501010A (zh) 确定移动设备在地理区域中的位置
US20140142891A1 (en) Generaton of map data
CN103217673B (zh) 一种非均匀Weibull杂波背景下的CFAR检测方法
EP3693759B1 (fr) Système et procédé de suivi de mouvement d'une cible dans un environnement intérieur
KR101628154B1 (ko) 수신 신호 세기를 이용한 다중 표적 추적 방법
CN104180799A (zh) 一种基于自适应蒙特卡罗定位的机器人定位方法
KR101960280B1 (ko) 자기장 지도를 수정하는 방법, 자기장 지도를 수정하는 사용자 단말 및 서버
Zeng et al. An elevation difference model for building height extraction from stereo-image-derived DSMs
CN109828302A (zh) 一种基于多振动传感器的震源定位方法及装置
WO2022264342A1 (fr) Dispositif d'estimation de profondeur, procédé d'estimation de profondeur et programme d'estimation de profondeur
KR101354522B1 (ko) 변분법과 초기추정법을 이용한 고분해능 레이더 바람장 산출방법
CN114641701A (zh) 使用表面穿透雷达和深度学习的改进的导航和定位
JP5765163B2 (ja) 自己位置推定装置、方法、及びプログラム
Choi et al. Correlation-based scan matching using ultrasonic sensors for EKF localization
KR101480834B1 (ko) 다중 경로 음파 전달 모델 및 표적 식별을 이용한 표적 기동분석 방법
CN113868807A (zh) 三维环境分析方法和设备、计算机存储介质以及无线传感器系统
US11686837B2 (en) Apparatus and method for determining kinetic information
He et al. Stereo radargrammetry in south-east Asia using TerraSAR-X stripmap data
CN112781591A (zh) 机器人定位方法、装置、计算机可读存储介质及机器人
JP2020008310A (ja) 物体検出装置
CN113361379B (zh) 生成目标检测系统和检测目标的方法及装置

Legal Events

Date Code Title Description
121 Ep: the epo has been informed by wipo that ep was designated in this application

Ref document number: 21946019

Country of ref document: EP

Kind code of ref document: A1

WWE Wipo information: entry into national phase

Ref document number: 2023528867

Country of ref document: JP

NENP Non-entry into the national phase

Ref country code: DE