CN109799476B - Relative positioning method and device, computer readable storage medium - Google Patents
Relative positioning method and device, computer readable storage medium Download PDFInfo
- Publication number
- CN109799476B CN109799476B CN201711144322.3A CN201711144322A CN109799476B CN 109799476 B CN109799476 B CN 109799476B CN 201711144322 A CN201711144322 A CN 201711144322A CN 109799476 B CN109799476 B CN 109799476B
- Authority
- CN
- China
- Prior art keywords
- nodes
- relative positioning
- azimuth
- measurement error
- matching pairs
- 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.)
- Active
Links
Images
Landscapes
- Position Fixing By Use Of Radio Waves (AREA)
- Image Processing (AREA)
Abstract
The present disclosure relates to a method and apparatus for relative positioning between a plurality of nodes based on distance measurement and a panoramic image, and a computer-readable storage medium. The method for relative positioning among a plurality of nodes comprises the following steps: obtaining a plurality of possible matching pairs of relative distances and azimuth angles between the plurality of nodes; converting the azimuth into a unified coordinate system, obtaining a plurality of candidate matching pairs corresponding to the plurality of possible matching pairs; determining a difference between the similarity of the candidate matching pair between the two nodes and the ideal similarity thereof based on a predetermined error model; based on the difference, filtering out impossible candidate matching pairs, obtaining a plurality of correct matching pairs of relative distances and azimuth angles among the plurality of nodes, and determining the relative positioning among the plurality of nodes.
Description
Technical Field
The present disclosure relates to the field of ranging and positioning, and more particularly, to a method and apparatus for relative positioning between a plurality of nodes based on distance measurement and a panoramic image, and a computer-readable storage medium.
Background
In application scenarios such as Virtual Reality (VR), augmented Reality (AR), and Mixed Reality (MR), it is necessary to sense objects appearing in a scene and determine relative positions between the respective objects and changes thereof, so as to realize interaction between the respective objects.
With conventional relative positioning schemes, in order to achieve sensing and relative positioning of objects in a scene, it is generally necessary to arrange markers and/or signal sources in the scene, so that markers always appear in the picture and cannot extend to areas outside the markers, while relative positioning based on signal sources can only be applied to robots performing specific tasks in a specific scene, and cannot be applied to augmented reality scenes. In addition, the GPS positioning in combination with the penetration is only suitable for positioning in outdoor large scenes, and is usually low in local accuracy, and is directly used for the phenomenon that virtual objects frequently jump and drift in augmented reality scenes.
Furthermore, in existing VR relative positioning schemes, there are some problems and drawbacks that cannot be solved. For example, in aIn an active spatial positioning scheme (such as a lighthouse technology of HTC) that relies on laser or infrared signal scanning, although full-range positioning can be achieved, efficiency is limited because scanning requires a certain scanning period, resulting in poor real-time performance; and additional equipment (scanning device) needs to be introduced, increasing the deployment cost. In another approach that utilizes markers on a head-mounted or handheld device for spatial modeling (such as the Constellation positioning technique and sony Play of Facebook (Facebook))
The handle ball positioning technology), although the real-time performance is high, additional receivers are required to be introduced for the marking point signal receiving and modeling judgment, and the deployment cost is also high. It is therefore desirable to provide a solution that is real-time and does not require as much additional equipment as possible, and that allows relative positioning to be achieved by the user's head-mounted device, especially when multiple people are in a VR scene.
Disclosure of Invention
In view of the above problems, the present disclosure provides a method and apparatus for relative positioning between a plurality of nodes based on distance measurement and a panoramic image, and a computer-readable storage medium.
According to an embodiment of the present disclosure, there is provided a relative positioning method between a plurality of nodes, including: obtaining a plurality of possible matching pairs of relative distances and azimuth angles between the plurality of nodes; converting the azimuth into a unified coordinate system, obtaining a plurality of candidate matching pairs corresponding to the plurality of possible matching pairs; determining a difference between the similarity of the candidate matching pair between the two nodes and the ideal similarity thereof based on a predetermined error model; based on the difference, filtering out impossible candidate matching pairs, obtaining a plurality of correct matching pairs of relative distances and azimuth angles among the plurality of nodes, and determining the relative positioning among the plurality of nodes.
Further, according to a relative positioning method of an embodiment of the present disclosure, wherein the filtering out unlikely candidate matching pairs based on the difference includes: comparing a plurality of said differences corresponding to a plurality of candidate matching pairs between two nodes, selecting the candidate matching pair corresponding to the smallest of said differences as the correct matching pair.
Further, a relative positioning method according to an embodiment of the present disclosure, wherein a difference between a similarity of a candidate matching pair and an ideal similarity thereof is caused by a relative distance measurement error, a horizontal azimuth measurement error, and a vertical azimuth measurement error, wherein the determining a difference between a similarity of a candidate matching pair between two nodes and an ideal similarity thereof based on a predetermined error model further comprises: and adjusting the weights of the relative distance measurement error, the horizontal azimuth angle measurement error and the vertical azimuth angle measurement error in the preset error model.
Furthermore, a relative positioning method according to an embodiment of the present disclosure, wherein the obtaining a plurality of possible matching pairs of relative distances and azimuth angles between the plurality of nodes includes: performing image recognition on a panoramic image photographed from one of the plurality of nodes, determining other nodes in the panoramic image, and determining azimuth information with the other nodes through the panoramic image; performing wireless ranging using wireless signals transmitted at the plurality of nodes, determining relative distance information between the plurality of nodes; sharing node identification information among the plurality of nodes; and forming the possible matching pairs by the azimuth angle information, the relative distance information and the node identification information.
Further, a relative positioning method according to an embodiment of the present disclosure, wherein the unified coordinate system is a geodetic coordinate system determined based on magnetic field sensing.
Further, a relative positioning method according to an embodiment of the present disclosure, wherein the relative distance measurement error is derived from an error of the wireless ranging, the horizontal azimuth measurement error is derived from an error of the image recognition and the magnetic field sensing, and the vertical azimuth measurement error is derived from an error of the image recognition.
According to another embodiment of the present disclosure, there is provided a relative positioning apparatus for use between a plurality of nodes, including: a processor; and a memory configured to store computer program instructions; wherein, when the computer program instructions are executed by the processor, the processor is to: obtaining a plurality of possible matching pairs of relative distances and azimuth angles between the plurality of nodes; converting the azimuth into a unified coordinate system, obtaining a plurality of candidate matching pairs corresponding to the plurality of possible matching pairs; determining a difference between the similarity of the candidate matching pair between the two nodes and the ideal similarity thereof based on a predetermined error model; filtering out candidate matching pairs with the difference larger than a preset threshold value, obtaining a plurality of correct matching pairs of relative distances and azimuth angles among the plurality of nodes, and determining the relative positioning among the plurality of nodes.
Further, a relative positioning apparatus according to another embodiment of the present disclosure, wherein said processor compares a plurality of said differences between two nodes corresponding to a plurality of candidate matching pairs, selects a candidate matching pair corresponding to a smallest said difference as a correct matching pair.
Further, a relative positioning apparatus according to another embodiment of the present disclosure, wherein the difference between the similarity of a candidate matching pair and its ideal similarity is caused by a relative distance measurement error, a horizontal azimuth measurement error and a vertical azimuth measurement error, wherein, when the computer program instructions are executed by the processor, the processor adjusts the weights of the relative distance measurement error, the horizontal azimuth measurement error and the vertical azimuth measurement error in the predetermined error model.
Furthermore, a relative positioning apparatus according to another embodiment of the present disclosure further includes: a panoramic image acquirer for acquiring a panoramic image photographed from one of the plurality of nodes; a wireless signal transceiver for transceiving wireless signals between the plurality of nodes; wherein, when the computer program instructions are executed by the processor, the processor performs image recognition on a panoramic image taken from one of the plurality of nodes, determines other nodes in the panoramic image, and determines azimuth information with the other nodes from the panoramic image; performing wireless ranging using the wireless signal, determining relative distance information between the plurality of nodes; sharing node identification information among the plurality of nodes; and forming the possible matching pairs by the azimuth angle information, the relative distance information and the node identification information.
Further, a relative positioning apparatus according to another embodiment of the present disclosure, wherein the unified coordinate system is a geodetic coordinate system determined based on magnetic field sensing.
Further, a relative positioning device according to another embodiment of the present disclosure, wherein the relative distance measurement error is derived from an error of the wireless ranging, the horizontal azimuth measurement error is derived from an error of the image recognition and the magnetic field sensing, and the vertical azimuth measurement error is derived from an error of the image recognition.
According to yet another embodiment of the present disclosure, there is provided a computer readable storage medium storing computer program instructions, wherein the computer program instructions, when executed by a processor, cause the processor to perform the relative positioning method as described above.
According to the relative positioning method and device between multiple nodes of the present disclosure, by using the distance measurement and the panoramic image, a filter for filtering noise data is generated and analytically modeled based on the distance measurement and the measurement error of the panoramic image, and the parameter of the filter is adjusted in real time based on the environmental parameter during the positioning process, thereby realizing efficient relative positioning independent of scene configuration and adaptive to the environmental and equipment errors without additionally configuring special equipment.
Drawings
The above and other objects, features and advantages of the present invention will become more apparent by describing in more detail embodiments of the present invention with reference to the attached drawings. The accompanying drawings are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings, like reference numbers generally indicate like parts or steps.
FIG. 1 is a flow chart illustrating a method for relative positioning between a plurality of nodes according to an embodiment of the present disclosure;
FIG. 2 is a hardware block diagram illustrating a relative positioning apparatus for use between a plurality of nodes according to an embodiment of the present disclosure;
FIG. 3A is a scene schematic illustrating relative positioning between multiple nodes according to an embodiment of the disclosure;
fig. 3B is a block diagram illustrating a configuration of a relative positioning apparatus in the scenario of relative positioning between a plurality of nodes illustrated in fig. 3A;
FIG. 4 is a flow chart further illustrating an acquisition process for possible matching pairs in a relative positioning method between multiple nodes according to an embodiment of the present disclosure;
fig. 5A and 5B are schematic diagrams illustrating a coordinate conversion process in a relative positioning method for between a plurality of nodes according to an embodiment of the present disclosure;
FIG. 6 is a flow chart further illustrating a method for relative positioning between a plurality of nodes according to an embodiment of the present disclosure;
fig. 7 is a schematic diagram illustrating the selection of correct matching pairs by parameter adjustment; and
fig. 8 is a schematic diagram illustrating a computer-readable storage medium according to an embodiment of the present disclosure.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more apparent, example embodiments according to the present disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the described embodiments are only some of the embodiments of the present disclosure, and not all of the embodiments of the present disclosure, and it is to be understood that the present disclosure is not limited by the example embodiments described herein. All other embodiments, which can be derived by a person skilled in the art from the embodiments described in the disclosure without inventive step, shall fall within the scope of protection of the disclosure.
Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings.
First, a relative positioning method for between a plurality of nodes according to an embodiment of the present disclosure will be summarized with reference to fig. 1. As shown in fig. 1, a method for relative positioning between a plurality of nodes according to an embodiment of the present disclosure includes the following steps.
In step S101, a plurality of possible matching pairs of relative distances and azimuth angles between a plurality of nodes is obtained. As will be described in detail below, in one embodiment of the present disclosure, one node may refer to a user in a scene that is configured with a relative positioning device. The relative distance between two nodes may be determined by performing wireless ranging through a wireless signal transmitted at the node, and the azimuth angle between two nodes may be determined by performing image recognition on a panoramic image photographed from one node, determining other nodes in the panoramic image, and determining azimuth angle information with the other nodes through the panoramic image. Further, as will be described below, the possible matching pairs also include a device identifier of a wireless signal transmitted at the node as the ID of the node. Thereafter, the process proceeds to step S102.
In step S102, the azimuth is converted into a unified coordinate system, and a plurality of candidate matching pairs corresponding to the plurality of possible matching pairs are obtained. In one embodiment of the present disclosure, it is necessary to convert a plurality of possible matching pairs represented in the spherical coordinate system of each node itself into a unified coordinate system. For example, the unified coordinate system may be a geodetic coordinate system based on local magnetic field information (compass). Through the coordinate conversion in step S102, a plurality of normalized and unified candidate matching pairs are obtained. Thereafter, the process proceeds to step S103.
In step S103, a difference between the similarity of the candidate matching pair between the two nodes and the ideal similarity thereof is determined based on a predetermined error model. In one embodiment of the present disclosure, based on the measurement process of obtaining the plurality of candidate matching pairs in steps S101 and S102 above, a possible error source is analytically determined to build a predetermined error model. Specifically, the relative distance measurement error is derived from an error of the wireless ranging, the horizontal azimuth angle measurement error is derived from an error of the image recognition and the magnetic field sensing, and the vertical azimuth angle measurement error is derived from an error of the image recognition. The difference between the similarity of the candidate matching pairs between the two nodes and their ideal similarity can be obtained by a predetermined error model based on measurement error sources, which will be described in detail below. In addition, in a predetermined error model based on a measurement error source, which will be described in detail below, different weights may be assigned to the relative distance measurement error, the horizontal azimuth measurement error, and the vertical azimuth measurement error according to the real environment, so as to further distinguish similar differences. Thereafter, the process proceeds to step S104.
In step S104, based on the difference, impossible candidate matching pairs are filtered out, multiple correct matching pairs of relative distances and azimuth angles between the multiple nodes are obtained, and relative positioning between the multiple nodes is determined. In one embodiment of the present disclosure, a candidate matching pair corresponding to the smallest of said differences is selected as the correct matching pair by comparing a plurality of said differences corresponding to a plurality of candidate matching pairs between two nodes.
Fig. 2 is a hardware block diagram illustrating a relative positioning apparatus for between a plurality of nodes according to an embodiment of the present disclosure. As shown in fig. 2, a relative positioning apparatus 20 for use between a plurality of nodes according to an embodiment of the present disclosure includes a processor 201 and a memory 202. The memory 202 is configured to store computer program instructions which, when executed by the processor 201, perform a method for relative positioning between a plurality of nodes as described above with reference to fig. 1 and subsequent figures above.
The processor 201 may be a Central Processing Unit (CPU) or other form of processing unit having data processing capabilities and/or instruction execution capabilities, and may control other components in the relative positioning apparatus 20 to perform desired functions. In case the relative positioning apparatus 20 is configured in a device such as a Head Mounted Display (HMD), the processor 201 may be a central processing unit, a Graphics Processing Unit (GPU) or a dedicated control unit of the Head Mounted Display (HMD).
The memory 202 may include one or more computer program products that may include various forms of computer-readable storage media, such as volatile memory and/or non-volatile memory. The volatile memory may include, for example, random Access Memory (RAM), cache memory (cache), and/or the like. The non-volatile memory may include, for example, read Only Memory (ROM), hard disk, flash memory, etc. One or more computer program instructions may be stored on the computer-readable storage medium,
the processor 201 may execute the program instructions to implement the steps of: obtaining a plurality of possible matching pairs of relative distances and azimuth angles between the plurality of nodes; converting the azimuth into a unified coordinate system, obtaining a plurality of candidate matching pairs corresponding to the plurality of possible matching pairs; determining a difference between the similarity of the candidate matching pair between the two nodes and the ideal similarity thereof based on a predetermined error model; based on the difference, filtering out impossible candidate matching pairs, obtaining a plurality of correct matching pairs of relative distances and azimuth angles among the plurality of nodes, and determining the relative positioning among the plurality of nodes.
Fig. 3A is a scenario diagram illustrating relative positioning between multiple nodes according to an embodiment of the present disclosure. As shown in fig. 3A, in a scenario where relative positioning needs to be performed, there are, for example, three nodes P1, P2, and P3. A relative positioning means is provided at each node.
Fig. 3B is a block diagram illustrating a configuration of a relative positioning apparatus in the scenario of relative positioning between a plurality of nodes illustrated in fig. 3A. As shown in fig. 3B, the relative positioning device 20 is configured in an HMD worn by the user, which includes a panoramic image acquirer 301 and a wireless signal transceiver 302. It will be readily appreciated that the relative positioning device 20 of course also includes a processor and memory (not shown in fig. 3B) as described above with reference to fig. 2. It should be noted that the panoramic image acquirer 301 and the wireless signal transceiver 302 may utilize a panoramic camera and a wireless signal transceiver module already configured in a general-purpose HMD, without additionally configuring other special-purpose devices for relative positioning.
Specifically, the panoramic image acquirer 301 is configured to acquire a panoramic image from one of the plurality of nodes. The wireless signal transceiver 302 is used for transceiving wireless signals between the plurality of nodes.
Hereinafter, a process of acquiring a possible matching pair in the scenario of relative positioning between a plurality of nodes as shown in fig. 3A using the relative positioning apparatus 20 as shown in fig. 3B will be described in further detail with reference to fig. 4.
Fig. 4 is a flow chart further illustrating an acquisition process for a possible matching pair in a relative positioning method between a plurality of nodes according to an embodiment of the present disclosure. As shown in fig. 4, the acquisition process for a possible matching pair in the relative positioning method between a plurality of nodes includes the following steps.
In step S401, image recognition is performed on a panoramic image taken from one of a plurality of nodes, other nodes in the panoramic image are determined, and azimuth information with the other nodes is determined from the panoramic image. In one embodiment of the present disclosure, the panoramic image acquirer 301 at one node P1 photographs a panoramic image, and determines other nodes P2 and P3 in the panoramic image by performing image recognition on the photographed panoramic image, and determines azimuth information P with the other nodes from the panoramic image 12 (α 12 ，β 12 ) And P 13 (α 13 ，β 13 ). Thereafter, the process proceeds to step S402.
In step S402, wireless ranging is performed using wireless signals transmitted at a plurality of nodes, and relative distance information between the plurality of nodes is determined. In one embodiment of the present disclosure, wireless signals transmitted by wireless signal transceiver 302 at one node P1 perform wireless ranging, for example using Wi-Fi time-of-flight (TOF) ranging. For example, the distance between node P1 and nodes P2 and P3 is determined as d by wireless ranging 12 And d 13 . Thereafter, the process proceeds to step S403.
In step S403, node identification information is shared among a plurality of nodes. In one embodiment of the present disclosure, the device identification transmitted by the wireless signal transceiver 302 is used as the node identification information. For example, the node P1 obtains node identification information I of the nodes P2 and P3 2 And I 3 . Thereafter, the processing proceeds toStep S404.
In step S404, a possible matching pair is composed with the azimuth information, the relative distance information, and the node identification information. In one embodiment of the present disclosure, after the azimuth information, the relative distance information and the node identification information are obtained through the above steps S401 to S403, it is determined that there is a possible matching pair of the node P1 (P404) 12 (α 12 ，β 12 )，d 12 ，I 2 ) And (P) 13 (α 13 ，β 13 )，d 13 ，I 3 ). More generally, for a node i of a plurality of nodes in a relatively positioned scenario, its possible matching pair may be represented as (P) ij (α ij ，β ij )，d ij ，I j )。
Fig. 5A and 5B are schematic diagrams illustrating a coordinate conversion process in a relative positioning method for between a plurality of nodes according to an embodiment of the present disclosure.
Fig. 5A shows a possible matching pair (P (α + e, β + ζ), d + δ, I) obtained in a node's own spherical coordinate system. The measurement error terms epsilon, zeta and delta are added compared to the representation of possible matching pairs (P (alpha, beta), d, I) in fig. 4. Wherein the horizontal azimuth measurement error ∈ (d) and the vertical azimuth measurement error ζ (d) are derived from image recognition of the panoramic image. The relative distance measurement error δ is derived from distance measurement errors such as Wi-Fi TOF.
As described above, in order to determine the relative positioning between all nodes in the relative positioning scenario, it is necessary to convert the possible matching pairs obtained in the own spherical coordinate system of each node into candidate matching pairs in a unified coordinate system. Fig. 5B shows a coordinate normalization process based on magnetic field sensing. Since the relative distance component d and the node identification information I in the possible matching pair are not changed in this coordinate normalization process, fig. 5B shows only the conversion of the azimuth angle component. Converting P (α + e, β + ζ) in the node's own spherical coordinate system into P (θ + e + λ, β + ζ) in a geodetic coordinate system determined based on the magnetic field, wherein the vertical azimuth component is not changed in the conversion nor is a new error introduced. The horizontal azimuth component P (α + ε) is converted to P (θ + ε + λ)) In which an error lambda (m) is introduced due to the magnetic field sensing. More generally, for a node i of a plurality of nodes in a relatively positioned scene, its candidate matching pair may be represented as (P) ij (θ ij ，β ij )，d ij ，I j ). After candidate matching pairs in the unified coordinate system are obtained and possible sources of error for each component in the candidate matching pairs are identified, a filter model may be involved based on the sources of error to filter out unlikely candidate matching pairs due to measurement errors.
Fig. 6 is a flow chart further illustrating a method for relative positioning between multiple nodes, in accordance with an embodiment of the present disclosure.
In step S601, a plurality of possible matching pairs of relative distances and azimuth angles between a plurality of nodes is obtained. In one embodiment of the present disclosure, first, the panoramic image acquirer 301 at one node captures a panoramic image, and by performing image recognition on the captured panoramic image, determines other nodes in the panoramic image, and determines azimuth information P with the other nodes from the panoramic image ij (α ij ，β ij ) (ii) a Performing wireless ranging using wireless signals transmitted at a plurality of nodes, determining relative distance information d between the plurality of nodes ij (ii) a Device identification transmitted by wireless signal transceivers 302 at a plurality of nodes as node identification information I j . Thus, the plurality of possible matching pairs of relative distances and azimuth angles between the plurality of nodes is denoted as (P) ij (α ij ，β ij )，d ij ，I j ). Thereafter, the process proceeds to step S602.
In step S602, the azimuth is converted into a unified coordinate system, and a plurality of candidate matching pairs corresponding to the plurality of possible matching pairs are obtained. In one embodiment of the present disclosure, after the azimuth is converted to the unified coordinate system, a plurality of candidate matching pairs corresponding to the plurality of possible matching pairs is represented as (P) ij (θ ij ，β ij )，d ij ，I j ). Thereafter, the process proceeds to step S603.
In step S603, a difference between the similarity of the candidate matching pair between the two nodes and the ideal similarity thereof is determined based on a predetermined error model. From the acquisition process of the plurality of candidate matching pairs, a predetermined error model may be determined, wherein:
the relationship between different distance information d is shown in expression (1)
dij＝dji+δ (1)
The relationship between the different vertical azimuth angles β is shown in expression (2)
βij＝-βji+ζ (2)
The relationship between the different horizontal azimuth angles θ is shown in expression (3)
θij＝θji+π+ε+λ (3)
The relative distance measurement error resulting from the error δ of wireless ranging, the vertical azimuth measurement error resulting from the error ζ of image recognition, and the horizontal azimuth measurement error resulting from the image recognition error ∈ and the magnetic field sensing error λ are included in the expressions (1) to (3) above.
Based on the relationships between nodes and the associated errors represented by the above expressions (1) to (3), the similarity of matching pairs between two nodes is represented by a relationship vector s, as shown in expression (4):
for two nodes, assuming no measurement error exists, the ideal similarity of their matching pairs is represented by the vector n as:
that is, for a correct matched pair of nodes, the distances between the nodes are the same, the vertical azimuths are in opposite relation, and the horizontal azimuths are in complementary relation.
The filter model is designed to represent the difference between the similarity of candidate matching pairs between two nodes and their ideal similarity:
similarity<s,n> (6)
thereafter, the process proceeds to step S604.
In step S604, weights of the relative distance measurement error, the horizontal azimuth measurement error, and the vertical azimuth measurement error in the predetermined error model are adjusted.
It is to be understood that step S604 is not necessary for each candidate matching pair. Referring to fig. 7, fig. 7 is a schematic diagram illustrating selection of a correct matching pair by parameter adjustment. As shown, the matching pair represented by the vector Sa matches the ideal similarity vector n, and Sa can be determined to be the correct matching pair. However, for candidate matching pairs represented by the vectors Sb and Sc, it is difficult to distinguish the relationship between the vectors Sb and Sc and the ideal similarity vector n, and it is necessary to adjust the weight of each error term according to the usage environment. The relationship vector s' to which the weight is assigned is as shown in expression (7):
for example, in the case of poor magnetic field sensing conditions, the weight k corresponding to the magnetic field sensing error may be reduced λ (ii) a In the case where the image recognition accuracy is not satisfactory, the weight k corresponding to the image recognition error can be reduced β And k ∈ (ii) a Similarly, the weight k of the error corresponding to the radio ranging may be adjusted according to the ranging condition δ . It should be noted that part of the error of wireless ranging is derived from the ranging method itself, for example, the WiFi TOF theoretically has an error of 30 cm. Another part of the error of wireless ranging is derived from the ranging conditions, such as the environment of the ranging. If the number of partitions and walls in the environment is large, the error is increased due to the multipath effect; if the used distance measurement is a wireless signal, the wireless signal can be absorbed under the condition that metal exists in the environment; if there are many wireless signal sources, such as multiple access points or bluetooth, the ranging may be interfered.
As shown in fig. 7, after adjusting the weight of the relative distance measurement error, the horizontal azimuth measurement error, and/or the vertical azimuth measurement error in the predetermined error model according to circumstances, the vectors Sb and Sc are changed to Sb 'and Sc', respectively. Thereafter, the process proceeds to step S605.
In step S605, the candidate matching pair with the smallest difference is retained. As shown in fig. 7, for the vectors Sb ' and Sc ' obtained after the adjustment, sb ' closer to n will remain. Thereafter, the process proceeds to step S606.
In step S606, the impossible candidate matching pairs are filtered out, a plurality of correct matching pairs of relative distances and azimuth angles between the plurality of nodes are obtained, and the relative positioning between the plurality of nodes is determined.
The relative positioning method and the relative positioning apparatus according to the embodiment of the present disclosure are described above with reference to fig. 1 to 7. Fig. 8 is a schematic diagram further illustrating a computer-readable storage medium according to an embodiment of the present disclosure.
As shown in fig. 8, a computer-readable storage medium 800 according to embodiments of the present disclosure has stored thereon computer program instructions 801. The computer program instructions 801, when executed by a processor, perform the relative positioning method according to embodiments of the present disclosure described with reference to the above figures.
In the above, the relative positioning method and apparatus between a plurality of nodes according to the embodiments of the present disclosure are described with reference to the accompanying drawings, by using a distance measurement and a panorama image, generating an analysis modeling filter for filtering noise data based on a measurement error of the distance measurement and the panorama image, and adjusting a parameter of the filter in real time based on an environmental parameter during positioning, thereby achieving efficient relative positioning independent of a scene configuration and adaptive to an environmental and equipment error without additionally configuring a dedicated equipment.
The foregoing describes the general principles of the present invention in conjunction with specific embodiments, however, it is noted that the advantages, effects, etc. mentioned in this disclosure are only examples and not limitations, and should not be considered essential to every embodiment of the present invention. Furthermore, the foregoing disclosure of specific details is for the purpose of illustration and description and is not intended to be limiting, since the invention is not limited to the specific details described above.
The block diagrams of devices, apparatuses, devices, systems involved in the present disclosure are only given as illustrative examples and are not intended to require or imply that the connections, arrangements, configurations must be made in the manner shown in the block diagrams. These devices, apparatuses, devices, systems may be connected, arranged, configured in any manner, as will be appreciated by one skilled in the art. Words such as "including," "comprising," "having," and the like are open-ended words that mean "including, but not limited to," and are used interchangeably herein. The words "or" and "as used herein mean, and are used interchangeably with, the word" and/or, "unless the context clearly dictates otherwise. The word "such as" is used herein to mean, and is used interchangeably with, the phrase "such as but not limited to".
The flowchart of steps in the present disclosure and the above description of the methods are only given as illustrative examples and are not intended to require or imply that the steps of the various embodiments must be performed in the order given, some steps may be performed in parallel, independently of each other or in other suitable orders. Additionally, words such as "thereafter," "then," "next," etc. are not intended to limit the order of the steps; these words are simply used to guide the reader through the description of the methods.
In addition, as used herein, "or" as used in a listing of items beginning with "at least one" indicates a separate listing, such that a listing of, for example, "A, B or at least one of C" means a or B or C, or AB or AC or BC, or ABC (i.e., a and B and C). Furthermore, the word "exemplary" does not mean that the described example is preferred or better than other examples.
It should also be noted that in the apparatus and method of the present invention, the components or steps may be disassembled and/or reassembled. These decompositions and/or recombinations are to be regarded as equivalents of the present invention.
It will be understood by those of ordinary skill in the art that all or any portion of the methods and apparatus of the present disclosure may be implemented in any computing device (including processors, storage media, etc.) or network of computing devices, in hardware, firmware, software, or any combination thereof. The hardware may be implemented with a general purpose processor, a Digital Signal Processor (DSP), an ASIC, a field programmable gate array signal (FPGA) or other Programmable Logic Device (PLD), discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general purpose processor may be a microprocessor, but in the alternative, the processor may be any commercially available processor, controller, microcontroller or state machine. A processor may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. The software may reside in any form of computer readable tangible storage medium. By way of example, and not limitation, such computer-readable tangible storage media can comprise RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other tangible medium that can be used to carry or store desired program code in the form of instructions or data structures and that can be accessed by a computer. Disk, as used herein, includes Compact Disk (CD), laser disk, optical disk, digital Versatile Disk (DVD), floppy disk, and Blu-ray disk.
The intelligent control techniques disclosed herein may also be implemented by running a program or a set of programs on any computing device. The computing device may be a general purpose device as is well known. The disclosed intelligent techniques can also be implemented by simply providing a program product containing program code for implementing the method or apparatus, or by any storage medium having such a program product stored therein.
Various changes, substitutions and alterations to the techniques described herein may be made without departing from the techniques of the teachings as defined by the appended claims. Moreover, the scope of the claims of the present disclosure is not limited to the particular aspects of the process, machine, manufacture, composition of matter, means, methods and acts described above. Processes, machines, manufacture, compositions of matter, means, methods, or acts, presently existing or later to be developed that perform substantially the same function or achieve substantially the same result as the corresponding aspects described herein may be utilized. Accordingly, the appended claims are intended to include within their scope such processes, machines, manufacture, compositions of matter, means, methods, or acts.
The previous description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these aspects will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other aspects without departing from the scope of the invention. Thus, the present invention is not intended to be limited to the aspects shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
The foregoing description has been presented for purposes of illustration and description. Furthermore, the description is not intended to limit embodiments of the invention to the form disclosed herein. While a number of example aspects and embodiments have been discussed above, those of skill in the art will recognize certain variations, modifications, alterations, additions and sub-combinations thereof.
Claims (13)
1. A method for relative positioning between a plurality of nodes, comprising:
obtaining a plurality of possible matching pairs of relative distances and azimuth angles between the plurality of nodes;
converting the azimuth into a unified coordinate system, obtaining a plurality of candidate matching pairs corresponding to the plurality of possible matching pairs;
determining a difference between the similarity of the candidate matching pair between the two nodes and the ideal similarity thereof based on a predetermined error model;
based on the difference, filtering out impossible candidate matching pairs, obtaining a plurality of correct matching pairs of relative distances and azimuth angles among the plurality of nodes, and determining the relative positioning among the plurality of nodes.
2. The relative positioning method of claim 1, wherein said filtering out unlikely candidate matching pairs based on said difference comprises:
comparing a plurality of said differences corresponding to a plurality of candidate matching pairs between two nodes, selecting the candidate matching pair corresponding to the smallest of said differences as the correct matching pair.
3. The relative positioning method of claim 1, wherein the difference between the similarity of the candidate matching pair and its ideal similarity is caused by a relative distance measurement error, a horizontal azimuth measurement error, and a vertical azimuth measurement error, wherein the determining the difference between the similarity of the candidate matching pair and its ideal similarity between the two nodes based on a predetermined error model further comprises:
and adjusting the weights of the relative distance measurement error, the horizontal azimuth angle measurement error and the vertical azimuth angle measurement error in the preset error model.
4. The relative positioning method of claim 2, wherein said obtaining a plurality of possible matching pairs of relative distances and azimuth angles between the plurality of nodes comprises:
performing image recognition on a panoramic image photographed from one of the plurality of nodes, determining other nodes in the panoramic image, and determining azimuth information with the other nodes through the panoramic image;
performing wireless ranging using wireless signals transmitted at the plurality of nodes, determining relative distance information between the plurality of nodes;
sharing node identification information among the plurality of nodes; and
and forming the possible matching pairs by using the azimuth angle information, the relative distance information and the node identification information.
5. The relative positioning method as set forth in claim 4, wherein the unified coordinate system is a geodetic coordinate system determined based on magnetic field sensing.
6. The relative positioning method as set forth in claim 5, wherein the relative distance measurement error is derived from an error of the wireless ranging, a horizontal azimuth angle measurement error is derived from an error of the image recognition and the magnetic field sensing, and a vertical azimuth angle measurement error is derived from an error of the image recognition.
7. A relative positioning apparatus for use between a plurality of nodes, comprising:
a processor; and
a memory configured to store computer program instructions;
wherein, when the computer program instructions are executed by the processor, the processor is to:
obtaining a plurality of possible matching pairs of relative distances and azimuth angles between the plurality of nodes;
converting the azimuth into a unified coordinate system, obtaining a plurality of candidate matching pairs corresponding to the plurality of possible matching pairs;
determining a difference between the similarity of the candidate matching pair between the two nodes and the ideal similarity thereof based on a predetermined error model;
and filtering out candidate matching pairs with the difference larger than a preset threshold value, obtaining a plurality of correct matching pairs of relative distances and azimuth angles among the plurality of nodes, and determining the relative positioning among the plurality of nodes.
8. The relative positioning apparatus of claim 7 wherein the processor compares a plurality of said differences between two nodes corresponding to a plurality of candidate matching pairs, selecting the candidate matching pair corresponding to the smallest of said differences as the correct matching pair.
9. The relative positioning apparatus of claim 7, wherein the difference between the similarity of a candidate matching pair and its ideal similarity is caused by a relative distance measurement error, a horizontal azimuth measurement error and a vertical azimuth measurement error, wherein when the computer program instructions are executed by the processor, the processor adjusts the weights of the relative distance measurement error, the horizontal azimuth measurement error and the vertical azimuth measurement error in the predetermined error model.
10. The relative positioning device of claim 9, further comprising:
a panoramic image acquirer for acquiring a panoramic image photographed from one of the plurality of nodes;
a wireless signal transceiver for transceiving wireless signals between the plurality of nodes;
wherein, when the computer program instructions are executed by the processor, the processor performs image recognition on a panoramic image taken from one of the plurality of nodes, determines other nodes in the panoramic image, and determines azimuth information with the other nodes from the panoramic image;
performing wireless ranging using the wireless signal, determining relative distance information between the plurality of nodes;
sharing node identification information among the plurality of nodes; and
and forming the possible matching pairs by using the azimuth angle information, the relative distance information and the node identification information.
11. The relative positioning device of claim 10, wherein the uniform coordinate system is a geodetic coordinate system determined based on magnetic field sensing.
12. The relative positioning device of claim 11, wherein the relative distance measurement error is derived from an error of the wireless ranging, a horizontal azimuth measurement error is derived from an error of the image recognition and the magnetic field sensing, and a vertical azimuth measurement error is derived from an error of the image recognition.
13. A computer readable storage medium storing computer program instructions, wherein the computer program instructions, when executed by a processor, cause the processor to perform the relative positioning method of any of claims 1 to 6.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711144322.3A CN109799476B (en) | 2017-11-17 | 2017-11-17 | Relative positioning method and device, computer readable storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201711144322.3A CN109799476B (en) | 2017-11-17 | 2017-11-17 | Relative positioning method and device, computer readable storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109799476A CN109799476A (en) | 2019-05-24 |
| CN109799476B true CN109799476B (en) | 2023-04-18 |
Family
ID=66554831
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201711144322.3A Active CN109799476B (en) | 2017-11-17 | 2017-11-17 | Relative positioning method and device, computer readable storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109799476B (en) |
Family Cites Families (11)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP4934441B2 (en) * | 2007-01-22 | 2012-05-16 | 株式会社日立製作所 | Wireless node position estimation method, system, and processing apparatus therefor |
| CN101196561B (en) * | 2007-12-26 | 2012-03-14 | 北京理工大学 | Wireless ranging omnidirectional image combined locating system |
| CN101661098B (en) * | 2009-09-10 | 2011-07-27 | 上海交通大学 | Multi-robot automatic locating system for robot restaurant |
| US8531523B2 (en) * | 2009-12-08 | 2013-09-10 | Trueposition, Inc. | Multi-sensor location and identification |
| US8332174B1 (en) * | 2011-05-23 | 2012-12-11 | NCS Subsea, Inc. | Computer instructions for positioning a wire using sensor information |
| CN102711241B (en) * | 2012-05-30 | 2015-04-22 | 济南大学 | Method for positioning radio self-organized network nodes based on homogeneous coordinate |
| US10588107B2 (en) * | 2012-10-26 | 2020-03-10 | Telefonaktiebolaget Lm Ericsson (Publ) | Methods of positioning in a system comprising measuring nodes with multiple receiving points |
| CN104754696A (en) * | 2013-12-27 | 2015-07-01 | 株式会社理光 | Method and system for finding out equipment with interest match in designated area |
| US20160133230A1 (en) * | 2014-11-11 | 2016-05-12 | Bent Image Lab, Llc | Real-time shared augmented reality experience |
| CN107037403B (en) * | 2016-09-26 | 2020-07-24 | 蔚来汽车有限公司 | Augmented reality-based object relative positioning method |
| CN106405495A (en) * | 2016-09-30 | 2017-02-15 | 上海乐蜗信息科技有限公司 | VR equipment positioning system and positioning method thereof |
-
2017
- 2017-11-17 CN CN201711144322.3A patent/CN109799476B/en active Active
Non-Patent Citations (2)
| Title |
|---|
| 李善仓 ; 张德运 ; 马富海 ; 张克旺 ; .基于分布式加权多维定标的节点自身定位算法.西安交通大学学报.2006,第40卷(第12期),第1388-1392页. * |
| 钱晓明 ; 张浩 ; 王晓勇 ; 武星 ; .基于激光扫描匹配的移动机器人相对定位技术研究.农业机械学报.2016,第47卷(第03期),第14-21页. * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109799476A (en) | 2019-05-24 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| US11704833B2 (en) | Monocular vision tracking method, apparatus and non-transitory computer-readable storage medium | |
| CN110705574B (en) | Positioning method and device, equipment and storage medium | |
| US10339708B2 (en) | Map summarization and localization | |
| US10242458B2 (en) | Registration of range images using virtual gimbal information | |
| US10802147B2 (en) | System and method for concurrent odometry and mapping | |
| WO2021057744A1 (en) | Positioning method and apparatus, and device and storage medium | |
| CN110704563B (en) | Map fusion method and device, equipment and storage medium | |
| US9154919B2 (en) | Localization systems and methods | |
| CN107702714B (en) | Positioning method, device and system | |
| US9103863B2 (en) | Method and apparatus for measuring a location of a terminal using magnetic field | |
| JP2019145085A (en) | Method, device, and computer-readable medium for adjusting point cloud data acquisition trajectory | |
| CN101763632B (en) | Method for demarcating camera and device thereof | |
| CN109724603A (en) | A kind of Indoor Robot air navigation aid based on environmental characteristic detection | |
| KR20140014298A (en) | Planar mapping and tracking for mobile devices | |
| JP2019190974A (en) | Calibration device, calibration method and program | |
| Hashemifar et al. | Augmenting visual SLAM with Wi-Fi sensing for indoor applications | |
| Piciarelli | Visual indoor localization in known environments | |
| US20160239585A1 (en) | 3d modelling system | |
| KR20180015961A (en) | Method of estimating the location of object image-based and apparatus therefor | |
| Wang et al. | Robust component-based localizationin sparse networks | |
| US20180374237A1 (en) | Method, system and apparatus for determining a pose for an object | |
| Ruiz-Ruiz et al. | A multisensor LBS using SIFT-based 3D models | |
| KR100327120B1 (en) | Image Registration Method Using 3D Tracker And Computer Vision For Augmented Reality | |
| Jiang et al. | Indoor localization with a signal tree | |
| CN109799476B (en) | Relative positioning method and device, computer readable 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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |