CN110457417B - Indoor map construction method based on edge detection algorithm, computer storage medium and terminal - Google Patents
Indoor map construction method based on edge detection algorithm, computer storage medium and terminal Download PDFInfo
- Publication number
- CN110457417B CN110457417B CN201910713142.5A CN201910713142A CN110457417B CN 110457417 B CN110457417 B CN 110457417B CN 201910713142 A CN201910713142 A CN 201910713142A CN 110457417 B CN110457417 B CN 110457417B
- Authority
- CN
- China
- Prior art keywords
- indoor
- positioning
- edge
- detection algorithm
- edge detection
- 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
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/29—Geographical information databases
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Databases & Information Systems (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Remote Sensing (AREA)
- Data Mining & Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Navigation (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention relates to an indoor map construction method, in particular to an indoor map construction method based on an edge detection algorithm, which comprises the following steps: the indoor space is regarded as a two-dimensional image, and is divided into grids with proper sizes according to a certain interval to be used as pixel points of the image; acquiring positioning hotspot information of the indoor environment in a period of time, and converting the positioning hotspot information of the indoor space into an image pixel value of the indoor space for removing random noise; identifying edge information of the indoor space by using an edge detection algorithm, and removing isolated noise points; the invention realizes the accurate and effective construction of the indoor map, thereby realizing the accurate and effective indoor navigation positioning and expanding to various mainstream indoor positioning technical schemes.
Description
Technical Field
The invention belongs to the field of indoor map construction, and particularly relates to an indoor map construction method based on an edge detection algorithm, a computer storage medium and a terminal.
Background
With popularization of mobile internet and rising application of internet of things, technologies such as cloud computing, big data, robots and intelligent sensing slowly enter the visual field of people, and a positioning technology is one of important technologies of a sensing layer and plays a significant role. The development of the outdoor positioning technology is mature depending on the satellite positioning technologies such as GPS, Beidou and the like. However, due to the fact that satellite communication is not smooth due to signal shielding and serious reflection inside 'urban canyons' and large buildings, indoor navigation requirements become more urgent, especially in indoor large shopping malls, museums, libraries, museums and other scenes.
The existing developed map mainly focuses on public areas such as urban roads and the like, and does not income indoor scenes. In order to implement indoor navigation, indoor map data needs to be constructed. Most indoor navigation and positioning technologies are based on the combination of GPS and WSN, or realize indoor positioning based on Beidou satellite. And article storage position changes often appear under indoor environment, for example, the position of the material changes along with the production process in the factory, the position of the booth changes caused by sales promotion activities in large-scale shopping malls, etc., and the indoor map data often still before the position changes, which leads to inaccurate or even invalid indoor navigation positioning, and the indoor map has low application degree.
Disclosure of Invention
The invention provides the method for constructing the indoor map based on the edge detection algorithm, which overcomes the defects of the prior art, and realizes the accurate and effective construction of the indoor map, thereby realizing the accurate and effective indoor navigation positioning.
The technical scheme provided by the invention is as follows:
the method for constructing the indoor map based on the edge detection algorithm comprises the following steps:
the method comprises the following steps:
the method comprises the following steps: the indoor space is regarded as a two-dimensional image, and is divided into grids with proper sizes according to a certain interval to be used as pixel points of the image;
step two: acquiring positioning hotspot information of the indoor environment in a period of time, and converting the positioning hotspot information of the indoor space into an image pixel value of the indoor space for removing random noise;
step three: identifying edge information of the indoor space by using an edge detection algorithm, and removing isolated noise points;
step four: an indoor map is determined.
Preferably, in the second step, positioning hotspot information in the indoor environment in a near period of time is acquired through an indoor positioning technology.
Preferably, in the second step, converting the positioning hotspot information of the indoor space into an image pixel value of the indoor space specifically includes: the method comprises the steps of obtaining a positioning result in the indoor environment in a recent period of time, calculating a quantity value of positioning information falling on each grid, and converting the quantity value into an image pixel value.
Preferably, the image pixel value is [0,255 [ ]]Within the range, the calculation formula is as follows:
wherein, in the step (A),
is the maximum value of the current data,
is the current data minimum, X is any value in the assumed current data, and y is the normalized mapped value.
Preferably, in the second step, the removing of the random noise specifically includes: the method automatically removes the interference of background noise by using a fuzzy mathematics-based method, and removes random noise by using median filtering and Gaussian smooth filtering.
Preferably, the method for automatically removing the interference of the background noise by using the fuzzy mathematics-based method specifically comprises the following steps: the image is divided into a background area and a target area (an area needing edge detection), and the target area is automatically extracted according to the maximum membership principle in fuzzy mathematics.
Preferably, in the third step, using an edge detection algorithm, the identifying the edge information of the indoor space specifically includes: carrying out edge tracking by using a Canny edge detection algorithm to obtain continuous edges, taking extreme points with pixel values larger than the optimal high threshold value in the gradient amplitude image as edge points, searching pixel points marked as the extreme points in eight neighborhoods around each edge point, marking the points with the pixel values larger than the low threshold value as the edge points, finally, taking a 3 x 3 area with the edge points as the center for each edge point, solving the total number m of the edge points in the area, and if m =1, removing the edge points as isolated noise points to obtain a final edge image so as to obtain edge positions and determine an indoor map.
Preferably, the method for acquiring the optimal high threshold is as follows: the automatic threshold selection based on the maximum entropy is realized by firstly solving the gradients in the width direction and the height direction of each pixel point after filtering to obtain a gradient amplitude image, utilizing a non-maximum value inhibition method to label the grids in the gradient image, and calculating the maximum entropy according to the gradient amplitude image and the labeled image to obtain the optimal threshold.
Preferably, before the first step, a positioning result storing step is performed: and acquiring data, positioning an unknown label representing new positioning hotspot information by using an indoor positioning algorithm, storing a positioning result in a MySQL database, and updating the positioning hotspot information.
Preferably, before the step of storing the positioning result, an indoor positioning deployment scheme is determined according to environmental characteristics, and a communication module is used to deploy an indoor positioning environment.
A computer-readable storage medium, on which a computer program is stored, wherein the computer program, when executed, implements the above-mentioned method for indoor map construction based on an edge detection algorithm.
A terminal comprising a processor and a memory, the memory storing a computer program, characterized in that: the processor, when executing the computer program, implements the above-described method for indoor map construction based on the edge detection algorithm.
The invention has the beneficial effects that: the method for constructing the indoor map based on the edge detection algorithm comprises the steps of taking an indoor space as a two-dimensional image, dividing the indoor space into grids with proper sizes according to a certain interval, and taking the grids as pixel points of the image; acquiring positioning hotspot information of the indoor environment in a period of time, and converting the positioning hotspot information of the indoor space into an image pixel value of the indoor space for removing random noise; identifying edge information of the indoor space by using an edge detection algorithm, and removing isolated noise points; the invention realizes the accurate and effective construction of the indoor map, thereby realizing the accurate and effective indoor navigation positioning and expanding to various mainstream indoor positioning technical schemes.
Drawings
Fig. 1 is a flowchart of an indoor map construction method based on an edge detection algorithm according to the present invention.
Fig. 2 is a flow chart of an indoor positioning algorithm.
Detailed Description
In order to make the technical solutions of the present invention better understood, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
Referring to fig. 1, an embodiment of the present invention provides a method for constructing an indoor map based on an edge detection algorithm, including the following steps:
s1: determining a deployment scheme of indoor positioning according to environmental characteristics, such as using indoor positioning technologies like bluetooth, WiFi, WLAN (Wireless Local Area Networks) to deploy indoor positioning environment.
S2: and testing the connectivity of the network, and obtaining parameters (environment complexity and signal strength) required by a subsequent indoor positioning algorithm through maximum likelihood estimation.
The maximum likelihood estimation method is another method of finding an estimate; the method is a method which is still widely applied in the past, and is a statistical method established on the basis of the maximum likelihood principle, and the intuitive idea of the maximum likelihood principle is as follows: there are several possible outcomes a, B, C, … for a random trial. If the result a appears in one test, it is generally considered that the test conditions are favorable for the occurrence of a, i.e., the probability of the occurrence of a is high.
S3: and collecting data, positioning the unknown label by using an indoor positioning algorithm, and storing a positioning result into a MySQL (MySQL _ num _ rows) database.
S4: the whole indoor environment is regarded as a two-dimensional image, such as 1 meter and 0.5 meter, the indoor space is divided into grids with proper sizes according to a certain interval, and each grid is used as a pixel point.
S5: obtaining the positioning results of the indoor environment in the recent period of time, calculating the number of the positioning results falling on each grid, and converting the value into an image pixel value, namely converting into a range of [0,255], wherein the calculation formula is as follows:
the image pixel value is a value given by a computer when the original image is digitized, and represents average luminance information of a certain small block of the original, or average reflection (transmission) density information of the small block. When a digital image is converted into a halftone image, the dot area ratio (dot percentage) has a direct relationship with the pixel value (gray value) of the digital image, i.e., the dots represent the average brightness information of a certain small square of the original document by their size.
S6: the method based on Fuzzy mathematics (Fuzzy mathematics) is used for automatically removing the interference of background noise, dividing the image into a background area and a target area (an area needing to detect edges), and automatically extracting the target area according to the maximum membership principle in the Fuzzy mathematics.
S7: and removing random noise by median filtering and Gaussian smoothing filtering. The median filtering is a nonlinear signal processing technology which is based on the ordering statistical theory and can effectively inhibit noise, and the basic principle of the median filtering is to replace the value of one point in a digital image or a digital sequence by the median of all point values in a neighborhood of the point, so that the surrounding pixel values are close to the true values, and isolated noise points are eliminated. The method is that a two-dimensional sliding template with a certain structure is used to sort the pixels in the template according to the size of the pixel value, and a monotonously rising (or falling) two-dimensional data sequence is generated; the gaussian smoothing filter is a linear smoothing filter, is suitable for eliminating gaussian noise, and is widely applied to a noise reduction process of image processing.
S8: the automatic threshold selection based on the maximum entropy is realized by firstly solving the gradients in the width direction and the height direction of each pixel point after filtering to obtain a gradient amplitude image, utilizing a non-maximum value inhibition method to label the grids in the gradient image, and calculating the maximum entropy according to the gradient amplitude image and the labeled image to obtain the optimal threshold.
S9: performing edge tracking by using a Canny edge detection algorithm to obtain continuous edges, taking extreme points with pixel values larger than the optimal high threshold value in the gradient amplitude image as edge points, searching pixel points marked as the extreme points in eight neighborhoods around each edge point, and marking the points with the pixel values larger than the low threshold value as the edge points; the Canny edge detection algorithm is divided into smoothing images by applying Gaussian filtering, and aims to remove noise; finding intensity gradients (intensity gradients) of the image; applying non-maximum inhibition (non-maximum suppression) technology to eliminate edge false detection; applying a dual threshold approach to determine possible (potential) boundaries; the boundaries are tracked using a hysteresis technique.
S10: taking a 3 x 3 area with the edge point as the center for each edge point, calculating the total number m of the edge points in the area, and if m =1, removing the edge point as an isolated noise point to obtain a final edge image so as to obtain an edge position and determine an indoor map; and repeating the steps S3 to S9 to update the indoor map in real time.
In the above, each step is prepared for the subsequent step.
S1 and S2 determine a deployment scheme according to a specific indoor positioning environment, so that the positioning accuracy is improved and the deployment cost is reduced;
S3-S9 are methods for updating indoor maps based on edge detection algorithms;
s10 obtains a final edge image to obtain the edge position.
As shown in fig. 2, the indoor positioning algorithm specifically includes:
the method comprises the following steps: and a calculation module for receiving the preprocessed data and converting the RSSI value (the received signal strength indication value) into a distance.
Step two: and judging whether the label is positioned at the edge of the indoor space.
Step three: and the position at the edge of the indoor space is as follows: and selecting 3 base stations with the maximum RSSI.
The three-point positioning algorithm based on the RSSI is to solve the coordinates of the unknown points by knowing the coordinates of the three points and the RSSI signal values from the unknown points to the three points.
Step four: and solving N estimated positions based on trilateral and projection equal proportion algorithm.
In a positioning algorithm based on ranging, trilateration is a relatively simple algorithm, and the algorithm principle is as follows: three base stations A, B and C which are not collinear and an unknown terminal D are arranged on the plane, the distances from the three base stations to the terminal D are measured to be R1, R2 and R3 respectively, three intersected circles can be drawn by taking the coordinates of the three base stations as the circle center and the distances from the three base stations to the unknown terminal as the radius, and as shown in the lower graph of the figure, the unknown node coordinates are the intersection points of the three circles.
Step five: and estimating the clustering center of the position by using a CFDP algorithm.
The clustering algorithm comprises the following steps: location-based clustering (kmeans \ kmodes \ kmedians), hierarchical clustering (aggregative \ birch), density-based clustering (DBSCAN), model-based clustering (GMM \ neural network-based algorithm).
Step six: and calculating the distances from the N estimated positions to the clustering center.
Step six: and solving N weighted centroids according to the distance and the clustering center.
Step seven: and calculating the distances from the N weighted centroids to the clustering center, and then calculating the weighted centroids.
Step eight: and carrying out smooth denoising processing on the estimated coordinates by using a filtering algorithm.
Step nine: and putting the data into a database operation module through a visual terminal.
By the method for constructing the indoor map based on the edge detection algorithm, the accurate and effective indoor map can be constructed, the map is provided for accurate and effective indoor navigation positioning, and the method can be expanded to various mainstream indoor positioning technical schemes.
The method for building an indoor map based on an edge detection algorithm according to the present embodiment is implemented by a computer program stored in a storage medium, and specifically, the storage may include a mass storage for data or instructions, for example, but not limited to, a Hard Disk Drive (HDD), a floppy Disk Drive, a flash memory, an optical Disk, a magneto-optical Disk, a magnetic tape, or a Universal Serial Bus (USB) Drive, or a combination of two or more of these drives; memory may include removable or non-removable (or fixed) media, where appropriate; where appropriate, the memory may be internal or external to the data processing apparatus; in a particular embodiment, the memory is a non-volatile solid-state memory; in a particular embodiment, the memory includes Read Only Memory (ROM); where appropriate, the ROM may be mask-programmed ROM, Programmable ROM (PROM), Erasable PROM (EPROM), Electrically Erasable PROM (EEPROM), electrically rewritable ROM (EAROM), or flash memory or a combination of two or more of these.
The method for constructing an indoor map based on an edge detection algorithm according to the present embodiment is implemented by a terminal running a computer program, and more specifically, by a processor in the terminal running a computer program in a storage medium, where the processor may include a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or may be configured to implement one or more Integrated circuits according to an embodiment of the present invention, where an element of the present invention is a program or a code segment used for executing a required task; the program or code segments may be stored in a machine-readable medium or a "machine-readable medium" conveyed via a data signal embodied in a carrier wave over a transmission medium or communication link may include any medium that is capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, ROM, flash memory, erasable ROM (erom), floppy disks, CD-ROMs, optical disks, hard disks, fiber-optic media, Radio Frequency (RF) links, and so forth, wherein the code segments may be downloaded via a computer network, such as the internet, an intranet, and so forth.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the protection scope of the present invention, although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications or equivalent substitutions can be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.
Claims (10)
1. The method for constructing the indoor map based on the edge detection algorithm is characterized by comprising the following steps of:
the method comprises the following steps: the indoor space is regarded as a two-dimensional image, and is divided into grids with fixed sizes according to a certain interval to be used as pixel points of the image;
step two: acquiring positioning hotspot information of the indoor environment in a period of time, and converting the positioning hotspot information of the indoor space into an image pixel value of the indoor space for removing random noise;
step three: identifying edge information of the indoor space by using an edge detection algorithm, and removing isolated noise points;
step four: the map of the interior of the room is determined,
in the second step, converting the positioning hotspot information of the indoor space into an image pixel value of the indoor space specifically comprises: obtaining the positioning result in the indoor environment in a period of time, calculating the quantity value of the positioning information falling on each grid, converting the quantity value into an image pixel value,
the image pixel value is [0,255%]Within the range, the calculation formula is as follows:
wherein x ismaxIs the maximum value of the data in the period, xminIs the minimum value of the data in the period of time, X is any value in the data in the assumed period of time, and y is the normalized mapped value.
2. The method of indoor mapping based on edge detection algorithm of claim 1, wherein: and in the second step, positioning hotspot information in the indoor environment in a period of time is acquired through an indoor positioning technology.
3. The method of indoor mapping based on edge detection algorithm of claim 1, wherein: in the second step, the step of removing random noise specifically comprises the following steps: the method automatically removes the interference of background noise by using a fuzzy mathematics-based method, and removes random noise by using median filtering and Gaussian smooth filtering.
4. The method of indoor mapping based on the edge detection algorithm of claim 3, wherein: the method for automatically removing the interference of the background noise by using the fuzzy mathematics-based method specifically comprises the following steps: dividing the image into a background area and a target area, and automatically extracting the target area according to the maximum membership principle in fuzzy mathematics.
5. The method of indoor mapping based on the edge detection algorithm of claim 3, wherein: in the third step, using an edge detection algorithm, identifying edge information of the indoor space specifically includes: carrying out edge tracking by using a Canny edge detection algorithm to obtain continuous edges, taking extreme points with pixel values larger than the optimal high threshold value in the gradient amplitude image as edge points, searching pixel points marked as the extreme points in eight neighborhoods around each edge point, marking the points with the pixel values larger than the low threshold value as the edge points, finally, taking a 3 x 3 area with the edge points as the center for each edge point, solving the total number m of the edge points in the area, and if m is 1, removing the edge points as isolated noise points to obtain a final edge image so as to obtain edge positions and determine an indoor map.
6. The method of indoor mapping based on the edge detection algorithm of claim 5, wherein: the method for acquiring the optimal high threshold value comprises the following steps: the automatic threshold selection based on the maximum entropy is realized by firstly solving the gradients in the width direction and the height direction of each pixel point after filtering to obtain a gradient amplitude image, utilizing a non-maximum value inhibition method to label the grids in the gradient image, and calculating the maximum entropy according to the gradient amplitude image and the labeled image to obtain the optimal threshold.
7. The method of indoor mapping based on edge detection algorithm of claim 1, wherein: before the first step, a positioning result storage step is executed: and acquiring data, positioning an unknown label representing new positioning hotspot information by using an indoor positioning algorithm, storing a positioning result in a MySQL database, and updating the positioning hotspot information.
8. The method of indoor mapping based on the edge detection algorithm of claim 7, wherein: before the step of storing the positioning result is executed, an indoor positioning deployment scheme is determined according to environmental characteristics, and a communication module is used for deploying indoor positioning environment.
9. A computer-readable storage medium, on which a computer program is stored, which, when executed, implements a method of indoor mapping based on an edge detection algorithm as claimed in any one of claims 1 to 8.
10. A terminal comprising a processor and a memory, the memory storing a computer program, characterized in that: the processor, when executing the computer program, implements a method of indoor mapping based on an edge detection algorithm as claimed in any one of claims 1 to 8.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910713142.5A CN110457417B (en) | 2019-08-02 | 2019-08-02 | Indoor map construction method based on edge detection algorithm, computer storage medium and terminal |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910713142.5A CN110457417B (en) | 2019-08-02 | 2019-08-02 | Indoor map construction method based on edge detection algorithm, computer storage medium and terminal |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110457417A CN110457417A (en) | 2019-11-15 |
| CN110457417B true CN110457417B (en) | 2022-01-11 |
Family
ID=68484789
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910713142.5A Active CN110457417B (en) | 2019-08-02 | 2019-08-02 | Indoor map construction method based on edge detection algorithm, computer storage medium and terminal |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110457417B (en) |
Families Citing this family (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3832598A1 (en) * | 2019-12-05 | 2021-06-09 | Aptiv Technologies Limited | Methods and systems for determining an initial ego-pose for initialization of self-localization |
| CN115273039B (en) * | 2022-09-29 | 2023-01-10 | 中汽数据(天津)有限公司 | Small obstacle detection method based on camera |
| CN116167091B (en) * | 2023-04-24 | 2023-06-30 | 南京麦堤微林信息科技有限公司 | Mapping data encryption method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105631773A (en) * | 2014-11-24 | 2016-06-01 | 三星电子株式会社 | Electronic device and method for providing map service |
| CN109949414A (en) * | 2019-01-31 | 2019-06-28 | 顺丰科技有限公司 | The construction method and device of indoor map |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US10311752B2 (en) * | 2017-02-03 | 2019-06-04 | Honeywell International Inc. | Compressed edge map representation for image aided navigation |
-
2019
- 2019-08-02 CN CN201910713142.5A patent/CN110457417B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105631773A (en) * | 2014-11-24 | 2016-06-01 | 三星电子株式会社 | Electronic device and method for providing map service |
| CN109949414A (en) * | 2019-01-31 | 2019-06-28 | 顺丰科技有限公司 | The construction method and device of indoor map |
Non-Patent Citations (1)
| Title |
|---|
| 单目视觉室内无人飞行器运动目标测距技术研究;邓孝逸等;《计算机与现代化》;20130315;第152-155、164页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110457417A (en) | 2019-11-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110457417B (en) | Indoor map construction method based on edge detection algorithm, computer storage medium and terminal | |
| US20210333108A1 (en) | Path Planning Method And Device And Mobile Device | |
| WO2021143778A1 (en) | Positioning method based on laser radar | |
| CN110557716B (en) | Indoor positioning method based on lognormal model | |
| CN106454747B (en) | Wireless positioning method of mobile phone terminal | |
| US20100305851A1 (en) | Device and method for updating cartographic data | |
| CN108830876B (en) | Water area contour extraction and area detection method | |
| Chandel et al. | InLoc: An end-to-end robust indoor localization and routing solution using mobile phones and BLE beacons | |
| JP2008005476A (en) | Method of estimating location of wireless terminal | |
| WO2018112825A1 (en) | Positioning method based on wi-fi access point, and device | |
| Du et al. | CRCLoc: A crowdsourcing-based radio map construction method for WiFi fingerprinting localization | |
| WO2021092873A1 (en) | Method for determining floor of terminal device | |
| CN111698774B (en) | Indoor positioning method and device based on multi-source information fusion | |
| CN111709988B (en) | Method and device for determining characteristic information of object, electronic equipment and storage medium | |
| CN111475746B (en) | Point-of-interest mining method, device, computer equipment and storage medium | |
| CN113596989A (en) | Indoor positioning method and system for intelligent workshop | |
| CN114722944A (en) | Point cloud precision determination method, electronic device and computer storage medium | |
| CN117128947A (en) | Grid map generation method and server | |
| CN111578938B (en) | Target object positioning method and device | |
| CN111241881A (en) | Method, apparatus, device and medium for region identification | |
| CN109784225B (en) | Geographical space mode identification method based on reachable space | |
| CN112862921B (en) | Power grid distribution image drawing method | |
| JP2013008310A (en) | Three-dimensional map creation device and its window area detection device | |
| Ashqer et al. | Parking lot space detection based on image processing | |
| CN114286282A (en) | Fingerprint positioning method based on WiFi RSS data of mobile phone and dimension reduction algorithm |
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 |