CN111935641B - Indoor self-positioning realization method, intelligent mobile device and storage medium - Google Patents
Indoor self-positioning realization method, intelligent mobile device and storage medium Download PDFInfo
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- CN111935641B CN111935641B CN202010817351.7A CN202010817351A CN111935641B CN 111935641 B CN111935641 B CN 111935641B CN 202010817351 A CN202010817351 A CN 202010817351A CN 111935641 B CN111935641 B CN 111935641B
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- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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Abstract
The invention provides an indoor self-positioning realization method, intelligent mobile equipment and a storage medium, wherein the method comprises the following steps: searching a WIFI signal at the position of the WIFI signal to acquire WIFI scanning data; screening candidate feature points matched with WIFI scanning data according to the WIFI fingerprint feature library, and scanning to obtain environment sensing data around the position where the candidate feature points are located; respectively matching the candidate characteristic points with the environmental sensing data to obtain corresponding similarity; and determining the self positioning result according to the floor map name and the sampling position corresponding to the candidate characteristic point with the maximum similarity. According to the method, correlation matching is carried out based on the WIFI fingerprint feature library and the environment sensing data, the floor and the position of each intelligent mobile device are positioned according to matching similarity, and the accuracy of indoor self-positioning is prompted.
Description
Technical Field
The invention relates to the technical field of signal processing, in particular to an indoor self-positioning realization method, intelligent mobile equipment and a storage medium.
Background
The existing indoor positioning scheme of the intelligent mobile device does not need to additionally lay equipment because a general market is covered with Wifi (WIreless-Fidelity), and therefore indoor positioning is mainly achieved through Wifi (WIreless-Fidelity).
However, Wifi positioning requires fingerprint collection, which consumes a lot of human resources, and Wifi fingerprints have timeliness, which is about 6 months. Therefore, the Wifi fingerprint may slowly fail, resulting in a decrease in positioning accuracy. In the existing mode of positioning according to the WIFI strength, as historical WIFI lists and signal strengths which are not updated are likely to change greatly compared with actual WIFI lists and signal strengths, the cost for maintaining wireless Access Points (APs) is high, and the positioning of coordinates of each AP is difficult.
In view of the foregoing, the prior art is yet to be further improved.
Disclosure of Invention
The invention aims to provide an indoor self-positioning realization method, intelligent mobile equipment and a storage medium, which are used for realizing correlation matching based on a WIFI fingerprint feature library and environment sensing data, positioning the specific position of each intelligent mobile equipment according to a matching result and prompting the accuracy of indoor self-positioning.
The technical scheme provided by the invention is as follows:
the invention provides an indoor self-positioning realization method, which comprises the following steps:
searching a WIFI signal at the position of the WIFI signal to acquire WIFI scanning data;
screening out candidate feature points matched with the WIFI scanning data according to a WIFI fingerprint feature library, and scanning to obtain environment sensing data around the position where the candidate feature points are located;
respectively matching the candidate characteristic points with the environment sensing data to obtain corresponding similarity;
and determining the self positioning result according to the floor map name and the sampling position corresponding to the candidate feature point with the maximum similarity.
Further, the method comprises the following steps before searching for the WIFI signal at the position of the device to acquire the WIFI scanning data:
collecting WIFI position fingerprint points in different directions at different sampling position points, and establishing a WIFI fingerprint feature library; the WIFI position fingerprint points comprise corresponding relations between sampling positions and fingerprint features, and the fingerprint features comprise environment acquisition data, floor map names, MAC address lists and signal intensity lists.
Further, screening out candidate feature points matched with the WIFI scanning data according to a WIFI fingerprint feature library, and scanning and acquiring environment sensing data around the position where the candidate feature points are located comprises the following steps:
acquiring the scanning signal intensity and the scanning MAC address of a WIFI signal source connected with the intelligent mobile equipment at the current moment according to the WIFI scanning data;
screening out a plurality of candidate feature points according to the matching of the scanning signal intensity and the scanning MAC address with the WIFI fingerprint feature library;
scanning the current surrounding environment of the environment sensing sensor through the environment sensing sensor, and acquiring the environment sensing data; the environmental sensing data comprises visual observation data and laser observation data;
and the candidate characteristic points are WIFI position fingerprint points matched with the scanning signal intensity and the scanning MAC address.
Further, the step of respectively matching the candidate feature points with the environmental sensing data to obtain corresponding similarities includes:
extracting environmental features in the environmental sensing data;
and respectively matching the environmental characteristics with preset characteristics corresponding to the candidate characteristic points to obtain corresponding similarity.
Further, the step of respectively matching the candidate feature points with the environmental sensing data to obtain corresponding similarities includes:
matching the laser observation data with laser sampling data to which each candidate characteristic point belongs respectively according to a point cloud registration algorithm to obtain corresponding similarity; or the like, or, alternatively,
and matching the visual observation data with the image sampling data to which each candidate feature point belongs respectively according to an image recognition algorithm to obtain corresponding similarity.
The present invention also provides an intelligent mobile device, comprising:
the searching module is used for searching the WIFI signal at the position of the searching module to acquire WIFI scanning data;
the screening module is used for screening out candidate feature points matched with the WIFI scanning data according to a WIFI fingerprint feature library;
the scanning module is used for scanning and acquiring environmental sensing data around the position of the scanning module;
the matching module is used for respectively matching the candidate characteristic points with the environment sensing data to obtain corresponding similarity;
and the positioning processing module is used for determining a self positioning result according to the floor map name and the sampling position corresponding to the candidate feature point with the maximum similarity.
Further, the method also comprises the following steps:
the acquisition module is used for acquiring WIFI position fingerprint points in different directions at different sampling position points; the WIFI position fingerprint points comprise corresponding relations between sampling positions and fingerprint features, and the fingerprint features comprise environment acquisition data, floor map names, MAC address lists and signal intensity lists
And the database building module is used for building a WIFI fingerprint feature database according to the WIFI position fingerprint points.
Further, the screening module comprises:
the extraction unit is used for acquiring the scanning signal intensity and the scanning MAC address of the WIFI signal source connected with the intelligent mobile equipment at the current moment according to the WIFI scanning data;
the matching unit is used for screening out a plurality of candidate feature points according to the matching of the scanning signal intensity and the scanning MAC address with the WIFI fingerprint feature library;
the candidate characteristic points are WIFI position fingerprint points matched with the scanning signal intensity and the scanning MAC address;
the scanning module includes:
the laser scanning unit is used for scanning the current surrounding environment of the laser scanning unit through the laser sensor and acquiring and obtaining laser observation data;
the visual scanning unit is used for scanning the current surrounding environment of the visual scanning unit through a visual sensor and acquiring visual observation data;
wherein the environmental sensing data comprises visual observation data and laser observation data.
Further, the matching module comprises:
an extraction unit configured to extract an environmental feature in the environmental sensing data;
the first matching unit is used for matching the environmental features with preset features corresponding to the candidate feature points respectively to obtain corresponding similarity;
the second matching unit is used for matching the laser observation data with the laser sampling data to which each candidate feature point belongs respectively to obtain corresponding similarity according to a point cloud registration algorithm;
and the third matching unit is used for matching the visual observation data with the image sampling data to which each candidate feature point belongs respectively to acquire corresponding similarity according to an image recognition algorithm.
The present invention also provides a storage medium, which stores at least one instruction that is loaded and executed by a processor to implement the operations performed by the method for implementing indoor self-positioning.
By the indoor self-positioning realization method, the intelligent mobile equipment and the storage medium, correlation matching can be performed on the basis of the WIFI fingerprint feature library and the environment sensing data, specific positions of the intelligent mobile equipment are positioned according to matching results, and the indoor self-positioning accuracy is prompted.
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The above features, technical features, advantages and implementations of an indoor self-positioning implementation method, a smart mobile device and a storage medium will be further described in the following detailed description of preferred embodiments with reference to the accompanying drawings.
FIG. 1 is a flow chart of one embodiment of a method of implementing indoor self-localization of the present invention;
FIG. 2 is a flow chart of another embodiment of an indoor self-positioning implementation method of the invention;
fig. 3 is a flow chart of another embodiment of an implementation method of indoor self-positioning of the present invention.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. However, it will be apparent to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It will be understood that the terms "comprises" and/or "comprising," when used in this specification and the appended claims, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.
For the sake of simplicity, the drawings only schematically show the parts relevant to the present invention, and they do not represent the actual structure as a product. In addition, in order to make the drawings concise and understandable, components having the same structure or function in some of the drawings are only schematically illustrated or only labeled. In this document, "a" means not only "only one of this but also a case of" more than one ".
It should be further understood that the term "and/or" as used in this specification and the appended claims refers to any and all possible combinations of one or more of the associated listed items and includes such combinations.
In addition, in the description of the present application, the terms "first," "second," and the like are used only for distinguishing the description, and are not intended to indicate or imply relative importance.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following description will be made with reference to the accompanying drawings. It is obvious that the drawings in the following description are only some examples of the invention, and that for a person skilled in the art, other drawings and embodiments can be derived from them without inventive effort.
One embodiment of the present invention, as shown in fig. 1, is an implementation method of indoor self-positioning, including:
s100, searching for a WIFI signal at the position of the WIFI signal to acquire WIFI scanning data;
specifically, the preset space of a hospital, an office building, a market and the like is an activity area of the intelligent mobile device, and a WIFI topological network is often laid in the preset space, so that a user can freely use the WIFI network to perform entertainment or communication in the preset space. The smart mobile device is not limited to types including a mobile robot, an unmanned vehicle, and the like. The intelligent mobile device is provided with the WIFI communication module, and can search the WIFI wireless signal of the position of the intelligent mobile device through the WIFI communication module, and then acquire WIFI scanning data.
S200, screening out candidate feature points matched with WIFI scanning data according to a WIFI fingerprint feature library, and scanning to acquire environmental sensing data around the position where the WIFI fingerprint feature point is located;
specifically, after the intelligent mobile device acquires the WIFI scanning data, the WIFI scanning data are matched with the WIFI fingerprint feature library, and candidate feature points matched with the WIFI scanning data are found.
The intelligent mobile device can firstly scan and acquire the environment sensing data of the intelligent mobile device for the surrounding environment of the position, and after the candidate feature points are acquired through matching, the intelligent mobile device can perform matching evaluation according to the candidate feature points and the environment sensing data to acquire the corresponding similarity. Of course, the intelligent mobile device may also automatically detect and acquire the environmental sensing data of the self about the surrounding environment of the location after the candidate feature points are acquired by matching. The sequence of the intelligent mobile device acquiring the environment sensing data and acquiring the candidate feature points is not limited herein, and is within the protection scope of the present invention.
S300, respectively matching the candidate feature points with the environmental sensing data to obtain corresponding similarity;
s400, determining the self positioning result according to the floor map name and the sampling position corresponding to the candidate feature point with the maximum similarity.
Specifically, the positioning result of the smart mobile device includes the floor and the location of the smart mobile device. The name of the floor map name is named by the floor number, so that the corresponding floor environment map and the floor number where the floor environment map is located can be obtained by finding the name of the floor map.
After the intelligent mobile device obtains the candidate feature points and the environment sensing data in the above manner, the environment sensing data is respectively matched with each candidate feature point to obtain corresponding similarity, so that the floor to which the floor map name corresponding to the candidate feature point with the maximum similarity belongs is the current floor of the intelligent mobile device, and under the condition that the floor to which the intelligent mobile device belongs is determined, the sampling position corresponding to the candidate feature point with the maximum similarity is the position of the current floor of the intelligent mobile device.
In the embodiment, the method and the device screen out the candidate feature points by combining with the pre-established WIFI fingerprint feature library, can determine the approximate position range of the intelligent mobile device in the field according to the candidate feature points, then perform similarity matching on the environment sensing data and the candidate feature points by utilizing the environment sensing data sensed by the intelligent mobile device, determine the floor map name and the sampling position corresponding to the candidate feature point with the maximum similarity, and determine the positioning result, thereby realizing the indoor self-positioning of the intelligent mobile device. Secondly, the problem of positioning loss caused by network disconnection or disconnection of the WIFI network in the positioning process completely depending on the WIFI network in the prior art is avoided, the indoor positioning result can be more accurate, and the indoor positioning accuracy is improved. Finally, the approximate position where the intelligent mobile device in the site is located is identified through the assistance of the existing indoor WIFI network to realize preliminary screening, so that the positioning matching range can be greatly reduced, and the overall positioning efficiency of the intelligent mobile device in the site is improved.
One embodiment of the present invention, as shown in fig. 2, is an implementation method of indoor self-positioning, including:
s010 collects WIFI position fingerprint points in different directions at different sampling position points, and establishes a WIFI fingerprint feature library; the WIFI position fingerprint point comprises a sampling position and a corresponding relation between fingerprint characteristics, wherein the fingerprint characteristics comprise environment acquisition data, a floor map name, an MAC address list and a signal intensity list;
specifically, the WIFI fingerprint feature library comprises a plurality of WIFI position fingerprint points, the WIFI position fingerprint points can be automatically acquired at different sampling position points by the intelligent mobile device at intervals, and the intelligent mobile device acquires the WIFI position fingerprint points in different directions at each sampling position point. Certainly, the staff can also move by themselves and manually acquire the WIFI position fingerprint points at different sampling position points at intervals, and the staff acquires the WIFI position fingerprint points in different directions at each sampling position point. The environment acquisition data comprises laser sampling data and image sampling data. In this embodiment, the WIFI location fingerprint points may be collected by dotting, or may be collected based on a random path. Preferably, WIFI position fingerprint points are acquired based on a random path, namely the WIFI position fingerprint points do not need to be acquired according to a set route, so that the acquisition efficiency of the WIFI position fingerprint points is improved.
The WIFI position fingerprint point is used for associating sampling positions in the actual environment with certain fingerprint characteristics, and one sampling position corresponds to one unique fingerprint characteristic. WIFI location fingerprint points may be of various types, and any fingerprint feature may be used as a WIFI location fingerprint point. For example, the signal strength (RSSI) of a plurality of WIFI signal sources detected at a certain location may be used as a fingerprint feature, the MAC address names corresponding to a plurality of WIFI signal sources detected at a certain location may also be used as a fingerprint feature, and the environment acquisition data detected at a certain location may also be used as a fingerprint feature. For example, the laser sample data or the image sample data detected at a certain position may be used as a fingerprint feature.
For example, the list of MAC addresses included in the WIFI location fingerprint generated by the WIFI data collected at the current time t may be represented as: macst ═ mac0, mac1, … …, macn }. In addition, the list of signal strengths included in the WIFI location fingerprint generated by collecting WIFI data at the current time t may be denoted as rssi ═ rssi0, rssi1, … …, rssin }. Wherein MAC0, MAC1 and … … macn represent MAC addresses of the WIFI signal sources, and rsi 0, rsi 1. For example, a laser point cloud formed by laser sampling data of the second floor elevator hall, which is obtained by scanning the second floor elevator hall by a laser radar, can be used as the fingerprint feature.
S100, searching for a WIFI signal at the position of the WIFI signal to acquire WIFI scanning data;
s210, acquiring the scanning signal intensity and the scanning MAC address of a WIFI signal source connected with the intelligent mobile equipment at the current moment according to the WIFI scanning data;
specifically, the WIFI scanning data are analyzed, a target WIFI signal provided by a WIFI signal source connected to the intelligent mobile device at the current moment can be acquired, and then a scanning MAC address and scanning signal intensity of the connected target WIFI signal are acquired. The prior art is to analyze WIFI data to obtain corresponding signal strength and MAC address, and details thereof are not described here.
S220, screening out a plurality of candidate feature points according to the matching of the scanning signal intensity and the scanning MAC address with the WIFI fingerprint feature library; the candidate characteristic points are WIFI position fingerprint points matched with the scanning signal intensity and the scanning MAC address;
specifically, each WIFI position fingerprint point in the WIFI fingerprint feature library includes a corresponding relationship between a sampling position and a fingerprint feature, that is, environment acquisition data, a floor map name, an MAC address list, and a signal strength list. Therefore, the scanning signal intensity and the scanning MAC address are respectively compared with the MAC address list and the signal intensity list corresponding to each WIFI position fingerprint point in the WIFI fingerprint feature library, and the WIFI position fingerprint point matched with the scanning signal intensity and the scanning MAC address can be screened out.
S230, scanning the current surrounding environment of the environment sensing sensor through the environment sensing sensor, and acquiring environment sensing data; the environmental sensing data comprises visual observation data and laser observation data;
specifically, the environment sensing sensors include a vision sensor (a camera, a depth camera, a binocular camera, etc.) and a laser sensor (a laser radar, a millimeter wave radar, etc.). The intelligent mobile device can start the vision sensor to acquire picture data in a visual angle range as visual observation data, and can also start the laser sensor to scan the surrounding environment to acquire laser point data as laser observation data. Here, the vision sensor may be rotated to acquire vision observation data of multiple directions around itself, and the laser sensor may be rotated to acquire laser observation data of multiple directions around itself in the same manner.
S310, extracting environmental features in the environmental sensing data;
s320, matching the environment features with preset features corresponding to the candidate feature points respectively to obtain corresponding similarity;
specifically, the corresponding floor environment map can be queried and obtained according to the floor map name. Each floor environment map has known preset features including structural features and color features, the structural features include but are not limited to straight line segments, corners, points, vertical lines and the like, and the corresponding examples are walls, corners, convex corners, doors and the like. The environment features comprise geometric feature information and color feature information, the geometric feature information comprises but is not limited to features such as straight line segments, corners, points, vertical lines and the like, and corresponding examples are features such as walls, corners, convex corners, doors and the like.
The intelligent mobile device acquires visual observation data through the visual sensor, or acquires laser observation data through the laser sensor, and performs feature extraction through any one or two of the visual observation data and the laser observation data to acquire environmental features around the position where the intelligent mobile device is located at the current moment.
The following specifically describes a process of obtaining environmental features by feature extraction through laser observation data: the prior art is that the laser observation data are obtained, the laser observation data are subjected to region segmentation, then the geometric feature information included in the laser observation data are extracted through an angular point detection algorithm and a straight line fitting algorithm, and the extraction of the geometric feature information from the laser observation data is not described in detail herein. The geometric characteristic information may be used to represent an environmental characteristic corresponding to a position where the smart mobile device obtains the laser observation data, and obtain the geometric characteristic information of the smart mobile device scanned and obtained at a current position (or a start-up position, which may be any position in the preset space) as the environmental characteristic.
The following specifically describes a process of obtaining environmental features through feature extraction performed by visual observation data: the method comprises the steps of acquiring visual observation data, carrying out gray processing and binarization processing on the visual observation data, namely a shot image, extracting geometric feature information included in the visual observation data by utilizing an edge detection algorithm such as an SIFT algorithm, a Sobel operator, a Previtt operator and the like, and extracting the geometric feature information from the image. The geometric feature information may be used to characterize one of the environmental features corresponding to the location where the smart mobile device obtains the visual observation data. In addition, if the camera installed on the smart mobile device is a depth camera, the smart mobile device may further use a color attribute corresponding to each piece of geometric feature information in the captured image as one of the environmental features through an image recognition algorithm.
Because each WIFI position fingerprint point comprises a corresponding floor map name, after the candidate feature point is obtained, a target floor map name corresponding to the candidate feature point can be found out from the WIFI fingerprint feature library, then a corresponding target floor environment map is called according to the target floor map name, preset features contained in the target floor environment map are obtained from the target floor environment map to obtain target preset features, the intelligent mobile device matches the environment features with the target preset features respectively to obtain the similarity between the environment features and the target preset features, and the similarity between the environment features and the preset features corresponding to the candidate feature points is also obtained.
S400, determining the self positioning result according to the floor map name and the sampling position corresponding to the candidate feature point with the maximum similarity.
Specifically, the intelligent mobile device compares the similarity degrees to find out the candidate feature point with the maximum similarity degree, and the candidate feature point with the maximum similarity degree belongs to one element in the WIFI fingerprint feature library, namely one of the WIFI position fingerprint points in the WIFI fingerprint feature library, so that after the candidate feature point with the maximum similarity degree is found out, the corresponding floor map name and the sampling position are directly called from the WIFI fingerprint feature library, the floor where the intelligent mobile device is located at present can be determined according to the floor corresponding to the floor map name, and the position of the floor where the intelligent mobile device is located at present is determined according to the sampling position.
In this embodiment, the method and the device screen out candidate feature points by combining a pre-established WIFI fingerprint feature library, can determine an approximate position range where an intelligent mobile device in a field is located according to the candidate feature points, perform similarity matching on environment sensing data and the candidate feature points by using environment sensing data sensed by the intelligent mobile device, determine a floor map name and a sampling position corresponding to the candidate feature point with the maximum similarity, and determine a positioning result, thereby implementing indoor self-positioning of the intelligent mobile device. Secondly, the problem of positioning loss caused by network disconnection or disconnection of the WIFI network in the positioning process completely depending on the WIFI network in the prior art is solved, the indoor positioning result can be more accurate, and the accuracy of indoor positioning is improved. Finally, the approximate position where the intelligent mobile device in the site is located is identified through the assistance of the existing indoor WIFI network to realize preliminary screening, so that the positioning matching range can be greatly reduced, and the overall positioning efficiency of the intelligent mobile device in the site is improved.
And finally, after the intelligent mobile device is started, according to the laser observation data acquired by the laser sensor or the visual observation data acquired by the visual sensor, the floor map name and the sampling position corresponding to the candidate feature point with the maximum similarity value are found out in an enumeration matching mode, so that the positioning result of the intelligent mobile device is obtained through analysis, and the initial position is positioned. According to the embodiment, the environment does not need to be modified, the modification such as landmark sticking, light reflecting strips and the like in the environment does not need to be utilized in the traditional method, and the applicability is wide. Moreover, after the initial position is positioned, the movement track of the intelligent mobile equipment is monitored by utilizing the movement data of the intelligent mobile equipment, so that the positioning of the intelligent mobile equipment in the movement process can be tracked and acquired in real time, and the accuracy and the reliability of indoor positioning are greatly improved.
The method comprises the steps of acquiring an identification position list by identifying video image data acquired by monitoring equipment which is deployed in a preset space in advance according to the video image data, then matching the identification position list with the known space position of the intelligent mobile equipment which is positioned, screening out all intelligent equipment to be positioned and a candidate position list, scanning the surrounding environment of the current position of the intelligent mobile equipment by utilizing laser scanning equipment (laser radar, millimeter wave radar and the like) or visual scanning equipment (a camera, a depth camera, a binocular camera and the like) which is installed on the intelligent mobile equipment, extracting features, and performing matching positioning with structural features corresponding to candidate floor maps in the candidate position list.
By the method, the intelligent mobile equipment has global positioning and positioning recovery capability, and meanwhile, the real-time performance of positioning recovery is greatly guaranteed. By obtaining the environmental features, when the environmental features meet the structural features, similarity matching is carried out on the environmental features and the structural features, and the structural feature with the maximum similarity is determined to be the candidate floor map corresponding to the environment where the intelligent mobile device is located. The method has the advantages that more straight line features are utilized in the environment, when the straight line features of the environment are obvious, straight lines in the environment are extracted to greatly reduce the number of matching points, and when the floor and the position of the intelligent mobile device are located according to the matching points, the algorithm can be quickly converged, the algorithm efficiency is improved, and therefore the locating result of the intelligent mobile device can be quickly obtained.
One embodiment of the present invention, as shown in fig. 3, is an implementation method of indoor self-positioning, including:
s010 collects WIFI position fingerprint points in different directions at different sampling position points, and establishes a WIFI fingerprint feature library; the WIFI position fingerprint point comprises a sampling position and a corresponding relation between fingerprint characteristics, wherein the fingerprint characteristics comprise environment acquisition data, a floor map name, an MAC address list and a signal intensity list;
s100, searching for a WIFI signal at the position of the WIFI signal to acquire WIFI scanning data;
s210, acquiring the scanning signal intensity and the scanning MAC address of a WIFI signal source connected with the intelligent mobile equipment at the current moment according to the WIFI scanning data;
s220, screening out a plurality of candidate feature points according to the matching of the scanning signal intensity and the scanning MAC address with the WIFI fingerprint feature library; the candidate characteristic points are WIFI position fingerprint points matched with the scanning signal intensity and the scanning MAC address;
s230, scanning the current surrounding environment of the environment sensing sensor through the environment sensing sensor, and acquiring environment sensing data; the environmental sensing data comprises visual observation data and laser observation data;
s330, matching the laser observation data with the laser sampling data to which each candidate feature point belongs respectively to obtain corresponding similarity according to a point cloud registration algorithm; or the like, or, alternatively,
specifically, the point cloud registration algorithm calculates the dislocation between two point clouds through a statistical rule, so as to achieve the effect of automatically registering the two point clouds. In this embodiment, the laser observation data is used as one point cloud object for automatic registration, and the laser sampling data to which each candidate feature point belongs is used as another point cloud object for automatic registration in pair. The concrete implementation steps are as follows: according to the same key point selection standard, key points are respectively extracted from laser observation data (hereinafter referred to as a laser point set n, n represents the laser observation data) and laser sampling data to which any candidate feature point belongs (hereinafter referred to as a laser point set mi, i belongs to a positive integer, and mi represents the laser sampling data to which any candidate feature point belongs). Respectively calculating the feature descriptors of all the selected key points, then combining the coordinate positions of the feature descriptors in the two laser point sets, estimating the corresponding relation between the feature descriptors and the coordinate positions on the basis of the similarity of the features and the positions between the feature descriptors and the coordinate positions, and preliminarily estimating corresponding point pairs. Assuming that the data is noisy, the corresponding pairs of points of error that contribute to the registration are removed. And estimating rigid body transformation by using the residual correct corresponding relation, finishing single iterative registration, and circularly and repeatedly registering the steps for multiple times to obtain the similarity between the laser point set n and the laser point set mi, namely the similarity between the laser observation data and the laser sampling data to which any candidate characteristic point belongs. And similarly, the similarity between the acquired laser observation data and the laser sampling data to which each candidate characteristic point belongs can be calculated.
Through the point cloud registration algorithm, the similarity between the environment where the intelligent mobile device is located and each candidate feature point can be improved, and therefore the indoor self-positioning rate and accuracy of the intelligent mobile device are improved. And moreover, matching and positioning are identified through a point cloud registration algorithm, since key points are extracted quickly and accurately, the matching accuracy of the key points is high, the speed is high, the accurate result can reach very high precision after iteration fine registration is iterated for a certain number of times, the registration precision can reach millimeter level, the method can be used for complex point cloud data, the engineering application requirements are met, and the registration method is wide in application range.
S340, matching the visual observation data with the image sampling data to which each candidate feature point belongs according to an image recognition algorithm to obtain corresponding similarity.
Specifically, the image recognition algorithm is to extract image features in the image data, which include, but are not limited to, geometric features and pixel features. In this embodiment, the visual observation data is used as one image object for automatic registration, and the image sampling data to which each candidate feature point belongs is used as another image object for automatic registration in pair. The concrete implementation steps are as follows: and comparing the visual observation data with the image sampling data to which any candidate characteristic point belongs by adopting pixel point comparison, projection comparison, gravity center comparison and block comparison algorithms to obtain the similarity between the visual observation data and the image sampling data to which any candidate characteristic point belongs, namely the similarity between the visual observation data and the image sampling data to which any candidate characteristic point belongs. And similarly, the similarity between the acquired visual observation data and the image sampling data to which each candidate feature point belongs can be calculated. Illustratively, when pixel point comparison is adopted, a standard gray pixel of visual observation data and a matching gray pixel of image sampling data to which any candidate feature point belongs are obtained, the hamming distance of the visual observation data and the image sampling data to which any candidate feature point belongs is respectively obtained according to the standard gray pixel and the matching gray pixel, the similarity of the visual observation data and the image sampling data to which any candidate feature point belongs is calculated according to the hamming distance, and the value range is [0.0, 1.0 ].
Through the embodiment, the image similarity is compared through the image characteristics, the similarity between the environment where the intelligent mobile equipment is located and each candidate characteristic point can be improved, and therefore the indoor self-positioning rate and accuracy of the intelligent mobile equipment are improved.
S400, determining the self positioning result according to the floor map name and the sampling position corresponding to the candidate feature point with the maximum similarity.
If the laser similarity corresponding to the laser observation data and the laser sampling data of each candidate feature point is obtained by matching according to the point cloud registration algorithm, the corresponding graph similarity is obtained by matching according to the point cloud registration algorithm and the visual observation data and the image sampling data of each candidate feature point, the weight values of the laser similarity and the image similarity are set according to the empirical value, the final similarity is obtained by weight calculation, then the final similarity between the environment sensing data acquired at the current position and the environment acquisition data corresponding to each candidate feature point is compared, the final similarity is compared, the floor map name and the sampling position corresponding to the candidate feature point with the maximum final similarity are determined, and the self positioning result is determined according to the floor map name and the sampling position corresponding to the candidate feature point with the maximum final similarity
In one embodiment of the present invention, a method for implementing indoor self-positioning includes:
the searching module is used for searching the WIFI signal at the position of the searching module to acquire WIFI scanning data;
the screening module is used for screening candidate feature points matched with the WIFI scanning data according to the WIFI fingerprint feature library;
the scanning module is used for scanning and acquiring environmental sensing data around the position of the scanning module;
the matching module is used for respectively matching the candidate characteristic points with the environment sensing data to obtain corresponding similarity;
and the positioning processing module is used for determining the self positioning result according to the floor map name and the sampling position corresponding to the candidate characteristic point with the maximum similarity.
Based on the foregoing embodiment, further comprising:
the acquisition module is used for acquiring WIFI position fingerprint points in different directions at different sampling position points; the WIFI position fingerprint point comprises a sampling position and a corresponding relation between fingerprint characteristics, wherein the fingerprint characteristics comprise environment acquisition data, a floor map name, an MAC address list and a signal intensity list
And the database building module is used for building a WIFI fingerprint feature database according to the WIFI position fingerprint points.
Based on the foregoing embodiment, the screening module includes:
the extraction unit is used for acquiring the scanning signal intensity and the scanning MAC address of the WIFI signal source connected with the intelligent mobile equipment at the current moment according to the WIFI scanning data;
the matching unit is used for screening out a plurality of candidate feature points according to the matching of the scanning signal intensity and the scanning MAC address with the WIFI fingerprint feature library;
the candidate characteristic points are WIFI position fingerprint points matched with the scanning signal intensity and the scanning MAC address;
the scanning module includes:
the laser scanning unit is used for scanning the current surrounding environment of the laser scanning unit through the laser sensor and acquiring and obtaining laser observation data;
the visual scanning unit is used for scanning the current surrounding environment of the visual scanning unit through a visual sensor and acquiring visual observation data;
wherein the environmental sensing data includes visual observation data and laser observation data.
Based on the foregoing embodiment, the matching module includes:
an extraction unit for extracting environmental features in the environmental sensing data;
the first matching unit is used for matching the environmental characteristics with preset characteristics corresponding to the candidate characteristic points respectively to obtain corresponding similarity;
the second matching unit is used for matching the laser observation data with the laser sampling data to which each candidate feature point belongs respectively to obtain corresponding similarity according to a point cloud registration algorithm;
and the third matching unit is used for matching the visual observation data with the image sampling data to which each candidate feature point belongs respectively to acquire corresponding similarity according to an image recognition algorithm.
Specifically, this embodiment is a device embodiment corresponding to the method embodiment, and specific effects refer to the method embodiment, which is not described in detail herein.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of program modules is illustrated, and in practical applications, the above-described distribution of functions may be performed by different program modules, that is, the internal structure of the apparatus may be divided into different program units or modules to perform all or part of the above-described functions. Each program module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one processing unit, and the integrated unit may be implemented in a form of hardware, or may be implemented in a form of software program unit. In addition, the specific names of the program modules are only used for distinguishing the program modules from one another, and are not used for limiting the protection scope of the application.
In an embodiment of the present invention, a storage medium stores at least one instruction, and the instruction is loaded and executed by a processor to implement the operations performed by the corresponding embodiments of the indoor self-positioning implementation method. For example, the storage medium may be a read-only memory (ROM), a Random Access Memory (RAM), a compact disc read-only memory (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
They may be implemented in program code executable by a computing device such that it is executed by the computing device, or as individual integrated circuit modules, or as a plurality of modules or steps within a single integrated circuit module, when stored in a storage device. Thus, the present invention is not limited to any specific combination of hardware and software.
In the foregoing embodiments, the descriptions of the respective embodiments have their respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or recited in detail in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be through some interfaces, indirect coupling or communication connection of devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
The integrated modules/units may be stored in a storage medium if they are implemented in the form of software functional units and sold or used as separate products. Based on such understanding, all or part of the flow in the method according to the embodiments of the present invention may also be implemented by sending instructions to relevant hardware by the computer program 121, where the computer program 121 may be stored in a storage medium, and when the computer program 121 is executed by a processor, the steps of the above-described embodiments of the method may be implemented. The computer program 121 may be in a source code form, an object code form, an executable file or some intermediate form, etc. The storage medium may include: any entity or device capable of carrying the computer program 121, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium, etc. It should be noted that the content of the storage medium may be increased or decreased as appropriate according to the requirements of legislation and patent practice in the jurisdiction, for example: in certain jurisdictions, in accordance with legislation and patent practice, computer-readable storage media do not include electrical carrier signals and telecommunications signals.
It should be noted that the above embodiments can be freely combined as necessary. The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (9)
1. An indoor self-positioning realization method is characterized by comprising the following steps:
searching a WIFI signal at the position of the WIFI signal to acquire WIFI scanning data;
screening out candidate feature points matched with the WIFI scanning data according to a WIFI fingerprint feature library, and scanning to obtain environment sensing data around the position where the candidate feature points are located;
searching out a target floor map name corresponding to the candidate feature point from the WIFI fingerprint feature library, calling a corresponding target floor environment map according to the target floor map name, and acquiring preset features from the target floor environment map;
respectively matching the candidate feature points with the environmental sensing data to obtain corresponding similarity, wherein the similarity comprises the following steps: extracting environmental features in the environmental sensing data; matching the environmental features with preset features corresponding to the candidate feature points respectively to obtain corresponding similarity, wherein the preset features comprise structural features and color features;
and determining the self positioning result according to the floor map name and the sampling position corresponding to the candidate characteristic point with the maximum similarity.
2. The method for implementing indoor self-positioning according to claim 1, wherein before searching for WIFI signals at its own location to obtain WIFI scan data, the method includes:
collecting WIFI position fingerprint points in different directions at different sampling position points, and establishing a WIFI fingerprint feature library; the WIFI position fingerprint points comprise corresponding relations between sampling positions and fingerprint features, and the fingerprint features comprise environment acquisition data, floor map names, MAC address lists and signal intensity lists.
3. The method for realizing indoor self-positioning according to claim 1, wherein the step of screening out candidate feature points matching with the WIFI scanning data according to a WIFI fingerprint feature library, and scanning to obtain environmental sensing data around the position of the self-positioning comprises the steps of:
acquiring the scanning signal intensity and the scanning MAC address of a WIFI signal source connected with the intelligent mobile equipment at the current moment according to the WIFI scanning data;
screening out a plurality of candidate feature points according to the matching of the scanning signal intensity and the scanning MAC address with the WIFI fingerprint feature library;
scanning the current surrounding environment of the environment sensing sensor through the environment sensing sensor, and acquiring the environment sensing data; the environmental sensing data comprises visual observation data and laser observation data;
and the candidate characteristic points are WIFI position fingerprint points matched with the scanning signal intensity and the scanning MAC address.
4. The method for implementing indoor self-positioning according to claim 3, wherein the obtaining the corresponding similarity according to the matching of the candidate feature points with the environment sensing data respectively comprises:
matching the laser observation data with laser sampling data to which each candidate characteristic point belongs respectively according to a point cloud registration algorithm to obtain corresponding similarity; or the like, or, alternatively,
and matching the visual observation data with the image sampling data to which each candidate characteristic point belongs respectively according to an image recognition algorithm to obtain corresponding similarity.
5. An intelligent mobile device, comprising:
the searching module is used for searching the WIFI signal at the position of the searching module to acquire WIFI scanning data;
the screening module is used for screening out candidate feature points matched with the WIFI scanning data according to a WIFI fingerprint feature library;
the scanning module is used for scanning and acquiring environmental sensing data around the position where the scanning module is located;
the matching module is used for searching out a target floor map name corresponding to the candidate characteristic point from the WIFI fingerprint characteristic library, then calling a corresponding target floor environment map according to the target floor map name, and acquiring preset characteristics from the target floor environment map; respectively matching the candidate feature points with the environmental sensing data to obtain corresponding similarity; the matching module comprises an extraction unit and a first matching unit; an extraction unit configured to extract an environmental feature in the environmental sensing data; the first matching unit is used for matching the environmental features with preset features corresponding to the candidate feature points respectively to obtain corresponding similarity; wherein the preset features comprise structural features and color features;
and the positioning processing module is used for determining the self positioning result according to the floor map name and the sampling position corresponding to the candidate characteristic point with the maximum similarity.
6. The smart mobile device of claim 5, further comprising:
the acquisition module is used for acquiring WIFI position fingerprint points in different directions at different sampling position points; the WIFI position fingerprint points comprise corresponding relations between sampling positions and fingerprint features, and the fingerprint features comprise environment acquisition data, floor map names, MAC address lists and signal intensity lists
And the database building module is used for building a WIFI fingerprint feature database according to the WIFI position fingerprint points.
7. The smart mobile device of claim 5, wherein the filtering module comprises:
the extraction unit is used for acquiring the scanning signal intensity and the scanning MAC address of the WIFI signal source connected with the intelligent mobile equipment at the current moment according to the WIFI scanning data;
the matching unit is used for screening out a plurality of candidate feature points according to the matching of the scanning signal intensity and the scanning MAC address with the WIFI fingerprint feature library;
the candidate characteristic points are WIFI position fingerprint points matched with the scanning signal intensity and the scanning MAC address;
the scanning module includes:
the laser scanning unit is used for scanning the current surrounding environment of the laser scanning unit through the laser sensor and acquiring and obtaining laser observation data;
the visual scanning unit is used for scanning the current surrounding environment of the visual scanning unit through a visual sensor and acquiring visual observation data;
wherein the environmental sensing data comprises visual observation data and laser observation data.
8. The smart mobile device of claim 7, wherein the matching module comprises:
the second matching unit is used for matching the laser observation data with the laser sampling data to which each candidate feature point belongs respectively to obtain corresponding similarity according to a point cloud registration algorithm;
and the third matching unit is used for matching the visual observation data with the image sampling data to which each candidate characteristic point belongs respectively to acquire corresponding similarity according to an image recognition algorithm.
9. A storage medium having stored therein at least one instruction, which is loaded and executed by a processor to implement the operations performed by the method of implementing indoor self-positioning according to any one of claims 1 to 4.
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| CN113382376B (en) * | 2021-05-08 | 2022-05-10 | 湖南大学 | Indoor positioning method based on WIFI and visual integration |
| CN117271499A (en) * | 2023-11-17 | 2023-12-22 | 威海市驰云网络科技有限公司 | Wi-Fi geographic positioning datum point data cleaning method in IP technology |
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