CN111148033B - Auxiliary navigation method of self-moving equipment - Google Patents
Auxiliary navigation method of self-moving equipment Download PDFInfo
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- CN111148033B CN111148033B CN201911319701.0A CN201911319701A CN111148033B CN 111148033 B CN111148033 B CN 111148033B CN 201911319701 A CN201911319701 A CN 201911319701A CN 111148033 B CN111148033 B CN 111148033B
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/024—Guidance services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/50—Network services
- H04L67/52—Network services specially adapted for the location of the user terminal
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/029—Location-based management or tracking services
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/80—Services using short range communication, e.g. near-field communication [NFC], radio-frequency identification [RFID] or low energy communication
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination
Abstract
The invention discloses an auxiliary navigation method of self-moving equipment, which comprises the following steps: the method comprises the steps that a self-mobile device searches WIFI router signals nearby the self-mobile device in real time in the process of traveling and feeds back the signals to a scheduling server; the scheduling server determines the position of the mobile equipment according to the WIFI router information and calls image data of a camera near the position of the mobile equipment; the scheduling server compares and identifies the obtained image data, searches the image data collected from the mobile equipment, calculates the position of the mobile equipment and the positions of surrounding obstacles by adopting a distance measurement algorithm for the image data containing the mobile equipment, and feeds back a position analysis result to the mobile equipment; and the self-mobile equipment performs fusion calculation on the position analysis result sent by the scheduling server and navigation data of a self-navigation system, and performs auxiliary correction on the shifted movement track. The invention can assist the navigation system of the mobile device to correct the route and improve the navigation precision.
Description
Technical Field
The invention belongs to the technical field of navigation, and particularly relates to an auxiliary navigation method for self-moving equipment such as a hospital delivery vehicle.
Background
The navigation modes of the hospital distribution vehicle in the market at present mainly include two modes, one mode is that a reflective strip is arranged at a specific position of a ceiling, signal transmitting/receiving equipment such as an infrared transmitting tube, a Bluetooth module or a radio frequency module is arranged on the distribution vehicle, after the signal transmitting/receiving equipment transmits a signal, the signal is reflected by the reflective strip and received by the signal transmitting/receiving equipment on the distribution vehicle, and the directional movement of the distribution vehicle is guided by identifying the signal to complete distribution. The other mode is to carry out intelligent obstacle avoidance and map navigation through a laser radar and guide the movement of a delivery vehicle.
The first navigation mode needs to deploy a large number of reflective strips at fixed positions, and after the reflective strips are arranged, the route needs to be changed to relocate the reflective strips, so that the workload is large, and the flexibility is low. The method for navigating by adopting the laser radar is relatively more flexible, but when the distribution vehicle navigates by adopting the laser radar, the situation of navigation failure may occur due to various accidental factors, for example, the gyroscope data on the vehicle body suddenly fails to cause walking angle deviation, or the signals of the navigation system are interfered or failed to cause deviation between the actual position and the map position, so that the phenomena of positioning misalignment, map deviation and the like occur, and at the moment, the distribution vehicle may not identify the position deviation of the distribution vehicle, so that the normal work of the distribution vehicle is influenced.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to provide an auxiliary navigation method of self-moving equipment based on WIFI equipment and a camera, which can be matched with an original navigation system on the self-moving equipment to improve the navigation precision.
The purpose of the invention is realized by adopting the following technical scheme:
an assisted navigation method from a mobile device, comprising the steps of:
the method comprises the steps that a self-mobile device searches signals of a WIFI router nearby the position of the self-mobile device in real time in the process of traveling, and feeds back the searched device information of the WIFI router to a scheduling server;
the scheduling server determines the position of the mobile equipment according to the WIFI router information fed back by the mobile equipment and calls image data collected by a camera near the position of the mobile equipment;
the scheduling server compares and identifies the obtained image data, and searches the image data collected from the mobile equipment;
the scheduling server analyzes the image data containing the self-moving equipment, calculates the position of the self-moving equipment and the positions of obstacles around the self-moving equipment by adopting a ranging algorithm, and feeds back the position analysis result to the self-moving equipment;
and the self-mobile equipment performs fusion calculation on the position analysis result sent by the scheduling server and navigation data of a self-navigation system, and performs auxiliary correction on the shifted movement track.
Furthermore, the scheduling server is in communication connection with the WIFI router and the camera.
Further, the scheduling server stores device information of the WIFI router and the camera on the moving path of the self-moving device and a shape library of the self-moving device.
Further, when the mobile device searches signals of a plurality of WIFI routers, the scheduling server determines the position of the mobile device according to the signal intensity of the WIFI routers, selects the WIFI router with the maximum signal intensity from the searched signals of the WIFI routers, and determines the WIFI router with the maximum signal intensity as the router closest to the mobile device, wherein the position of the WIFI router is the position of the mobile device.
Further, when the scheduling server calls the image data collected by the cameras, the scheduling server calls the image data collected by more than 1 camera near the position of the self-moving device from near to far in sequence based on the position of the self-moving device, searches whether the called image data contains the self-moving device, and stops calling the image data of the cameras after the image data containing the self-moving device is found.
Further, a distance between the self-moving device and a surrounding reference object is calculated by adopting a ranging algorithm based on OpenCV:
a. the dispatching server performs normalization processing on the image data containing the mobile equipment, extracts a characteristic layer, performs binarization processing on the image data with the extracted characteristic layer, and determines a distribution vehicle and a reference object around the distribution vehicle in the image;
b. calculating the correlation between points and the image data after the binarization processing by using a proportional equation y which is kx + b, wherein y in the formula represents the correlation between the points, k is an image proportional coefficient, x is the point in the image data after the binarization processing, and b is a compensation distance; recording the points with close correlation obtained after correlation calculation according to the proportional equation, and calculating the equation according to the nodes
Calculating the distance P between the surrounding obstacles and the delivery vehicle, wherein tanh represents a hyperbolic function, W is a function based on OpenCV, and x1,…,xrDenotes the recorded 1 st toThe point where the r-th correlation is close.
Further, after the feature map layer is extracted, an object in the image data is determined by adopting a boundary judgment method.
Further, the step of performing fusion calculation on the position analysis result sent by the scheduling server and the navigation data of the self navigation system by the self mobile device is as follows: after the position analysis result is sent to the self-moving equipment by the dispatching server, the self-moving equipment judges which working mode the self-moving equipment is in, if the self-moving equipment is in the obstacle avoidance mode, the self-moving equipment is considered to be in a normal state without correction, and if the self-moving equipment is in the normal walking mode, the movement track is corrected according to the position analysis result of the dispatching server.
Compared with the prior art, the invention has the beneficial effects that: the existing WIFI router and camera in the space are used for auxiliary navigation, a large amount of new equipment does not need to be deployed, the workload is reduced, and the cost is reduced; the scheduling server initially positions the area where the self-moving equipment is located according to the signal intensity of the WIFI router searched by the self-moving equipment in the moving process, calls image data of a corresponding camera based on the position where the self-moving equipment is located, identifies the image data, determines the position of the self-moving equipment and the relative relation between the position of the self-moving equipment and surrounding obstacles, feeds an identification result back to the self-moving equipment, assists an original navigation system of the self-moving equipment to perform fusion and correction of a route, and improves the navigation precision, reliability, safety and obstacle avoidance flexibility.
Drawings
FIG. 1 is a schematic diagram of an assisted navigation system according to an embodiment of the present invention;
fig. 2 is a schematic diagram of WIFI signal identification according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of image data identification according to an embodiment of the present invention;
FIG. 4 is a diagram illustrating a navigation correction according to an embodiment of the present invention.
Detailed Description
The present invention will be further described with reference to the following embodiments.
The auxiliary navigation method is used for assisting navigation by matching with an original navigation system on the mobile equipment so as to improve the navigation precision of the mobile equipment. The auxiliary navigation method comprises the steps that a camera and a WIFI router which are deployed on a moving path of the self-moving equipment are utilized, a scheduling server determines the real-time position of the self-moving equipment based on WIFI routing signals received by the self-moving equipment on the moving path in the moving process, image data collected by the camera nearby the self-moving equipment is further called, the position of the self-moving equipment and the positions of obstacles around the self-moving equipment are determined by identifying and calculating images collected by the camera, then the identification result is fed back to the self-moving equipment, and the self-moving equipment corrects the navigation path according to auxiliary navigation information and by combining with an original navigation system (such as a laser radar, an ultrasonic sensor, an infrared sensor and the like) of the self-moving equipment so as to guarantee to move according to the set path.
The navigation assistance method of the present invention will be further described below by taking a hospital delivery vehicle as an example. The auxiliary navigation method can fully utilize the original cameras and WIFI routers which are already deployed in the hospital, does not need to deploy a large amount of new equipment, reduces the deployment workload and reduces the cost. Certainly, in order to ensure the effect of the auxiliary navigation and improve the reliability and the safety of the navigation of the distribution vehicle, if a part of positions, such as indoor environments or corners, are arranged on the moving path of the hospital distribution vehicle, a camera and/or a WIFI router are not arranged, and some cameras and WIFI routers can be additionally arranged according to the needs.
As shown in fig. 1, the scheduling server is in communication connection with the cameras and WIFI routers originally deployed in the hospital through a wired network or a wireless network, and the scheduling server can directly read information of all or designated cameras and WIFI routers through authorization.
The auxiliary navigation method comprises the following steps:
s1, identifying WIFI signals; in the process of moving, the hospital delivery vehicle searches signals of WIFI routers near the position of the hospital delivery vehicle in real time, and feeds back the searched information of the WIFI routers to the scheduling server, and the information of the routers deployed on the moving path of the hospital delivery vehicle is stored in the scheduling server, so that the position of the delivery vehicle can be determined according to the information of the WIFI routers fed back by the delivery vehicle;
in the embodiment, the scheduling server determines the position of the hospital delivery vehicle according to the fed back signal strength (RSSI value) of the WIFI router; in the moving process of the distribution vehicle, 1 or more than 1 WIFI router may be searched, when a plurality of WIFI routers exist, the signal intensity of each WIFI router may be different according to the distance from the distribution vehicle in the hospital, and when the signal intensity of the WIFI router is higher, the WIFI router is closer to the distribution vehicle, so that the WIFI router is selected as the router closest to the distribution vehicle, the WIFI router is determined as the router closest to the distribution vehicle, and the current area of the distribution vehicle is judged according to the position of the WIFI router; for example, as shown in fig. 2, when the dispensing car moves to a certain position, signals of the WIFI router 1 and the WIFI router 2 can be searched, wherein the signal strength of the WIFI router 1 is-70 (the signal strength of the WIFI router usually shows a negative value), and the signal strength of the WIFI router 2 is-50, so that it can be determined that the WIFI router 2 is closer to the dispensing car, and thus the area where the dispensing car is currently located, that is, the area is located near the WIFI router 2, can be roughly determined.
S2, identifying the camera; the dispatching server calls image data collected by a camera near the current position of the distribution vehicle according to the current position of the distribution vehicle;
when the image data of the camera is called, according to the current position of the delivery vehicle preliminarily determined in step S2, the image data collected by more than 1 camera near the position is called in sequence from near to far, the image data on the camera closest to the position of the determined delivery vehicle is checked first, if no delivery vehicle exists in the image data collected by the camera, the image data on the next (second near) camera is checked, and the image data on the cameras are called and compared in sequence until the image data containing the delivery vehicle is obtained; generally, after data of 2-3 cameras are checked from near to far, a distribution vehicle can be identified; the image data on the cameras are sequentially called and checked by adopting a rule from near to far instead of processing the image data of all the cameras in real time, so that the workload of a scheduling server can be reduced, and the identification efficiency is improved.
S3, recognizing image data; the dispatching server identifies image data containing the distribution vehicle, calculates the position of the distribution vehicle and the distance between the distribution vehicle and surrounding reference objects, and feeds back the position analysis result to the distribution vehicle;
in the embodiment, a distance measurement algorithm based on OpenCV is adopted to calculate the distance between a delivery vehicle and a surrounding reference object, a profile library of a self-moving device and the surrounding reference object of a walking path is stored in a scheduling server, when image data containing the self-moving device is identified, the position of the self-moving device is determined according to the profile of the self-moving device, then the positions of the self-moving device and the surrounding reference object are calculated by adopting a proportional equation, and the distance between the self-moving device and the surrounding reference object is calculated according to a node calculation equation; the method comprises the following steps:
a. normalizing the image data including the distribution vehicle, extracting a feature map layer, and determining the distribution vehicle and reference objects (walls and the like) around the distribution vehicle in the image, wherein as shown in fig. 3, the black square in fig. 3 represents the distribution vehicle, and the gray represents the walls; in the step, the normalization processing of the image data and the extraction of the feature layer are both performed by adopting a conventional method in the prior art, are not innovation points of the invention, and are not described redundantly; in the embodiment, a boundary judgment method is adopted to determine an object in image data, including a distribution vehicle and a surrounding reference object (including a wall body or other objects with fixed positions and the like), and binarization processing is performed on the image data with the extracted feature layer, so that the boundary of the object becomes obvious, and thus the object in the image data can be determined, and the movable range of the distribution vehicle is judged;
b. calculating the correlation between points and the image data after the binarization processing by using a proportional equation y which is kx + b, wherein y in the formula represents the correlation between the points and the points, k is an image proportional coefficient, x is a point in the image data after the binarization processing, b is a compensation distance which is a preset position information parameter known from the moving path of the mobile equipment; recording correlation calculation according to proportional equationThe obtained points with close correlation calculate an equation according to the nodes
Calculating the distance P between (the actual boundary of) the surrounding obstacle and the delivery vehicle, wherein tanh represents a hyperbolic function, W is a function based on OpenCV and used for comparing normalized characteristic data, and x1,…,xrIndicating the points where the recorded 1 st to r th correlations are close.
The invention adopts the speed with low real-time property to call the image data, and estimates the distance between the barrier and the self-moving equipment by adopting a pixel proportion amplification mode based on a distance measurement algorithm, does not need to carry out a large amount of calculation, and lightens the workload of a dispatching server.
And S4, the dispatching server feeds back the position analysis result to the distribution vehicle, and the distribution vehicle combines the navigation data of the navigation system of the distribution vehicle to perform fusion calculation on the position analysis result and the navigation data of the distribution vehicle, so as to correct the deviated walking route. For example, the distance between the delivery vehicle and the wall body (fig. 4) fed back by the dispatch server is fused with the map data of the original navigation system and the feedback result of the sensor in the navigation system, the current position of the delivery vehicle is obtained by the feedback distance of the wheel, the detection distance of the radar and the overtime feedback distance, and the actual running track is corrected if the actual running track deviates. The fusion calculation is a logic judgment process, the dispatching server sends a calculation identification result to the distribution vehicle, the distribution vehicle firstly judges the working mode of the distribution vehicle after receiving the result, if the distribution vehicle is in an obstacle avoidance mode, namely the route is deviated due to avoiding obstacles, the distribution vehicle considers that the distribution vehicle is in a normal state without correction, if the distribution vehicle is in a normal walking state, but the identification result of the dispatching server displays the deviated route, the map and the navigation are corrected by adopting a conventional method in the prior art, the method is not an innovation point of the invention, and the method is not a new narrative.
Although the navigation system of the existing self-moving equipment also has an autonomous repair function and can correct errors, the navigation system cannot correct the errors autonomously if the navigation system has no reference of other auxiliary information for irreversible error errors.
When the method is used for auxiliary navigation, the device information of the WIFI router and the camera on the moving path can be recorded in advance according to the set moving path of the self-moving device and stored in the scheduling server, so that the scheduling server can analyze the position of the delivery vehicle and call the corresponding camera according to the signal of the WIFI router during auxiliary navigation, and the auxiliary navigation function is realized.
Various other changes and modifications to the above-described embodiments and concepts will become apparent to those skilled in the art from the above description, and all such changes and modifications are intended to be included within the scope of the present invention as defined in the appended claims.
Claims (7)
1. An assisted navigation method from a mobile device, comprising the steps of:
the method comprises the steps that a self-mobile device searches signals of a WIFI router nearby the position of the self-mobile device in real time in the process of traveling, and feeds back the searched device information of the WIFI router to a scheduling server;
the scheduling server determines the position of the mobile equipment according to the WIFI router information fed back by the mobile equipment and calls image data collected by a camera near the position of the mobile equipment;
the scheduling server compares and identifies the obtained image data, and searches the image data collected from the mobile equipment;
the dispatching server analyzes the image data containing the self-moving equipment, calculates the position of the self-moving equipment and the distance between the self-moving equipment and the surrounding reference objects, feeds back the position analysis result to the self-moving equipment, and calculates the distance between the self-moving equipment and the surrounding reference objects by adopting a ranging algorithm based on OpenCV:
a. the dispatching server performs normalization processing on the image data containing the mobile equipment, extracts a characteristic layer, performs binarization processing on the image data with the extracted characteristic layer, and determines a distribution vehicle and a reference object around the distribution vehicle in the image;
b. calculating the correlation between points and the image data after the binarization processing by using a proportional equation y which is kx + b, wherein y in the formula represents the correlation between the points, k is an image proportional coefficient, x is the point in the image data after the binarization processing, and b is a compensation distance; recording the points with close correlation obtained after correlation calculation according to the proportional equation, and calculating the equation according to the nodes
Calculating the distance P between the surrounding obstacles and the delivery vehicle, wherein tanh represents a hyperbolic function, W is a function based on OpenCV, and x1,…,xrPoints representing the recorded 1 st to r th correlations close;
and the self-mobile equipment performs fusion calculation on the position analysis result sent by the scheduling server and navigation data of a self-navigation system, and performs auxiliary correction on the shifted movement track.
2. The method of assisted navigation from a mobile device of claim 1, wherein: and the scheduling server is in communication connection with the WIFI router and the camera.
3. The method of assisted navigation from a mobile device according to claim 1 or 2, characterized in that: the scheduling server stores the device information of the WIFI router and the camera on the moving path of the self-moving device and the appearance library of the self-moving device and the surrounding reference objects.
4. The method of assisted navigation from a mobile device of claim 1, wherein: when the mobile equipment searches signals of a plurality of WIFI routers, the scheduling server determines the position of the mobile equipment according to the signal intensity of the WIFI routers, selects the WIFI router with the maximum signal intensity from the searched signals of the WIFI routers, and determines the WIFI router with the maximum signal intensity as the router closest to the mobile equipment, wherein the position of the WIFI router is the position of the mobile equipment.
5. The method of assisted navigation from a mobile device of claim 1, wherein: when the scheduling server calls the image data collected by the cameras, the image data collected by more than 1 camera near the position is called in sequence from near to far based on the position of the self-moving equipment, whether the called image data contains the self-moving equipment or not is searched, and the calling of the image data of the cameras is stopped after the image data containing the self-moving equipment is searched.
6. The method of assisted navigation from a mobile device of claim 1, wherein: and after the characteristic map layer is extracted, determining an object in the image data by adopting a boundary judgment method.
7. The method of assisted navigation from a mobile device of claim 1, wherein: the method for performing fusion calculation on the position analysis result sent by the scheduling server and the navigation data of the self navigation system by the self mobile equipment comprises the following steps: after the position analysis result is sent to the self-moving equipment by the dispatching server, the self-moving equipment judges which working mode the self-moving equipment is in, if the self-moving equipment is in the obstacle avoidance mode, the self-moving equipment is considered to be in a normal state without correction, and if the self-moving equipment is in the normal walking mode, the movement track is corrected according to the position analysis result of the dispatching server.
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| CN106403951A (en) * | 2016-08-26 | 2017-02-15 | 观宇能源科技（上海）有限公司 | Computer vision based positioning system and positioning method thereof |
| CN106679648B (en) * | 2016-12-08 | 2019-12-10 | 东南大学 | Visual inertia combination SLAM method based on genetic algorithm |
| CN106899935B (en) * | 2017-01-18 | 2018-08-14 | 深圳大学 | A kind of indoor orientation method and system based on radio receiver and camera |
| CN109212571A (en) * | 2017-06-29 | 2019-01-15 | 沈阳新松机器人自动化股份有限公司 | Navigation locating method and device |
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