CN109239659B - Indoor navigation method, device, computer equipment and storage medium - Google Patents

Indoor navigation method, device, computer equipment and storage medium Download PDF

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Publication number
CN109239659B
CN109239659B CN201811008385.0A CN201811008385A CN109239659B CN 109239659 B CN109239659 B CN 109239659B CN 201811008385 A CN201811008385 A CN 201811008385A CN 109239659 B CN109239659 B CN 109239659B
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navigation
preset
route
point coordinate
fixed point
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CN109239659A (en
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秦勇
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Ping An Technology Shenzhen Co Ltd
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Ping An Technology Shenzhen Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/02—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using radio waves
    • G01S5/08—Position of single direction-finder fixed by determining direction of a plurality of spaced sources of known location
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/20—Instruments for performing navigational calculations
    • G01C21/206—Instruments for performing navigational calculations specially adapted for indoor navigation
    • 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
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/33—Services specially adapted for particular environments, situations or purposes for indoor environments, e.g. buildings
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Navigation (AREA)

Abstract

The invention discloses an indoor navigation method, an indoor navigation device, computer equipment and a storage medium, wherein the indoor navigation method comprises the following steps: acquiring an actually measured WIFI signal cluster of a mobile shooting end at the current position; comparing the actual measurement WIFI signal clusters with a preset random forest to obtain fixed point coordinates corresponding to the mobile shooting end in a preset navigation map as starting point coordinates; acquiring terminal coordinates, and generating at least two recommended navigation routes; and acquiring obstacle avoidance detection results of the mobile shooting end on at least two recommended navigation routes, selecting the recommended navigation route which is in an obstacle-free state and has the shortest distance as a target navigation route, sending the target navigation route to the mobile shooting end, and controlling the mobile shooting end to move according to the target navigation route. The indoor navigation method adopts the WIFI signal to locate the current position of the mobile shooting end, is not limited by the hardware detection range, and is simple and quick in locating mode.

Description

Indoor navigation method, device, computer equipment and storage medium
Technical Field
The present invention relates to the field of intelligent navigation, and in particular, to an indoor navigation method, an indoor navigation device, a computer device, and a storage medium.
Background
The path planning is one of important links of navigation research of the obstacle avoidance vehicle. When the obstacle avoidance vehicle executes a task, the obstacle avoidance vehicle is required to search an optimal path from the current position to the target place in the working environment according to the current road condition at any time. Therefore, positioning the current position of the obstacle avoidance vehicle becomes a primary problem in path planning.
The existing indoor positioning method comprises Bluetooth positioning, RFID (Radio Frequency Identification ) positioning, infrared positioning and the like, and the Bluetooth positioning system is poor in stability, the RFID positioning has no communication capability, and the infrared positioning has poor penetrability when meeting obstacles. How to ensure the indoor positioning stability and the timely communication capability of the obstacle avoidance vehicle so as to improve the real-time acquisition of the optimal driving route of the obstacle avoidance vehicle according to road conditions becomes a problem to be solved urgently.
Disclosure of Invention
The embodiment of the invention provides an indoor navigation method, an indoor navigation device, computer equipment and a storage medium, which are used for solving the problem that an obstacle avoidance vehicle acquires an optimal driving route according to road conditions in real time.
An indoor navigation method, comprising:
acquiring WIFI signal intensity of a mobile shooting end between a current position and at least three appointed wireless hotspots;
Acquiring an actually measured WIFI signal cluster of the mobile shooting end at the current position based on the WIFI signal intensity;
comparing the actual measurement WIFI signal clusters with a preset random forest to obtain fixed point coordinates corresponding to the mobile shooting end in a preset navigation map as starting point coordinates;
acquiring an end point coordinate, and generating at least two recommended navigation routes according to the start point coordinate and the end point coordinate;
and acquiring obstacle avoidance detection results of the mobile shooting end on at least two recommended navigation routes, selecting the recommended navigation route which is in an obstacle-free state and has the shortest distance as a target navigation route, sending the target navigation route to the mobile shooting end, and controlling the mobile shooting end to move according to the target navigation route.
An indoor navigation device, comprising:
the signal intensity acquisition module is used for acquiring the WIFI signal intensity between the current position of the mobile shooting end and at least three appointed wireless hotspots;
the WIFI signal cluster acquisition module is used for acquiring an actual measurement WIFI signal cluster of the mobile shooting end at the current position based on the WIFI signal intensity;
the starting point coordinate acquisition module is used for comparing the actual measurement WIFI signal cluster with a preset random forest and acquiring a fixed point coordinate corresponding to the mobile shooting end in a preset navigation map as a starting point coordinate;
The terminal coordinate acquisition module is used for acquiring terminal coordinates and generating at least two recommended navigation routes according to the starting point coordinates and the terminal coordinates;
the navigation route acquisition module is used for acquiring obstacle avoidance detection results of the mobile shooting end on at least two recommended navigation routes, selecting the recommended navigation route which is in an obstacle-free state and has the shortest route as a target navigation route, sending the target navigation route to the mobile shooting end, and controlling the mobile shooting end to move according to the target navigation route.
A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the indoor navigation method described above when the computer program is executed by the processor.
A computer readable storage medium storing a computer program which when executed by a processor performs the steps of the indoor navigation method described above.
According to the indoor navigation method, the device, the computer equipment and the storage medium, the actually measured WIFI signal cluster acquired by the mobile shooting end at the current position and between at least three appointed wireless hotspots is compared with the preset random forest, so that the fixed point coordinates corresponding to the mobile shooting end in the preset navigation map are acquired as the starting point coordinates, the current position of the mobile shooting end is positioned by adopting the WIFI signal positioning method, the limitation of the hardware detection range is avoided, and the positioning mode is simple and rapid. Meanwhile, the indoor navigation method, the indoor navigation device, the computer equipment and the storage medium can plan a target navigation route capable of avoiding the obstacle according to the starting point coordinate and the end point coordinate corresponding to the current position, so that the mobile shooting time is enabled to move based on the target navigation route, the target navigation route can be adjusted in real time according to road conditions, the navigation process is not influenced by the hardware detection range, and the navigation mode is flexible and reliable.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the description of the embodiments of the present invention will be briefly described below, it being obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic view of an application environment of an indoor navigation method according to an embodiment of the invention;
FIG. 2 is a flow chart of an indoor navigation method according to an embodiment of the present invention;
fig. 3 is a schematic diagram of acquiring WIFI signals of three designated wireless hotspots by using fixed point coordinates (0, 0) according to an embodiment of the invention;
FIG. 4 is a schematic diagram of a two-item target recommended route generated between a start point coordinate and an end point coordinate according to an embodiment of the present invention;
FIG. 5 is another flow chart of an indoor navigation method according to an embodiment of the present invention;
FIG. 6 is another flow chart of an indoor navigation method in an embodiment of the invention;
FIG. 7 is another flow chart of an indoor navigation method in an embodiment of the invention;
FIG. 8 is another flow chart of an indoor navigation method in an embodiment of the invention;
FIG. 9 is another flow chart of an indoor navigation method in an embodiment of the invention;
FIG. 10 is a schematic diagram of surrounding pixels around a candidate point in an embodiment of the invention;
FIG. 11 is a schematic diagram of four point pairs within a circle centered around a feature point in an embodiment of the invention;
FIG. 12 is a schematic view of an indoor navigation device according to an embodiment of the present invention;
FIG. 13 is a schematic diagram of a computer device in accordance with an embodiment of the invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and fully with reference to the accompanying drawings, in which it is evident that the embodiments described are some, but not all embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The indoor navigation method provided by the embodiment of the invention can be applied to an application environment as shown in fig. 1, and the indoor navigation method is applied to an indoor navigation system, wherein the indoor navigation system comprises a client and a server, and the client communicates with the server through a network. The client is also called a client, and refers to a program corresponding to the server for providing local service for the client. The client may be installed on, but is not limited to, various personal computers, notebook computers, smart phones, tablet computers, portable wearable devices, and other computer devices. The server may be implemented as a stand-alone server or as a server cluster composed of a plurality of servers.
In one embodiment, as shown in fig. 2, an indoor navigation method is provided, and the method is applied to the server in fig. 1, and the method includes the following steps:
s10, acquiring WIFI signal intensity of the mobile shooting end between the current position and at least three appointed wireless hotspots.
A wireless hotspot (AP) refers to a terminal that provides a Wireless Local Area Network (WLAN) access to internet services in a public place. The mobile shooting end is a mobile end carrying a wireless hotspot, and the mobile end can send and receive wireless information with the wireless hotspot in the surrounding environment.
The idea of adopting the WIFI signal intensity to perform the position location in the embodiment is to determine the indoor position of the mobile shooting end according to the signal intensity acquired by different wireless hot spots and the indoor layout of the wireless hot spots according to the different signal intensities of the mobile shooting end at different indoor positions and different signal intensities of the wireless hot spots which are not used in the indoor. It can be understood that the more wireless hotspots deployed indoors, the more the server obtains the WIFI signal intensity record through the mobile shooting end, the more accurate the server locates the position of the mobile shooting end. Tests prove that the positioning of the mobile shooting end can be more accurate by indoor deployment of five or six wireless hotspots. An indoor environment in which position location is generally performed through WIFI signal intensity requires one AP to be set every 3 meters, and at least three APs are located indoors. Taking three specified APs as an example in a room, the mobile terminal has three sets of WIFI signals at the current position, and the three sets of WIFI signals form a actually measured WIFI signal cluster at the current position. For convenience of explanation, three wireless hotspots may be deployed indoors in this embodiment, so that the server locates the location of the mobile capturing terminal.
Each AP carries a unique device factory number, i.e. MAC (Medium Access Control) address, that is infused by the manufacturer at the factory, to distinguish between different APs. In this embodiment, the MAC address of each AP may be used as a distinguishing identifier for designating the wireless hotspot.
WIFI signal strength (also called wireless received signal strength, received Signal Strength Indicator, hereinafter referred to as RSSI) ranges from-110 dbm to-20 dbm in a CDMA network. In general, if RSSI < -95dbm, it is indicated that the current network signal coverage is poor and there is little signal; -95dmb < rssi < -90dbm, indicating that the current network signal coverage is very weak; RSSI > 90dbm, which indicates that the current network signal coverage is better. Therefore, the current network coverage level is generally determined by taking-90 dbm as a critical point.
Specifically, in this embodiment, a "location fingerprint" manner is used to correlate the location coordinates of each preset location in the room with the "location fingerprint" RSSI, where each location coordinate corresponds to a unique fingerprint. This fingerprint may be single-dimensional (RSSI of one AP) or multi-dimensional (RSSI of multiple APs). In this embodiment, at least two APs are deployed indoors, so the "fingerprint" corresponding to each fixed point coordinate is multidimensional.
In step S10, the server records the WIFI signal intensities respectively corresponding to the mobile shooting end and at least three designated wireless hotspots indoors, so that the subsequent server can confirm the fixed point coordinates of the mobile shooting end based on the WIFI signal intensities and the "position fingerprint".
S20, acquiring an actually-measured WIFI signal cluster of the mobile shooting end at the current position based on the WIFI signal intensity.
The server records all signal intensities obtained by the mobile shooting end at the current position, and can obtain the actually-measured WIFI signal cluster of the mobile shooting end at the current position.
Specifically, when the server tests and measures the WIFI signal intensity between the mobile shooting end and each wireless hotspot, the server can calculate the sample mean value of all measured WIFI signal intensities for recording after multiple measurements so as to more accurately record the signal intensity. The sample mean value of the signal intensity of each wireless hotspot can be achieved by the following formula:
1/n*∑x(i)
wherein x (i) is a signal intensity value obtained by each measurement when the mobile shooting end measures the signal intensity of the designated wireless hot spot according to the designated collection times, and n is the designated collection times.
The implementation process of the actual measurement WIFI signal cluster is illustrated as follows, as shown in fig. 3:
1. the signal acquisition end sequentially acquires standard signal intensity between the signal acquisition end and each appointed wireless hot spot MAC1, MAC2 and MAC3 at fixed point coordinates (0, 0) corresponding to preset fixed points according to preset acquisition times (which can be set to 10 times), and records:
the actual measurement signal intensity between the acquired fixed point coordinates (0, 0) and the MAC1 is as follows in sequence:
x1, X2, X3, X4, & gt..x 9, and X10, bringing X1 to X10 into the formula 1/n X (i), wherein X (i) = { X1, X2, X3, X4, & gt..x 10}, n=10, gives a sample mean of measured signal intensity between the fixed point coordinates (0, 0) and MAC1 of X.
Similarly, the sample mean value of the measured signal intensity between the fixed point coordinate (0, 0) and the MAC2 is Y, and the sample mean value of the measured signal intensity between the fixed point coordinate (0, 0) and the MAC3 is Z.
2. And combining the fixed point coordinates (0, 0) with the sample mean value of the measured signal intensity between each appointed wireless hotspot to form a measured WIFI signal cluster corresponding to the fixed point coordinates (0, 0) at the current position as (X, Y, Z).
In this embodiment, the server obtains the actually measured WIFI signal cluster of the signal acquisition end at each fixed point coordinate according to the preset acquisition times, so as to obtain the indoor location preparation technology foundation where the actually measured WIFI signal cluster is compared with the training WIFI signal cluster.
S30, comparing the actual measurement WIFI signal clusters with a preset random forest, and acquiring fixed point coordinates corresponding to the mobile shooting end in a preset navigation map as starting point coordinates.
Wherein the random forest is a classifier comprising a plurality of decision trees and the class of the output is a mode of the class output by the individual trees. In this embodiment, each fixed point coordinate corresponds to a decision tree, and the decision tree is a training WIFI signal cluster formed by the fixed point coordinate and a signal intensity mean ("location fingerprint") of each designated AP.
The preset navigation map is a grid map with a coordinate system and preset points (namely grid intersection points) which are preset on a server and are established for an indoor feasible region. Wherein each preset point (i.e. grid intersection) corresponds to a fixed point coordinate in the coordinate system.
In step S30, the server may calculate the euclidean distance between the actually measured WIFI signal cluster and each "position fingerprint" based on the actually measured WIFI signal cluster obtained by the mobile capturing end at the current position and the "position fingerprint" recorded by each tree in the preset random forest, and set the fixed point coordinate with the smallest euclidean distance difference as the starting point coordinate of the mobile capturing end. The positioning mode does not need to install an additional positioning device, the server can acquire the starting point coordinates by moving the signal intensity between the shooting end and each appointed wireless hot spot, the positioning cost is saved, and the positioning mode is stable and reliable.
S40, acquiring an end point coordinate, and generating at least two recommended navigation routes according to the start point coordinate and the end point coordinate.
The destination coordinates are coordinates received by the server and used for determining a destination point which is expected to be reached by the mobile shooting end finally, namely the destination point which is expected to be reached by the mobile shooting end after the mobile shooting end moves along a specified preset fixed point on a preset navigation map. The recommended navigation route is a route starting along each movable direction of the pre-start point coordinates and reaching the end point coordinates, wherein all routes at least comprise a route with the shortest path, as shown in fig. 4.
In step S40, the server may input the start point coordinates and the end point coordinates into the a star algorithm for calculation, and may obtain at least two recommended navigation routes of the mobile capturing end in each movable direction. Among them, the A star algorithm is one of popular heuristic search algorithms, and is widely applied to the field of path optimization. The a-star algorithm is unique in that it introduces global information in the preset navigation map when checking each possible preset point in the shortest path, makes an estimate of the distance of the current starting point coordinates from the end point coordinates, and serves as a measure for evaluating the likelihood that the preset point is on the shortest line.
S50, obtaining obstacle avoidance detection results of the mobile shooting end on at least two recommended navigation routes, selecting the recommended navigation route with the shortest path in the obstacle avoidance detection result as a target navigation route, sending the target navigation route to the mobile shooting end, and controlling the mobile shooting end to move according to the target navigation route.
The target navigation route is a route with no obstacle between the starting point coordinate and the adjacent fixed point coordinate in the movable direction in the recommended navigation route, and the shortest path between the starting point coordinate and the end point coordinate.
In this embodiment, the infrared detector installed on the mobile shooting end is used to detect the obstacle, and the detection distance is limited between the current starting point coordinate and the fixed point coordinate of the adjacent fixed point in the next movable direction, that is, when the infrared detector does not detect the obstacle, it is indicated that no obstacle exists between the starting point coordinate and the adjacent fixed point coordinate in the next movable direction, and the mobile shooting end can be controlled to continue to move from the starting point coordinate to the next fixed point coordinate according to the target navigation route.
In step S50, the server may detect whether an obstacle exists between the start point coordinate and the next fixed point coordinate in the movable direction through an infrared detector installed on the mobile photographing end, and select a recommended navigation route, which does not exist an obstacle and has the shortest path from the start point coordinate to the end point coordinate, as the target navigation route, so as to guide the mobile photographing end to move to the end point coordinate. The step ensures that the mobile shooting end is not influenced by the obstacle in the moving process, and the mobile shooting end smoothly moves from the starting point coordinate to the ending point coordinate to finish the indoor moving task.
In the indoor navigation method provided in steps S10 to S50, the server compares the actually measured WIFI signal cluster acquired by the mobile shooting end at the current position and between the at least three specified wireless hotspots with the preset random forest to acquire the fixed point coordinates corresponding to the mobile shooting end in the preset navigation map as the starting point coordinates, and locates the current position of the mobile shooting end by using the WIFI signal to locate the current position, which is not limited by the hardware detection range, and the locating mode is simple and fast. Meanwhile, the indoor navigation method can plan a target navigation route capable of avoiding the obstacle according to the starting point coordinate and the end point coordinate corresponding to the current position, so that the mobile shooting is short and moves based on the target navigation route, the target navigation route can be adjusted in real time according to road conditions, the navigation process is not influenced by the hardware detection range, and the navigation mode is flexible and reliable.
In an embodiment, the preset navigation map includes at least three preset points, each of the preset points corresponds to a certain point coordinate, as shown in fig. 5, before step S30, that is, before the step of comparing the preset random forest based on the actually measured WIFI signal cluster to obtain the fixed point coordinate corresponding to the mobile shooting end in the preset navigation map as the starting point coordinate, the indoor navigation method further includes the following steps:
S301, acquiring a training WIFI signal cluster formed by the WIFI signal intensity between the signal acquisition end and each appointed wireless hotspot at each preset point according to preset acquisition times.
The WIFI signal cluster training method comprises the steps that when a WIFI signal cluster training stage is used for obtaining standard signal intensity, a mobile shooting end tests each fixed point coordinate to obtain a multidimensional standard signal cluster formed by the WIFI signal intensity between the mobile shooting end and each appointed wireless hotspot.
Specifically, when the standard signal intensity is measured between the mobile shooting end and each wireless hotspot, the sample mean value of all measured signal intensities can be calculated for recording after multiple measurements, so that the standard signal intensity can be recorded more accurately. The sample mean value of the standard signal intensity of each wireless hotspot can be realized by the following formula:
1/n*∑x(i)
wherein x (i) is a standard signal intensity value obtained by each measurement when the mobile shooting end measures standard signal intensity for a specified wireless hotspot according to specified acquisition times, and n is the specified acquisition times.
The implementation process of the training WIFI signal cluster is illustrated as follows, as shown in fig. 3:
1. the signal acquisition end sequentially acquires standard signal intensity between the signal acquisition end and each appointed wireless hot spot MAC1, MAC2 and MAC3 at fixed point coordinates (0, 0) corresponding to preset fixed points according to preset acquisition times (which can be set to 10 times), and records:
The standard signal intensity between the acquired fixed point coordinates (0, 0) and the MAC1 is as follows in sequence:
a1, A2, A3, A4, & a...a.a9, and a10, bringing A1 to a10 into formula 1/n Σx (i), where x (i) = { A1, A2, A3, A4, & a.10 }, n=10, gives a sample mean of standard signal intensity between the fixed point coordinates (0, 0) and MAC1 as a.
Similarly, the sample mean of the standard signal intensity between the fixed point coordinates (0, 0) and the MAC2 is B, and the sample mean of the standard signal intensity between the fixed point coordinates (0, 0) and the MAC3 is C.
2. And combining the fixed point coordinates (0, 0) with the sample mean value of the standard signal intensity between each appointed wireless hotspot to form a training WIFI signal cluster corresponding to the fixed point coordinates (0, 0) at the current position as (A, B and C).
In this embodiment, the server obtains the training WIFI signal cluster of the signal acquisition end at each fixed point coordinate according to the preset acquisition times, so as to perform a comparison preparation technology foundation for the subsequent client to send the actually measured WIFI signal cluster and the training WIFI signal cluster at different fixed point coordinates.
S302, storing corresponding fixed point coordinates and training WIFI signal clusters corresponding to the fixed point coordinates for each preset fixed point in an associated mode so as to form a decision tree corresponding to the fixed point coordinates.
The decision trees are clusters of each type forming a preset random forest, and each decision tree corresponds to one fixed point coordinate and a training WIFI signal cluster corresponding to the fixed point coordinate.
In step S302, the server stores each fixed point coordinate and the training WIFI signal cluster corresponding to the fixed point coordinate in an associated manner, so as to form a decision tree of the fixed point coordinate, which is beneficial to the subsequent server to position and locate the client based on the training WIFI signal, and is simple and fast.
In steps S301 to S302, the server obtains the training WIFI signal cluster of the signal acquisition end at each fixed point coordinate according to the preset acquisition times, and performs a comparison preparation technology foundation for the actually measured WIFI signal cluster and the training WIFI signal cluster of the subsequent client at different fixed point coordinates. And the server carries out association storage on each fixed point coordinate and the training WIFI signal cluster corresponding to the fixed point coordinate to form a decision tree of the fixed point coordinate, so that the subsequent server can position and position the client based on the decision tree, and the method is simple and quick.
In an embodiment, as shown in fig. 6, in step S30, that is, based on comparing the actually measured WIFI signal cluster with the preset random forest, the fixed point coordinates corresponding to the mobile shooting end in the preset navigation map are obtained as the starting point coordinates, which specifically includes the following steps:
s31, calculating the Euclidean distance between the actually measured WIFI signal cluster and each decision tree in the preset random forest, and obtaining the target decision tree with the shortest Euclidean distance.
Wherein, the Euclidean distance is derived from a distance formula between two points x1 and x2 in the N-dimensional Euclidean space:
wherein i is the number of wireless hotspots, X 1i In order to measure the WIFI signal intensity, X of the mobile shooting end at the current position and the ith wireless hot spot 2i The WIFI signal intensity is trained for the wireless hotspots of the mobile shooting end at the current position and the ith station.
The target decision tree is a decision tree with the closest Euclidean distance between a training WIFI signal cluster and an actual measurement WIFI signal cluster in a decision tree in a preset random forest.
The implementation process of calculating the Euclidean distance between the actually measured WIFI signal cluster and each decision tree in the preset random forest is illustrated:
actually measured WIFI signal cluster of mobile shooting end at current position is (X) 11 ,X 12 ,X 13 ) Comparing the actually measured WIFI signal cluster with the training WIFI signal clusters of each decision tree, wherein the training WIFI signal cluster corresponding to the fixed point coordinates (0, 0) is (X) 21 ,X 22 ,X 23 )。
Actual measurement WIFI Signal Cluster (X) 11 ,X 12 ,X 13 ) And training WIFI Signal Cluster (X) 21 ,X 22 ,X 23 ) Carry-over formulaThe euclidean distance d1 may be obtained.
And similarly, the Euclidean distance d 2..dx between the actually measured WIFI signal cluster and each other decision tree can be obtained. Wherein d1 is the smallest value among all Euclidean distances, namely, the training WIFI signal cluster (X 21 ,X 22 ,X 23 ) Is a target decision tree.
In step S31, the server finds the target decision tree closest to the actually measured WIFI signal cluster, so as to facilitate the subsequent confirmation of the current position of the mobile shooting end based on the fixed point coordinates corresponding to the target decision tree.
S32, acquiring fixed point coordinates corresponding to the target decision tree in a preset navigation map as starting point coordinates of the mobile shooting end.
The starting point coordinates are fixed point coordinates corresponding to the current position of the mobile shooting end. In this embodiment, the server also plans a navigation route for the mobile capturing end based on the position of the mobile capturing end, so the fixed point coordinate of the mobile capturing end may also be referred to as the start point coordinate.
In step S32, the server does not need to install other detection hardware, and only obtains the fixed point coordinates corresponding to the target decision tree in the database as the starting point coordinates based on the target decision tree obtained in step S31, so that the current position of the mobile shooting end can be confirmed, the positioning cost is saved, and the positioning mode is stable and reliable and is not influenced by environmental changes.
In steps S31 to S32, the server finds the target decision tree closest to the actually measured WIFI signal cluster, so as to facilitate the subsequent confirmation of the current position of the mobile shooting end based on the fixed point coordinates corresponding to the target decision tree. The server does not need to install other detection hardware, only obtains the fixed point coordinates corresponding to the target decision tree in the database as the starting point coordinates based on the target decision tree obtained in the step S31, and can confirm the current position of the mobile shooting end, so that the positioning cost is saved, and the positioning mode is stable and reliable and is not influenced by environmental changes.
In one embodiment, as shown in fig. 7, in step S40, the destination coordinates are obtained, and at least two recommended navigation routes are generated according to the start coordinates and the destination coordinates, which specifically includes the following steps:
s41, determining a starting point coordinate and an end point coordinate on a preset fixed point navigation map.
The terminal point coordinate is a terminal point reached after the mobile shooting end moves along a specified preset fixed point on a preset navigation map.
In step S41, the server may identify the start point coordinate and the end point coordinate on the preset navigation map, so that a background control personnel of the server can intuitively learn the current position of the mobile shooting end and the end point coordinate to be reached by the current movement.
S42, acquiring at least two recommended navigation routes on a preset fixed-point navigation map by adopting an A star algorithm.
The recommended navigation route is a route that starts to reach the destination coordinate along each movable direction of the pre-start point coordinate, and all the optimal routes at least include a route with the shortest path, as shown in fig. 4.
Specifically, the implementation process of acquiring the recommended navigation route in a movable direction on the preset fixed point navigation map by adopting the A star algorithm is as follows:
setting f=g+h, where F is the shortest path, g=a moving path from the start point coordinates to a preset point where it is currently located;
H=an estimated path moving from the preset fixed point where it is currently located to the end point coordinates.
1. The starting point coordinates are added to a walkable node list (each node is each preset point on a preset navigation map).
2. The following procedure was repeated:
a. traversing the walkable node list, searching the node with the minimum F value, and taking the searched node as the preset fixed point to be processed currently.
b. The preset point is moved to the infeasible list.
c. Analyzing each node of four adjacent nodes of a preset point:
if the neighbor node is unreachable or in a non-reachable list, it is ignored. Otherwise, the following operation is performed:
if the neighbor node is not in the walkable node list, adding the neighbor node to the walkable node list, setting the current node as a parent node, and recording F, G and H values of the node.
If the neighbor node is already in the walkable node list, it is checked whether the path (i.e. reaching the neighbor node via the current node) has a smaller G value. If yes, setting the father node as the current node, and recalculating the G and F values of the current node.
d. When the end point coordinates are added to the walkable node list, the searching of the optimal navigation path is completed at the moment.
3. Starting from the end point coordinates, each node moves along the parent node until the start point coordinates, i.e., the recommended navigation route.
In step S42, the server may acquire the recommended navigation route in each movable direction on the preset fixed point navigation map by using the a star algorithm, so that the following mobile shooting end can replace or adjust the route in real time according to the road condition (such as that an obstacle exists in the road), and the flexibility of the movement of the mobile shooting end is enhanced.
In steps S41 to S42, the server may identify the start point coordinate and the end point coordinate on the preset navigation map, so that a background controller of the server can intuitively learn the current position of the mobile shooting end and the end point coordinate to be reached by the current movement. The server acquires the recommended navigation route in each movable direction on the preset fixed-point navigation map by adopting an A star algorithm, so that the follow-up movable shooting end can replace or adjust the route in real time according to road conditions, and the movement flexibility of the movable shooting end is enhanced.
In an embodiment, the target navigation route includes at least one passing fixed point, as shown in fig. 8, in step S50, the moving capturing end is controlled to move according to the target navigation route, which specifically includes the following steps:
s51, controlling the mobile shooting end to move from the starting point coordinates to the next passing fixed point according to the target navigation route.
The target navigation route is a route with no obstacle between the starting point coordinate and the adjacent fixed point coordinate in the movable direction in the recommended navigation route, and the shortest path between the starting point coordinate and the end point coordinate.
The path fixed point is the next preset fixed point to which the mobile shooting end moves according to the direction of the target navigation route.
In step S51, the server sends the target navigation route to the mobile shooting end through the wireless network, and after the mobile shooting end receives the target navigation route, the mobile shooting end can move from the current position to the fixed point of the next path according to the guidance of the target navigation route, so that the safety and reliability of the mobile shooting end in the moving process are improved.
S52, updating the next passing fixed point to which the mobile shooting end moves into a new starting point coordinate, and if the new starting point coordinate is not an end point coordinate, repeatedly executing the step of acquiring the end point coordinate, and generating at least two recommended navigation routes according to the starting point coordinate and the end point coordinate.
Specifically, since the range of each time the mobile shooting end detects an obstacle is the distance between two preset points, when the mobile shooting end moves to the next path point according to the target navigation route, the safety of the path needs to be continuously determined, that is, the next path point needs to be updated to the starting point coordinate, and whether an obstacle exists between the starting point coordinate and the next path coordinate should be determined again.
It can be understood that when no obstacle exists between the starting point coordinate and the next path fixed point, the mobile shooting end can continue to move according to the target navigation route; when there is an obstacle between the start point coordinates and the next route fixed point, the server is required to re-plan the target navigation route, that is, repeatedly execute the step of obtaining the end point coordinates, and generate at least two recommended navigation routes according to the start point coordinates and the end point coordinates. The steps that are repeatedly executed are identical to steps S50 to S60, and will not be described here again.
In step S52, whenever the mobile shooting end moves to the next path fixed point and does not reach the destination coordinate yet, it is detected whether an obstacle exists between the current position and the next path fixed point, so that flexibility and movement safety of adjusting the target navigation route according to road conditions in real time during the movement process are improved, and smooth arrival of the mobile shooting end at the destination coordinate position can be ensured.
In step S51 to step S52, the server improves the safety and reliability of the mobile shooting end in the moving process by controlling the mobile shooting end to move from the current position to the next preset point according to the target navigation route. When the mobile shooting end moves to the next path fixed point and does not reach the end point coordinate yet, whether an obstacle exists between the current position and the next path fixed point or not is detected, so that the flexibility and the movement safety of adjusting the target navigation route according to road conditions in real time in the moving process are improved, and the mobile shooting end can be ensured to smoothly reach the position of the end point coordinate.
In an embodiment, as shown in fig. 9, after step S40, that is, after the step of obtaining the obstacle avoidance detection result of the mobile capturing end for at least two recommended navigation routes, the indoor navigation method further includes the following steps:
s401, adding 1 to the infeasible times between the starting point coordinates and adjacent preset fixed points along the advancing direction of the target navigation route, and if the infeasible times are larger than a first threshold value, acquiring all historical fixed point images corresponding to the starting point coordinates along the advancing direction of the target navigation route.
Specifically, in this embodiment, in order to update the map according to the indoor road condition in time, the server may control the mobile capturing terminal to capture a ground image (current fixed point image) corresponding to a preset fixed point to form a historical fixed point image when the mobile capturing terminal encounters an obstacle each time it moves to the preset fixed point according to the template navigation route, so as to facilitate the subsequent determination of whether the obstacle permanently has the current route. If the historical fixed point image corresponding to the preset fixed point meets the judging condition, the obstacle can be judged to be permanently present, and the server can set the current route as an infeasible route.
The infeasible times are historical infeasible times corresponding to step routes formed between every two preset points. Every time the server determines that there is an obstacle in the step-size route between the preset fixed point and the next preset fixed point, a record of adding 1 to the step-size route between the preset fixed point and the next preset fixed point adjacent to the preset fixed point is made in the database, so that whether the step-size route is permanently infeasible or not (i.e. whether there is an obstacle permanently between the preset fixed point and the next preset fixed point adjacent to the preset fixed point or not) is determined based on the infeasible number.
The first threshold is the minimum number of times the number of infeasible times reaches which determines whether the step route is permanently infeasible. For example, when the first threshold is 10 and the corresponding number of infeasible times between the preset fixed point and the next preset fixed point adjacent to the preset fixed point reaches 10, the server analyzes the step-size route between the preset fixed points and determines the mobility of the step-size route.
The historical fixed point images are obtained by the server, and each time the step-length route between the preset fixed point and the next preset fixed point is judged to have an obstacle, the server can keep the current fixed point image shot based on the preset fixed point in the database according to the direction, so that all the historical fixed point images corresponding to the preset fixed point can be screened out based on different directions for analysis. The current fixed point image is a ground image which is shot by the mobile shooting end according to the forward direction and comprises a preset fixed point where the current fixed point is located and a next preset fixed point. And each current fixed point image is stored in a database after being shot, namely, a historical fixed point image of a preset fixed point corresponding to the current fixed point image is formed.
In step S401, when the server determines that the number of non-feasible times corresponding to the step route between adjacent preset points is greater than the first threshold, the server shall perform mobility analysis on the step route to determine whether the obstacle existing on the step route is permanently existing.
S402, calculating the image similarity corresponding to any two history fixed-point images in all the history fixed-point images by adopting a feature extraction algorithm.
The image similarity is the similarity obtained by comparing any two historical fixed point images corresponding to the preset fixed point through a feature extraction algorithm.
Specifically, a feature extraction algorithm is adopted to obtain a first image feature and a second image feature corresponding to any two history fixed-point images.
Further, the process of calculating the image similarity corresponding to any two history fixed point images in all the history fixed point images by adopting the feature extraction algorithm is as follows:
1. and respectively extracting first characteristic points in any one history fixed-point image.
Extracting the first feature point of the history fixed-point image includes: and setting points with more remarkable historic fixed-point images, such as contour points, bright points in darker areas, dark points in lighter areas and the like as candidate points, detecting pixel values on circles with appointed selection radiuses around the candidate points, and if enough pixel points in the field around the candidate points have enough differences from the gray values of the candidate points, considering the candidate points as first characteristic points.
In order to obtain faster results, the following detection acceleration method may also be employed: the gray value difference between at least 3 points around the test candidate point and the candidate point is enough, if not, other points are not needed to be calculated, and the candidate point is not considered as the characteristic point directly. The radius of the circle around the candidate point is an important parameter, and for simplicity and efficiency, the detection radius can be specified to be 3, and then there are 16 peripheral pixels to be compared, as shown in fig. 10. To increase the efficiency of the comparison, only N surrounding pixels are typically used for comparison, namely FAST-N, FAST-9 is generally recommended.
The second feature point of the other history fixed-point image is extracted in accordance with the process of extracting the first feature point, and will not be described here again.
2. And respectively calculating and storing the feature point descriptors of each history fixed-point image.
Calculating a feature point descriptor of a history fixed-point image includes: the feature points of the history fixed-point image need to be described in some way after they are obtained. The attribute output of the feature point is a descriptor (Feature DescritorS) of the feature point. The ORB algorithm acquires the attribute of the feature point by the following steps:
(1) And taking the characteristic point P as a circle center and d as a radius to make a circle O.
(2) N point pairs are selected within the circle O. For convenience of explanation, in this embodiment, n=4 may be selected, as shown in fig. 11, where N may be 512 in practical application.
The 4 point pairs currently selected are respectively marked as follows:
P 1 (A,B)、P 2 (A,B)、P 3 (A, B) and P 4 (A,B)。
(3) Definition of T-operations
Wherein I is A Represents the gray scale of point A, I B The gray scale of point B is represented.
(4) And respectively carrying out T operation on the selected point pairs, and combining the obtained results. The description is described with the four points as continuing to be the description:
T(P 1 (A,B))＝1
T(P 2 (A,B))＝0
T(P 3 (A,B))＝1
T(P 4 (A,B))＝1
the final descriptor of the feature point P is 1011.
3. And comparing the feature point descriptors of any two history fixed-point images to obtain the image similarity. The process of comparing feature point descriptors of any two historical fixed point images is illustrated:
A characteristic point descriptor A of a history fixed-point image: 10101011
Feature point descriptor B for another history fixed point image: 10101010
In this example, only the last bit of A and B is different, and the image similarity is 87.5%. The image similarity of any two historical fixed point images can be calculated in sequence according to the steps.
In step S402, the server may record the similarity of any two of the history fixed-point images corresponding to the preset fixed-point, so as to determine whether the feature points corresponding to any two history fixed-point images are similar.
S403, if the similarity of each image is not smaller than a second threshold value, updating the route between the starting point coordinates and the adjacent preset fixed points into an infeasible route on the preset navigation map.
The second threshold is the minimum percentage of the image features described by the image features of any two history fixed-point images to be the same feature point.
In step S403, the server compares the second threshold value with each image similarity recorded in step S402, and when each image similarity is not smaller than the second threshold value, it indicates that the image features in each history fixed-point image depict the same feature point, so as to determine that there is an obstacle including the feature point on the current route for a long time. The route between the current starting point coordinate and the adjacent preset fixed point is not feasible temporarily, and the route should be updated to be a non-feasible route in the preset navigation map.
In steps S401 to S403, the server may determine whether the server should perform mobility analysis on the step route by determining the number of non-viable times corresponding to the step route between adjacent preset points, so as to determine whether the obstacle existing on the step route is permanently present. When the server confirms that the obstacle exists permanently, the infeasible route can be updated on the preset navigation map, so that the navigation flexibility is enhanced, and the accuracy of the navigation route is improved.
In the indoor navigation method provided by the embodiment, the server compares the actually measured WIFI signal cluster acquired by the mobile shooting end at the current position and at least three appointed wireless hotspots with the preset random forest to acquire the fixed point coordinates corresponding to the mobile shooting end in the preset navigation map as the starting point coordinates, and the current position of the mobile shooting end is positioned by adopting the WIFI signal positioning method, so that the method is not limited by the hardware detection range, and the positioning mode is simple and quick. Meanwhile, the indoor navigation method, the indoor navigation device, the computer equipment and the storage medium can plan a target navigation route capable of avoiding the obstacle according to the starting point coordinate and the end point coordinate corresponding to the current position, so that the mobile shooting time is enabled to move based on the target navigation route, the target navigation route can be adjusted in real time according to road conditions, the navigation process is not influenced by the hardware detection range, and the navigation mode is flexible and reliable.
Further, the server obtains the training WIFI signal clusters of the signal acquisition end at each fixed point coordinate according to the preset acquisition times, and performs a comparison preparation technology foundation for the actually measured WIFI signal clusters and the training WIFI signal clusters of the subsequent client end at different fixed point coordinates. And the server carries out association storage on each fixed point coordinate and the training WIFI signal cluster corresponding to the fixed point coordinate to form a decision tree of the fixed point coordinate, so that the subsequent server can position and position the client based on the training WIFI signal, and the method is simple and quick. The server finds the target decision tree closest to the actually measured WIFI signal cluster, and is beneficial to confirming the current position of the mobile shooting end based on the fixed point coordinates corresponding to the target decision tree. The server does not need to install other detection hardware, only obtains the fixed point coordinates corresponding to the target decision tree in the database as the starting point coordinates based on the target decision tree obtained in the step S31, and can confirm the current position of the mobile shooting end, so that the positioning cost is saved, and the positioning mode is stable and reliable and is not influenced by environmental changes. The server can respectively mark the starting point coordinates and the end point coordinates on a preset navigation map, so that background control personnel of the server can intuitively know the current position of the mobile shooting end and the end point coordinates to be reached by the current movement. The server acquires the recommended navigation route in each movable direction on the preset fixed-point navigation map by adopting an A star algorithm, so that the follow-up movable shooting end can replace or adjust the route in real time according to road conditions, and the movement flexibility of the movable shooting end is enhanced. The server controls the mobile shooting end to move from the current position to the next preset point according to the target navigation route, so that the safety and reliability of the mobile shooting end in the moving process are improved. When the mobile shooting end moves to the next path fixed point and does not reach the end point coordinate yet, whether an obstacle exists between the current position and the next path fixed point or not is detected, so that the flexibility and the movement safety of adjusting the target navigation route according to road conditions in real time in the moving process are improved, and the mobile shooting end can be ensured to smoothly reach the position of the end point coordinate. The server may determine whether the server should perform mobility analysis on the step-size route by determining an infeasible number of times corresponding to the step-size route between adjacent preset points to determine whether an obstacle present on the step-size route is permanently present. When the server confirms that the obstacle exists permanently, the infeasible route can be updated on the preset navigation map, so that the navigation flexibility is enhanced, and the accuracy of the navigation route is improved.
In an embodiment, an indoor navigation device is provided, and the indoor navigation device corresponds to the indoor navigation method in the embodiment one by one. As shown in fig. 12, the indoor navigation device includes an acquisition signal strength module 10, an acquisition WIFI signal cluster module 20, an acquisition start point coordinate module 30, an acquisition end point coordinate module 40, and an acquisition navigation route module 50. The functional modules are described in detail as follows:
the signal strength acquisition module 10 is configured to acquire WIFI signal strength between a current position of the mobile capturing terminal and at least three specified wireless hotspots.
The WIFI signal cluster acquisition module 20 is used for acquiring the actually-measured WIFI signal cluster of the mobile shooting end at the current position based on the WIFI signal intensity.
The starting point coordinate acquisition module 30 is configured to compare the actual measurement WIFI signal cluster with a preset random forest, and acquire a fixed point coordinate corresponding to the mobile shooting end in a preset navigation map as a starting point coordinate.
The terminal coordinate acquiring module 40 is configured to acquire terminal coordinates, and generate at least two recommended navigation routes according to the start point coordinates and the terminal coordinates.
The navigation route obtaining module 50 is configured to obtain obstacle avoidance detection results of the mobile shooting end on at least two recommended navigation routes, select a recommended navigation route with a shortest path and an unobstructed state as a target navigation route, send the target navigation route to the mobile shooting end, and control the mobile shooting end to move according to the target navigation route.
Preferably, the indoor navigation device further comprises a training signal cluster acquisition module 301 and a decision tree formation module 302.
The training signal cluster acquisition module 301 is configured to acquire, at each preset point, a training WIFI signal cluster formed by WIFI signal intensities between the signal acquisition end and each designated wireless hotspot according to a preset number of acquisitions.
A decision tree module 302 is formed, configured to store, for each preset fixed point association, a corresponding fixed point coordinate and a training WIFI signal cluster corresponding to the fixed point coordinate, so as to form a decision tree corresponding to the fixed point coordinate.
Preferably, the acquisition origin coordinate module 30 includes an acquisition target decision tree unit 31 and an acquisition origin coordinate unit 32.
The target decision tree obtaining unit 31 is configured to calculate the euclidean distance between the actually measured WIFI signal cluster and each decision tree in the preset random forest, and obtain the target decision tree with the shortest euclidean distance.
The start point coordinate acquiring unit 32 is configured to acquire a fixed point coordinate corresponding to the target decision tree in the preset navigation map as a start point coordinate of the mobile shooting end.
Preferably, the acquisition end point coordinate module 40 includes a determination start point coordinate unit 41 and an acquisition recommended route unit 42.
A start point coordinate determination unit 41 for determining start point coordinates and end point coordinates on a preset fixed point navigation map.
The recommended route acquisition unit 42 is configured to acquire at least two recommended navigation routes on a preset fixed-point navigation map using an a-star algorithm.
Preferably, the navigation route acquisition module 50 includes a control mobile shooting end module 51 and a recommended route generation module 52.
The control mobile shooting end module 51 is configured to control the control mobile shooting end to move from the start point coordinate to the next route point according to the target navigation route.
The recommended route generation module 52 is configured to update a next route fixed point to which the mobile capturing terminal moves to a new start point coordinate, and if the new start point coordinate is not an end point coordinate, repeatedly perform the step of acquiring the end point coordinate, and generate at least two recommended navigation routes according to the start point coordinate and the end point coordinate.
Preferably, the indoor navigation device further includes an acquisition history image module 501, an acquisition image similarity module 502, and an update infeasible route module 503.
The history image acquiring module 501 is configured to add 1 to the number of infeasibilities between the starting point coordinate and adjacent preset points along the advancing direction of the target navigation route, and if the number of infeasibilities is greater than a first threshold, acquire all the history fixed point images corresponding to the starting point coordinate along the advancing direction of the target navigation route.
The image similarity obtaining module 502 is configured to calculate image similarity corresponding to any two history fixed-point images in all the history fixed-point images by using a feature extraction algorithm.
The update infeasible route module 503 is configured to update the route between the start point coordinate and the adjacent preset fixed point to the infeasible route in the preset navigation map if the similarity of each image is not less than the second threshold.
For specific limitations of the indoor navigation device, reference may be made to the above limitation of the indoor navigation method, and the detailed description thereof will be omitted. The respective modules in the indoor navigation device described above may be implemented in whole or in part by software, hardware, and combinations thereof. The above modules may be embedded in hardware or may be independent of a processor in the computer device, or may be stored in software in a memory in the computer device, so that the processor may call and execute operations corresponding to the above modules.
In one embodiment, a computer device is provided, which may be a server, and the internal structure of which may be as shown in fig. 13. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer equipment is used for storing data to be stored in the indoor navigation method. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program is executed by a processor to implement an indoor navigation method.
In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, where the processor executes the computer program to implement the steps of the map construction method of the above embodiment, such as steps S10 to S50 shown in fig. 2. Alternatively, the processor when executing the computer program implements the functions of the respective modules/units of the map construction apparatus in the above embodiment, such as the functions of the modules 10 to 50 shown in fig. 12. To avoid repetition, no further description is provided here.
In an embodiment, a computer readable storage medium is provided, on which a computer program is stored, which when executed by a processor implements the map construction method of the above embodiment, such as steps S10 to S50 shown in fig. 2. Alternatively, the computer program, when executed by the processor, performs the functions of the modules/units of the map construction apparatus in the apparatus embodiment described above, such as the functions of the modules 10 to 50 shown in fig. 12. To avoid repetition, no further description is provided here.
Those skilled in the art will appreciate that implementing all or part of the above described methods may be accomplished by way of a computer program stored on a non-transitory computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous Link DRAM (SLDRAM), memory bus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-described division of the functional units and modules is illustrated, and in practical application, the above-described functional distribution may be performed by different functional units and modules according to needs, i.e. the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-described functions.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention, and are intended to be included in the scope of the present invention.

Claims (10)

1. An indoor navigation method, comprising:
acquiring WIFI signal intensity of a mobile shooting end between a current position and at least three appointed wireless hotspots;
Acquiring an actually measured WIFI signal cluster of the mobile shooting end at the current position based on the WIFI signal intensity;
comparing the actual measurement WIFI signal cluster with a preset random forest to obtain a fixed point coordinate corresponding to the mobile shooting end in a preset navigation map as a starting point coordinate;
acquiring an end point coordinate, and generating at least two recommended navigation routes according to the start point coordinate and the end point coordinate;
the method comprises the steps of obtaining obstacle avoidance detection results of a mobile shooting end on at least two recommended navigation routes, selecting a recommended navigation route which is in an obstacle-free state and has the shortest distance as a target navigation route, sending the target navigation route to the mobile shooting end, and controlling the mobile shooting end to move according to the target navigation route;
the method comprises the steps that a preset navigation map is a grid map which is preset in a server and is built for an indoor feasible region and provided with a coordinate system and preset fixed points, each preset fixed point corresponds to one fixed point coordinate in the coordinate system, the terminal point coordinate is an end point which is expected to be reached after the mobile shooting end moves along the appointed preset fixed point on the preset navigation map, the recommended navigation route is a route which starts along each movable direction of the starting point coordinate and reaches the terminal point coordinate, at least one route with the shortest route is included in all recommended navigation routes, no obstacle exists between the starting point coordinate and the adjacent fixed point coordinate in the movable direction in the recommended navigation route, and the route with the shortest route from the starting point coordinate to the terminal point coordinate is obtained.
2. The indoor navigation method of claim 1, wherein the preset navigation map includes at least three preset points, each corresponding to a certain point coordinate;
before the step of comparing the actual measurement WIFI signal cluster with a preset random forest to obtain the fixed point coordinate corresponding to the mobile shooting end in a preset navigation map as a starting point coordinate, the indoor navigation method further comprises the following steps:
acquiring a training WIFI signal cluster formed by the WIFI signal intensity between a signal acquisition end and each appointed wireless hotspot according to preset acquisition times at each preset fixed point;
and storing corresponding fixed point coordinates and training WIFI signal clusters corresponding to the fixed point coordinates for each preset fixed point association to form a decision tree corresponding to the fixed point coordinates.
3. The indoor navigation method according to claim 1, wherein the obtaining, based on the actually measured WIFI signal cluster versus a preset random forest, a fixed point coordinate corresponding to the mobile shooting end in a preset navigation map as a starting point coordinate includes:
calculating the Euclidean distance between the actually measured WIFI signal cluster and each decision tree in a preset random forest, and obtaining a target decision tree with the shortest Euclidean distance;
And acquiring a fixed point coordinate corresponding to the target decision tree in the preset navigation map as a starting point coordinate of the mobile shooting end.
4. The indoor navigation method of claim 1, wherein the obtaining the destination coordinates, generating at least two recommended navigation routes according to the start coordinates and the destination coordinates, comprises:
determining the starting point coordinates and the ending point coordinates on the preset fixed point navigation map;
and acquiring at least two recommended navigation routes on the preset fixed point navigation map by adopting an A star algorithm.
5. The indoor navigation method of claim 1, wherein the target navigation route includes at least one route point;
the controlling the mobile shooting end to move according to the target navigation route comprises the following steps:
controlling the mobile shooting end to move from a starting point coordinate to a next passing fixed point according to the target navigation route;
updating the next passing fixed point to which the mobile shooting end moves into a new starting point coordinate, and if the new starting point coordinate is not the terminal point coordinate, repeatedly executing the step of acquiring the terminal point coordinate, and generating at least two recommended navigation routes according to the starting point coordinate and the terminal point coordinate.
6. The indoor navigation method of claim 1, wherein after the step of obtaining obstacle avoidance detection results of the mobile capturing end for at least two recommended navigation routes, the indoor navigation method further comprises:
adding 1 to the number of non-feasible times between the starting point coordinates and adjacent preset fixed points along the advancing direction of the target navigation route, and if the number of non-feasible times is greater than a first threshold value, acquiring all historical fixed point images corresponding to the starting point coordinates along the advancing direction of the target navigation route;
calculating the image similarity corresponding to any two history fixed point images in all the history fixed point images by adopting a feature extraction algorithm;
and if the similarity of each image is not smaller than a second threshold value, updating the route between the starting point coordinates and the adjacent preset fixed points into an infeasible route on the preset navigation map.
7. An indoor navigation device, comprising:
the signal intensity acquisition module is used for acquiring the WIFI signal intensity between the current position of the mobile shooting end and at least three appointed wireless hotspots;
the WIFI signal cluster acquisition module is used for acquiring an actual measurement WIFI signal cluster of the mobile shooting end at the current position based on the WIFI signal intensity;
The starting point coordinate acquisition module is used for comparing the actual measurement WIFI signal cluster with a preset random forest to acquire a corresponding fixed point coordinate of the mobile shooting end in a preset navigation map as a starting point coordinate;
the terminal coordinate acquisition module is used for acquiring terminal coordinates and generating at least two recommended navigation routes according to the starting point coordinates and the terminal coordinates;
the navigation route acquisition module is used for acquiring obstacle avoidance detection results of the mobile shooting end on at least two recommended navigation routes, selecting a recommended navigation route which is in an obstacle-free state and has the shortest route as a target navigation route, sending the target navigation route to the mobile shooting end, and controlling the mobile shooting end to move according to the target navigation route;
the method comprises the steps that a preset navigation map is a grid map which is preset in a server and is built for an indoor feasible region and provided with a coordinate system and preset fixed points, each preset fixed point corresponds to one fixed point coordinate in the coordinate system, the terminal point coordinate is an end point which is expected to be reached after the mobile shooting end moves along the appointed preset fixed point on the preset navigation map, the recommended navigation route is a route which starts along each movable direction of the starting point coordinate and reaches the terminal point coordinate, at least one route with the shortest route is included in all recommended navigation routes, no obstacle exists between the starting point coordinate and the adjacent fixed point coordinate in the movable direction in the recommended navigation route, and the route with the shortest route from the starting point coordinate to the terminal point coordinate is obtained.
8. The indoor navigation device of claim 7, wherein the indoor navigation device further comprises:
the training signal cluster acquisition module is used for acquiring a training WIFI signal cluster formed by the WIFI signal intensity between the signal acquisition end and each appointed wireless hotspot according to the preset acquisition times at each preset fixed point;
the signal average value obtaining module is used for forming a decision tree module and storing corresponding fixed point coordinates and training WIFI signal clusters corresponding to the fixed point coordinates for each preset fixed point in a correlated mode so as to form a decision tree corresponding to the fixed point coordinates.
9. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the indoor navigation method according to any one of claims 1-6 when the computer program is executed.
10. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the indoor navigation method according to any one of claims 1 to 6.
CN201811008385.0A 2018-08-31 2018-08-31 Indoor navigation method, device, computer equipment and storage medium Active CN109239659B (en)

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CN112947434B (en) * 2021-02-04 2024-02-27 南京师范大学 Robot path planning method based on local optimal convolution evaluation
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CN114509072A (en) * 2022-01-06 2022-05-17 珠海格力电器股份有限公司 Indoor navigation method, device, storage medium, mobile terminal and system
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