CN110967028B - Navigation map construction method and device, robot and storage medium - Google Patents
Navigation map construction method and device, robot and storage medium Download PDFInfo
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Abstract
The application is applicable to the technical field of computers, and provides a navigation map construction method which comprises the steps of obtaining an environment map of a preset area, and determining a target navigation path based on the environment map; determining at least two path nodes corresponding to the target navigation path; determining road information of the target navigation path based on node information of two adjacent path nodes respectively; and fusing the road information and the environment map to obtain the navigation map of the preset area. The method comprises the steps of determining a target navigation path based on an environment map of a preset area, fusing road information of the determined target navigation path based on node information of two adjacent path nodes of the target navigation path with the environment map to obtain the navigation map of the preset area, fusing the road information into the environment map, and providing accurate road information for the robot in the navigation process.
Description
Technical Field
The application belongs to the technical field of computers, and particularly relates to a navigation map construction method and device, a robot and a storage medium.
Background
At present, a common robot navigation map is a grid map, and the grid map is constructed by laser radar or vision, so that accurate road information cannot be provided except position information of the current environment where the robot is located.
Disclosure of Invention
In view of this, embodiments of the present application provide a navigation map construction method, an apparatus, a robot, and a storage medium, so as to solve the problem that in the prior art, a navigation map of a robot can only provide location information of an environment where the robot is located, and cannot accurately provide road information.
A first aspect of an embodiment of the present application provides a navigation map construction method, including:
acquiring an environment map of a preset area, and determining a target navigation path based on the environment map;
determining at least two path nodes corresponding to the target navigation path;
determining road information of the target navigation path based on node information of two adjacent path nodes respectively;
and fusing the road information and the environment map to obtain the navigation map of the preset area.
In an optional implementation manner, at least two pieces of node identification information are preset on the target navigation path;
the determining at least two path nodes corresponding to the target navigation path includes:
and determining at least two path nodes corresponding to the target navigation path based on at least two pieces of node identification information.
In an optional implementation manner, the path node is a node that needs to perform path selection or has a path turn, any two adjacent path nodes are located in the same path, and the node information includes node coordinate information;
the determining the road information of the target navigation path based on the node information of two adjacent path nodes respectively includes:
determining a first position coordinate corresponding to each segmentation point of a target line segment based on the node coordinate information of two adjacent path nodes, wherein the target line segment is a connecting line segment between any two adjacent path nodes;
converting each first position coordinate into a corresponding second position coordinate, wherein the coordinate axis of the first position coordinate and the coordinate axis of the second position coordinate are positioned on the same plane and are perpendicular to each other;
and determining the road information of the target navigation path based on the likelihood value corresponding to each second position coordinate.
In an optional implementation manner, the determining, based on the node coordinate information of two adjacent path nodes, a first position coordinate corresponding to each segmentation point of a target line segment includes:
performing equal-distance segmentation on a target line segment based on a preset distance interval to obtain a plurality of segmentation points, wherein the target line segment is a connection line segment between any two adjacent path nodes;
and determining a first position coordinate corresponding to each segmentation point based on the node coordinate information and the distance interval of two adjacent path nodes.
In an optional implementation manner, the respectively converting each first position coordinate into a corresponding second position coordinate includes:
and respectively converting each first position coordinate into a corresponding second position coordinate according to a vertical slope, the distance interval and the total number of the segmentation points, wherein the vertical slope is the slope of a vertical line segment vertical to the target line segment.
In an optional implementation manner, before the determining the road information of the target navigation path based on the likelihood value corresponding to each of the second position coordinates, the method includes:
and analyzing the second position coordinates respectively according to a preset maximum likelihood analysis model to obtain likelihood values corresponding to the second position coordinates.
In an alternative implementation, the road information includes road boundary information;
the determining the road information of the target navigation path based on the likelihood value corresponding to each second position coordinate includes:
if the likelihood value corresponding to the current second position coordinate is greater than a preset first likelihood threshold, taking the position corresponding to the current second position coordinate as the boundary position of the road;
determining the road boundary information according to all the boundary positions;
in an alternative implementation, the road information includes road width information;
the determining the road information of the target navigation path based on the likelihood value corresponding to each second position coordinate includes:
acquiring all effective likelihood values, wherein the effective likelihood values are the likelihood values smaller than a preset second likelihood threshold value;
and determining the road width information according to the total number of the effective likelihood values and the preset distance interval.
A second aspect of the embodiments of the present application provides a navigation map constructing apparatus, including:
the system comprises an acquisition module, a navigation module and a navigation module, wherein the acquisition module is used for acquiring an environment map of a preset area and determining a target navigation path based on the environment map;
the first determining module is used for determining at least two path nodes corresponding to the target navigation path;
the second determining module is used for determining the road information of the target navigation path based on the node information of two adjacent path nodes;
and the obtaining module is used for fusing the road information and the environment map to obtain a navigation map of the preset area.
In an optional implementation manner, at least two pieces of node identification information are preset on the target navigation path;
the first determining module is specifically configured to:
and determining at least two path nodes corresponding to the target navigation path based on at least two pieces of node identification information.
In an optional implementation manner, the path node is a node that needs to perform path selection or has a path turn, any two adjacent path nodes are located in the same path, and the node information includes node coordinate information;
the second determining module includes:
the first determining unit is used for determining a first position coordinate corresponding to each segmentation point of a target line segment, wherein the target line segment is a connecting line segment between any two adjacent path nodes;
the conversion unit is used for respectively converting each first position coordinate into a corresponding second position coordinate, and the coordinate axis of the first position coordinate and the coordinate axis of the second position coordinate are positioned on the same plane and are mutually vertical;
and a second determining unit configured to determine road information of the target navigation path based on the likelihood value corresponding to each of the second position coordinates.
In an optional implementation manner, the first determining unit includes:
the obtaining subunit is configured to perform equidistant segmentation on a target line segment based on a preset distance interval to obtain a plurality of segmentation points, where the target line segment is a connection line segment between any two adjacent path nodes;
and the first determining subunit is configured to determine, based on the node coordinate information and the distance interval of two adjacent path nodes, a first position coordinate corresponding to each of the division points.
In an optional implementation manner, the conversion unit is specifically configured to:
and respectively converting each first position coordinate into a corresponding second position coordinate according to a vertical slope, the distance interval and the total number of the segmentation points, wherein the vertical slope is the slope of a vertical line segment vertical to the target line segment.
In an optional implementation manner, the second determining module further includes:
and the obtaining unit is used for analyzing the second position coordinates according to a preset maximum likelihood analysis model to obtain likelihood values corresponding to the second position coordinates.
In an alternative implementation, the road information includes road boundary information;
the second determination unit includes:
a first determining unit, configured to, if a likelihood value corresponding to the current second position coordinate is greater than a preset first likelihood threshold, take a position corresponding to the current second position coordinate as a boundary position of the road;
the second determining subunit is used for determining the road boundary information according to all the boundary positions;
in an alternative implementation, the road information includes road width information;
the second determination unit includes:
the obtaining subunit is configured to obtain all valid likelihood values, where the valid likelihood values are the likelihood values smaller than a preset second likelihood threshold;
and the third determining subunit is used for determining the road width information according to the total number of the effective likelihood values and the preset distance interval.
A third aspect of the embodiments of the present application provides a robot, including a memory, a processor, and a computer program stored in the memory and executable on the processor, where the processor implements the steps of the navigation map construction method according to the first aspect of the above embodiments when executing the computer program.
A fourth aspect of the embodiments of the present application provides a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, the computer program implements the steps of the navigation map constructing method according to the first aspect of the embodiments.
Compared with the prior art, the embodiment provided by the first aspect of the application has the advantages that the target navigation path is determined through the environment map based on the preset region, the node information of two adjacent path nodes based on the target navigation path is determined, the road information of the target navigation path is determined, the environment map is fused, the navigation map of the preset region is obtained, the road information can be fused into the environment map, and accurate road information is provided for the robot in the navigation process.
The embodiments provided in the second aspect to the fourth aspect of the present application have the same beneficial effects as the embodiments provided in the first aspect of the present application compared with the prior art, and are not described herein again.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
FIG. 1 is a flowchart illustrating an implementation of a navigation map construction method according to a first embodiment of the present application;
FIG. 2 is a flowchart illustrating an implementation of S103 in FIG. 1 in an embodiment;
fig. 3 is a flowchart of a specific implementation of S1031 in fig. 2;
FIG. 4 is a flow chart of a specific implementation of 103 in another embodiment of FIG. 1;
FIG. 5 is a flowchart illustrating an implementation of S1033 in an embodiment of FIG. 2;
FIG. 6 is a flowchart of a specific implementation of S1033 in another embodiment of FIG. 2;
FIG. 7 is a schematic structural diagram of a navigation map construction apparatus provided in an embodiment of the present application;
fig. 8 is a schematic structural diagram of a robot according to a fourth embodiment of the present application.
Detailed Description
In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular system structures, techniques, etc. in order to provide a thorough understanding of the embodiments of the present application. It will be apparent, however, to one skilled in the art that the present application may be practiced in other embodiments that depart from these specific details. In other instances, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present application with unnecessary detail.
It should be noted that, at present, the service robot basically needs to move by depending on an environment map, wherein, a common environment such as a grid map, a feature map, a topology map, etc. is usually marked with clear position information in the environment map, and the robot can accurately reach a destination according to the environment map. However, as the road where the robot walks usually has some other resistance, for example, temporary obstacles, other robots simultaneously driving, etc., the current environment map has the defect of insufficient information, so that the robot cannot accurately avoid the obstacles.
Therefore, if the visual features of the preset area can be fused into the environment map, so that the road information and the environment map are fused, accurate road information can be provided for the robot in the navigation process.
In order to explain the technical solution described in the present application, the following description will be given by way of specific examples. As shown in fig. 1, it is a flowchart of an implementation of a navigation map construction method provided in a first embodiment of the present application, and this embodiment may be implemented by software or hardware inside a robot. The details are as follows:
s101, obtaining an environment map of a preset area, and determining a target navigation path based on the environment map.
It can be understood that, during the walking process, the robot needs to continuously call the preset environment map to determine the target navigation path, which is the target walking path.
In this embodiment, the environment map may be any one of a grid map, a feature map, a topological map, and the like, the preset area is a pre-planned area to be entered, and the environment map includes geographical location information of the preset area.
S102, determining at least two path nodes corresponding to the target navigation path.
And selecting at least two nodes on the target navigation path in advance for marking to generate node identification information. The node identification information may be code characters, numbers, letters, or the like in a preset order, and is used to uniquely identify each of the path nodes.
By way of example and not limitation, each of the path nodes is undirected, all of the path nodes are similar to a topological graph, and thus the environment map labeled with path nodes may be referred to as an undirected topological map. In summary, the target navigation path is preset with node identification information of at least two path nodes;
the S102 specifically includes: and determining at least two path nodes corresponding to the target navigation path based on at least two pieces of node identification information.
S103, determining road information of the target navigation path based on the node information of two adjacent path nodes.
The path node is a node that needs to perform path selection or has a path turn, for example, the path node is located at an intersection or a t-intersection, it should be noted that any two adjacent path nodes are located on the same path, and the node information includes node coordinate information.
As shown in fig. 2, which is a flowchart of a specific implementation of S103 in an embodiment of fig. 1, as can be seen from fig. 2, S103 includes S1031 to S1033, which are detailed as follows:
and S1031, determining a first position coordinate corresponding to each segmentation point of a target line segment, wherein the target line segment is a connecting line segment between any two adjacent path nodes.
Connecting any two adjacent path nodes to generate the target line segment; it is to be understood that the target line segment is a line segment resembling a straight line; for example, if any two adjacent path nodes are located on a flat path, the target line segment is a straight line; and if any two adjacent path nodes are positioned on a path with a slope, the target line segment is an oblique line. Therefore, in this embodiment, in order to more accurately determine the road information between any two adjacent path nodes based on any two adjacent path nodes, the target line segment is equally divided at preset division intervals, and the first position coordinates corresponding to each division point of the target line segment are determined.
Specifically, as shown in fig. 3, it is a flowchart of a specific implementation of S1031 in fig. 2. As can be seen from fig. 3, S1031 includes S301 to S302, which are detailed as follows:
s301, performing equal-distance segmentation on the target line segment based on a preset distance interval to obtain a plurality of segmentation points.
For example, any two adjacent path nodes are a node a and a node b, the preset distance interval is 0.05, and a target line segment formed by the node a and the node b is divided at equal intervals at the distance interval of 0.05 to obtain a plurality of division points.
S302, determining a first position coordinate corresponding to each segmentation point based on the node coordinate information and the distance interval of two adjacent path nodes.
And S1032, respectively converting each first position coordinate into a corresponding second position coordinate, wherein the coordinate axis of the first position coordinate and the coordinate axis of the second position coordinate are positioned on the same plane and are perpendicular to each other.
Specifically, in an optional implementation manner, S1032 includes:
and respectively converting each first position coordinate into a corresponding second position coordinate according to a vertical slope, the distance interval and the total number of the segmentation points, wherein the vertical slope is the slope of a vertical line segment vertical to the target line segment.
For example, assume that the node coordinate information of the node a is a (x)1,y1) The node coordinate information of the node b is b (x)2,y2) If the slope of the target line segment formed by the node a and the node b is:
the slope of a vertical segment perpendicular to the target segment is:
α＝θ-3.14/2
as an example and not by way of limitation, substituting the vertical slope, the distance interval, and the total number of the division points into a preset coordinate conversion formula to obtain the second position coordinate corresponding to each first position coordinate; the preset coordinate conversion formula is as follows:
xl＝xl1+kncosα
yl＝yl1+knsinα
wherein x islSecond position coordinates corresponding to the ith division pointY is the ordinate of the second position coordinate corresponding to the ith division point, xl1Is the abscissa, y, of the first position coordinate corresponding to the first second position coordinatel1The first position coordinate corresponding to the ith second position coordinate is a vertical coordinate, k is the total number of the following points, n is the preset distance interval, and alpha is the vertical slope.
S1033, determining road information of the target navigation path based on the likelihood value corresponding to each of the second position coordinates.
Specifically, the likelihood value is a maximum likelihood value used for evaluating the maximum of the second position coordinate containing the preset road information, the value range of the maximum likelihood value is 0-1, and generally the closer to 1, the closer to the preset information is indicated.
It can be understood that the likelihood value corresponding to the second position coordinate may be preset or obtained according to the second position coordinate; specifically, in an alternative implementation manner, as shown in fig. 4, a flowchart of a specific implementation of S103 in another embodiment in fig. 1 is shown. As can be seen from fig. 4, in this embodiment, compared with the embodiment shown in fig. 2, the specific implementation processes of S401 to S402 are the same as those of S1031 to S1032 and S404 to S1033, except that S403 is further included before S404, and it should be noted that S403 and S402 are in a sequential execution relationship, which is described in detail as follows:
and S403, analyzing the second position coordinates respectively according to a preset maximum likelihood analysis model to obtain likelihood values corresponding to the second position coordinates.
By way of example and not limitation, the preset maximum likelihood analysis model is:
cost(x，y)＝e-kvalue(x，y)
the cost (x, y) is a maximum likelihood value corresponding to the position coordinate (x, y), k is a preset weight coefficient, and value (x, y) is an occupancy rate of the obstacle at the preset position coordinate (x, y). In general, the value of value may be set in advance, for example, in this embodiment, the value of value (x, y) is any one of-1, 0, and 1, where value (x, y) is-1, which indicates that the preset position coordinate (x, y) is an unknown area, value (x, y) is 0, which indicates that no obstacle is present at the preset position coordinate (x, y), value (x, y) is 1, and which indicates that the preset position coordinate (x, y) is an obstacle.
Through the analysis, the concept of the occupancy rate of the obstacle is introduced into the maximum likelihood model, so that the accuracy of the likelihood value obtained through analysis is improved, and the accuracy of determining the road information is further improved.
In an alternative implementation, it is assumed that the road information includes road boundary information.
Fig. 5 is a flowchart illustrating a specific implementation of S1033 in fig. 2 in an embodiment. As can be seen from fig. 5, in the present embodiment, S1031 includes: s501 to S502. The details are as follows:
and S501, if the likelihood value corresponding to the current second position coordinate is greater than a preset first likelihood threshold, taking the position corresponding to the current second position coordinate as the boundary position of the road.
It can be understood that, since the likelihood value is used to indicate the maximum of the second position coordinate containing the preset road information, when the road information contains the road boundary, the greater the likelihood value corresponding to the second coordinate is, the greater the probability that the second position coordinate contains the road boundary is, that is, the greater the probability that the second position coordinate is the boundary position of the road is, in this embodiment, the first likelihood threshold is used as a critical value for determining whether the second position coordinate is the boundary position of the road. Specifically, the first likelihood threshold is generally a value close to 1, for example, the first likelihood threshold is 0.9.
S502, determining the road boundary information according to all the boundary positions.
Specifically, the road boundary information includes a second position coordinate on the road boundary, in this embodiment, the second position coordinate located at the road boundary position is determined by a first preset likelihood threshold, and the road boundary information is composed of the second position coordinate located at the road boundary position.
In another optional implementation, the road information includes road width information;
correspondingly, as shown in fig. 6, it is a specific implementation flowchart of S1033 in another embodiment of fig. 2, as can be seen from fig. 6, in this embodiment, S1033 includes S601 to S602, which are detailed as follows:
s601, obtaining all effective likelihood values, wherein the effective likelihood values are the likelihood values smaller than a preset second likelihood threshold.
It should be noted that, in this embodiment, the determination of the road width information is based on whether there is an obstacle in the path, for example, another robot or another object that prevents the robot from walking, and the path is narrowed due to the existence of the other robot or the object.
Specifically, the closer the likelihood value corresponding to the second position coordinate is to 1, the narrower the road width corresponding to the second position coordinate is, that is, the higher the possibility that an obstacle exists at the second position coordinate is. Therefore, in this embodiment, the effective likelihood value is determined by a preset second likelihood threshold, where the effective likelihood value is a likelihood value smaller than the preset second likelihood threshold, and when the likelihood value corresponding to the second position coordinate is an effective likelihood value, it indicates that the probability that an obstacle exists at the position of the second position coordinate is low, and at this time, the determination of the road width information is performed.
S602, determining the road width information according to the total number of the effective likelihood values and the preset distance interval.
Specifically, in this embodiment, the total number of the valid likelihood values multiplied by the preset distance interval is used as the road width information.
It should be noted that the road information is not limited to the above-mentioned road boundary information and road width information, and may also include road gradient information, etc., and is not limited in particular herein.
And S104, fusing the road information and the environment map to obtain a navigation map of the preset area.
After the road information is obtained, the road information is fused with the environment map, so that the road information is fused in the environment map, and accurate road information is provided for the robot.
According to the navigation map construction method provided by the embodiment of the application, the target navigation path is determined based on the environment map of the preset area, the node information of two adjacent path nodes based on the target navigation path, the determined road information of the target navigation path and the environment map are fused to obtain the navigation map of the preset area, the road information can be fused into the environment map, and accurate road information is provided for the robot in the navigation process.
Fig. 7 is a schematic structural diagram of a navigation map construction device according to an embodiment of the present application. As can be seen from fig. 7, the navigation map construction apparatus 7 provided in the embodiment of the present application includes:
the acquiring module 701 is used for acquiring an environment map of a preset area and determining a target navigation path based on the environment map;
a first determining module 702, configured to determine at least two path nodes corresponding to the target navigation path;
a second determining module 703, configured to determine road information of the target navigation path based on node information of two adjacent path nodes, respectively;
an obtaining module 704, configured to fuse the road information with the environment map to obtain a navigation map of the preset area.
In an optional implementation manner, at least two pieces of node identification information are preset on the target navigation path;
the first determining module 702 is specifically configured to:
and determining at least two path nodes corresponding to the target navigation path based on at least two pieces of node identification information.
In an optional implementation manner, the path node is a node that needs to perform path selection or has a path turn, any two adjacent path nodes are located in the same path, and the node information includes node coordinate information;
the second determining module 703 includes:
the first determining unit is used for determining a first position coordinate corresponding to each segmentation point of a target line segment, wherein the target line segment is a connecting line segment between any two adjacent path nodes;
the conversion unit is used for respectively converting each first position coordinate into a corresponding second position coordinate, and the coordinate axis of the first position coordinate and the coordinate axis of the second position coordinate are positioned on the same plane and are mutually vertical;
and a second determining unit configured to determine road information of the target navigation path based on the likelihood value corresponding to each of the second position coordinates.
In an optional implementation manner, the first determining unit includes:
the obtaining subunit is configured to perform equidistant segmentation on a target line segment based on a preset distance interval to obtain a plurality of segmentation points, where the target line segment is a connection line segment between any two adjacent path nodes;
a first determining subunit, configured to determine, based on the node coordinate information and the distance interval of two adjacent path nodes, a first position coordinate corresponding to each of the division points.
In an optional implementation manner, the conversion unit is specifically configured to:
and respectively converting each first position coordinate into a corresponding second position coordinate according to a vertical slope, the distance interval and the total number of the segmentation points, wherein the vertical slope is the slope of a vertical line segment vertical to the target line segment.
In an optional implementation manner, the second determining module 703 further includes:
and the obtaining unit is used for analyzing the second position coordinates according to a preset maximum likelihood analysis model to obtain likelihood values corresponding to the second position coordinates.
In an alternative implementation, the road information includes road boundary information;
the second determination unit includes:
a first determining unit, configured to, if a likelihood value corresponding to the current second position coordinate is greater than a preset first likelihood threshold, take a position corresponding to the current second position coordinate as a boundary position of the road;
the second determining subunit is used for determining the road boundary information according to all the boundary positions;
in an alternative implementation, the road information includes road width information;
the second determination unit includes:
the obtaining subunit is configured to obtain all valid likelihood values, where the valid likelihood values are the likelihood values smaller than a preset second likelihood threshold;
and the third determining subunit is used for determining the road width information according to the total number of the effective likelihood values and the preset distance interval.
Fig. 8 is a schematic structural diagram of a robot according to a fourth embodiment of the present application. As shown in fig. 8, the robot 8 of this embodiment includes: a processor 80, a memory 81, and a computer program 82, such as a navigation map building program, stored in the memory 81 and executable on the processor 80. The processor 80, when executing the computer program 82, implements the steps in the various navigation map construction method embodiments described above, such as the steps 101-104 shown in fig. 1.
Illustratively, the computer program 82 may be partitioned into one or more modules/units that are stored in the memory 81 and executed by the processor 80 to accomplish the present application. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 82 in the robot 8. For example, the computer program 82 may be divided into an acquisition module, a first determination module, a second determination module, and an acquisition module (module in the virtual device), each module having the following specific functions:
the system comprises an acquisition module, a navigation module and a navigation module, wherein the acquisition module is used for acquiring an environment map of a preset area and determining a target navigation path based on the environment map;
the first determining module is used for determining at least two path nodes corresponding to the target navigation path;
the second determining module is used for determining the road information of the target navigation path based on the node information of two adjacent path nodes;
and the obtaining module is used for fusing the road information and the environment map to obtain a navigation map of the preset area.
It will be apparent to those skilled in the art that, for convenience and brevity of description, only the above-mentioned division of the functional units and modules is illustrated, and in practical applications, the above-mentioned function distribution may be performed by different functional units and modules according to needs, that is, the internal structure of the apparatus is divided into different functional units or modules to perform all or part of the above-mentioned functions. Each functional unit and module in the embodiments may be integrated in one processing unit, or each unit may exist alone physically, or two or more units are integrated in one unit, and the integrated unit may be implemented in a form of hardware, or in a form of software functional unit. In addition, specific names of the functional units and modules are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present application. The specific working processes of the units and modules in the system may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus/terminal device and method may be implemented in other ways. For example, the above-described embodiments of the apparatus/terminal device are merely illustrative, and for example, the division of the modules or units is only one logical division, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of communication units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated modules/units, if implemented in the form of software functional units and sold or used as separate products, may be stored in a computer readable storage medium. Based on such understanding, all or part of the flow in the method of the embodiments described above can be realized by a computer program, which can be stored in a computer-readable storage medium and can realize the steps of the embodiments of the methods described above when the computer program is executed by a processor. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-Only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, and the like. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present application, and not for limiting the same; although the present application has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; such modifications and substitutions do not substantially depart from the spirit and scope of the embodiments of the present application and are intended to be included within the scope of the present application.
Claims (9)
1. A navigation map construction method is characterized by comprising the following steps:
acquiring an environment map of a preset area, and determining a target navigation path based on the environment map;
determining at least two path nodes corresponding to the target navigation path, wherein the path nodes are nodes needing path selection or having path turning, any two adjacent path nodes are positioned in the same path, and the node information comprises node coordinate information;
determining road information of the target navigation path based on node information of two adjacent path nodes respectively, wherein the determining comprises the following steps: determining a first position coordinate corresponding to each segmentation point of a target line segment based on the node coordinate information of two adjacent path nodes, wherein the target line segment is a connecting line segment between any two adjacent path nodes; converting each first position coordinate into a corresponding second position coordinate, wherein the coordinate axis of the first position coordinate and the coordinate axis of the second position coordinate are positioned on the same plane and are perpendicular to each other; determining road information of the target navigation path based on the likelihood value corresponding to each second position coordinate;
and fusing the road information and the environment map to obtain the navigation map of the preset area.
2. The navigation map construction method according to claim 1, wherein at least two node identification information are preset on the target navigation path;
the determining at least two path nodes corresponding to the target navigation path includes:
and determining at least two path nodes corresponding to the target navigation path based on at least two pieces of node identification information.
3. The navigation map construction method according to claim 1, wherein the determining a first position coordinate corresponding to each division point of a target line segment based on the node coordinate information of two adjacent path nodes includes:
performing equal-distance segmentation on a target line segment based on a preset distance interval to obtain a plurality of segmentation points, wherein the target line segment is a connection line segment between any two adjacent path nodes;
and determining a first position coordinate corresponding to each segmentation point based on the node coordinate information and the distance interval of two adjacent path nodes.
4. The navigation map construction method of claim 1, wherein said separately converting each of said first location coordinates to a respective corresponding second location coordinate comprises:
and respectively converting each first position coordinate into a corresponding second position coordinate according to a vertical slope, a distance interval and the total number of the segmentation points, wherein the vertical slope is the slope of a vertical line segment vertical to the target line segment.
5. The navigation map construction method according to claim 1, wherein, before the determining the road information of the target navigation path based on the likelihood value corresponding to each of the second position coordinates, comprising:
and analyzing the second position coordinates respectively according to a preset maximum likelihood analysis model to obtain likelihood values corresponding to the second position coordinates.
6. The navigation map construction method of claim 1, wherein the road information includes road boundary information;
the determining the road information of the target navigation path based on the likelihood value corresponding to each second position coordinate includes:
if the likelihood value corresponding to the current second position coordinate is greater than a preset first likelihood threshold, taking the position corresponding to the current second position coordinate as the boundary position of the road;
and determining the road boundary information according to all the boundary positions.
7. The navigation map construction method according to claim 4 or 6, wherein the road information includes road width information;
the determining the road information of the target navigation path based on the likelihood value corresponding to each second position coordinate includes:
acquiring all effective likelihood values, wherein the effective likelihood values are the likelihood values smaller than a preset second likelihood threshold value;
and determining the road width information according to the total number of the effective likelihood values and the preset distance interval.
8. A robot 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 navigation map construction method according to any one of claims 1 to 6 when executing the computer program.
9. A computer-readable storage medium, in which a computer program is stored, which, when being executed by a processor, carries out the steps of the navigation map construction method according to any one of claims 1 to 6.
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