CN114119893A - Laser SLAM-oriented characteristic line segment extraction method and device, electronic device and storage medium - Google Patents
Laser SLAM-oriented characteristic line segment extraction method and device, electronic device and storage medium Download PDFInfo
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Abstract
The application relates to a laser SLAM-oriented characteristic line segment extraction method, device, electronic device and storage medium. The characteristic line segment extraction method facing the laser SLAM comprises the following steps: the method comprises the steps of obtaining a first data set, carrying out coordinate transformation on the first data set to determine a second data set, deleting interference data in the second data set to determine a third data set, determining segmentation point data in the third data set, grouping the third data set according to the segmentation point data to determine a grouped data set, and extracting characteristic line segments corresponding to the grouped data set. Through the method and the device, the problems of complexity and low accuracy in the concentrated extraction of the characteristic line segments from the laser radar scanning points are solved, and a scheme for simply, conveniently and accurately extracting the characteristic line segments from the laser radar scanning points is realized.
Description
Technical Field
The present disclosure relates to the field of laser SLAM image processing, and in particular, to a method, an apparatus, an electronic apparatus, and a storage medium for extracting a characteristic line segment for laser SLAM.
Background
The synchronous positioning and mapping (SLAM) technology refers to that a robot carrying a specific sensor performs real-time processing on observation data of the sensor in an unknown environment, analyzes and acquires environmental characteristics and self-position gestures, constructs an incremental map of the surrounding environment in real time according to the environmental characteristics and the self-position gestures, and realizes self-positioning. SLAM is currently widely used in the unmanned industry and the intelligent robot industry as a key technology of autonomous navigation. SLAM can be divided into two categories: laser-based SLAM and vision-based SLAM. The laser radar has the advantages of high measurement precision, fine time and spatial resolution, no need of arranging scenes in advance, capability of quickly responding to environmental changes, capability of fusing multiple sensors and the like, and compared with the SLAM technology based on vision, the laser SLAM autonomous positioning is safer and more stable, and becomes a hotspot of domestic and foreign research.
In the laser SLAM navigation system, feature extraction has a significant influence on subsequent patterning accuracy and positioning. In order to improve the practicability of the laser SLAM technology, a great deal of effort is made by broad scholars in feature extraction. Although the feature points extracted by the ORB algorithm are relatively robust, the feature points are sensitive to conditions such as fast motion and few texture features. Although the positioning accuracy of the robot can be improved based on the scheme of the structural line features, the method for representing the structural line features in a parameterization mode cannot be used for scenes with irregular distribution of texture features. Some scholars fuse point and line features, and meanwhile, the perpendicular distance between line segments after re-projection is regarded as a re-projection error, so that the influence of the length change of the line segments on a system is reduced, but the error rate is high during feature matching. Researchers have also improved the line feature extraction method, but it is still difficult to efficiently and reliably extract and track end points of line segments from consecutive frames, accurately, under a change in viewing angle, etc.
At present, no effective solution is provided for the problems of complex implementation and low accuracy of extracting characteristic line segments from a laser radar scanning point set in the related technology.
Disclosure of Invention
The embodiment of the application provides a method, a device, an electronic device and a storage medium for extracting characteristic line segments for laser SLAM, and aims to at least solve the problems of complex implementation and low accuracy of the concentrated extraction of the characteristic line segments from laser radar scanning points in the related art.
In a first aspect, an embodiment of the present application provides a method for extracting a characteristic line segment facing a laser SLAM.
In some of these embodiments, the above method comprises the steps of:
acquiring a first data set, and performing coordinate transformation on the first data set to determine a second data set;
deleting the interfering data in the second data set to determine a third data set;
determining segmentation point data in the third data set;
grouping the third data set according to the segmentation point data to determine a grouped data set;
and extracting the characteristic line segment corresponding to the grouped data set.
Further, in some of these embodiments, the determining the segmentation point data in the third data set comprises:
the third data set is { (x)m,ym) 1,2, …, M, wherein M is a positive integer greater than 2;
according to data (x) in the third data setm,ym) Respectively determining a first slope parameter H1A second slope parameter H2A third slope parameter H3：
When | H1-H2Is > beta and is H3-H1|+|H3-H2When | is greater than 2 β, data (x) is determinedm,ym) For initial segmentation point data, otherwise determine data (x)m,ym) Initial non-split point data, where β ∈ (0, 0.1);
in determining data (x)m,ym) After the initial segmentation point data or the initial non-segmentation point data, according to the data (x)m,ym) Determining the fourth slope parameters respectively4Fifth slope parameter5The sixth slope parameter6：
In the data (x)m,ym) In the case of the initial segmentation point data, | H4-H5Gamma 1 is less than or equal to | H6-H4|+|H6-H5When | ≦ 2 γ 1, the data (x) is determinedm,ym) Is non-split-point data, otherwise determines the data (x)m,ym) Is segmentation point data, where γ 1 ∈ (β, 0.1);
in the data (x)m,ym) In the case of the initial non-split dot data, | H4-H5| is > γ 2 and | H6-H4|+|H6-H5When | is > 2 γ 2, the data (x) is determinedm,ym) For segmentation point data, otherwise determining said data (x)m,ym) The data are non-split points, where γ 2 ∈ (β, 0.1).
Further, in some embodiments, the grouping the third data set according to the partition data to determine a grouped data set includes:
sequentially traversing the data (x) in the third data setm,ym) Acquiring adjacent segmentation point data in the third data set, taking the adjacent segmentation point data as a data set of head and tail data, and determining the data set as a grouped data set Zs＝{(xs,1,ys,1),…,(xs,ds,ys,ds) Where S is a positive integer and ds is a positive integer greater than 1.
Further, in some embodiments, the extracting feature line segments corresponding to the grouped data sets includes:
when the packet data set ZsWhen ds is less than or equal to 3, the data (x) is useds,1,ys,1) And data (x)s,ds,ys,ds) Extracting the characteristic line segment:
wherein x, y are coordinates used to fit the characteristic line segment.
Further, in some embodiments, the extracting feature line segments corresponding to the grouped data sets includes:
when the packet data set ZsWhen ds > 3, the first reference data (x) is determined1,y1) And second reference data (x)2,y2)：
Based on the first reference data (x)1,y1) And second reference data (x)2,y2) Extracting the characteristic line segment:
wherein x, y are coordinates used to fit the characteristic line segment.
Further, in some of these embodiments, the obtaining a first data set, the coordinate transforming the first data set to determine a second data set includes:
obtaining a first data set { (ρ)n,θn) 1,2, …, N, coordinate transforming the first data set by the following transformation formula to determine a second data set { (x)n,yn)|n＝1,2,…,N}：
Where N is the number of data in the first data set, ρnIs the distance, theta, measured by the nth laser beamnIs the angle of the nth laser beam.
Further, in some of these embodiments, the deleting the interference data in the second data set to determine a third data set includes:
according to the second data set { (x)n,yn) Data (x) in | N | 1,2, …, N }n,yn) Determining a first distance D1A second distance D2A third distance D3：
When D is present1+D2＞(2+α)D3Determining said data (x)n,yn) Deleting said data (x) for disturbing the datan,yn) Wherein α ∈ [0,1 ]]；
And traversing the second data set, deleting all the interference data in the second data set, and determining the second data set after the interference data is deleted as a third data set.
In a second aspect, an embodiment of the present application provides a characteristic line segment extraction device facing a laser SLAM.
In some embodiments, the apparatus includes a data acquisition module, an interference data deletion module, a segmentation point data determination module, a grouping data set determination module, and a feature line segment extraction module:
the data acquisition module is used for acquiring a first data set and performing coordinate transformation on the first data set to determine a second data set;
the interference data deleting module is used for deleting the interference data in the second data set to determine a third data set;
the segmentation point data determination module is used for determining segmentation point data in the third data set;
the grouped data set determining module is used for grouping the third data set according to the division point data to determine a grouped data set;
and the characteristic line segment extraction module is used for extracting the characteristic line segment corresponding to the grouped data set.
In a third aspect, an embodiment of the present application provides an electronic apparatus, which includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor, when executing the computer program, implements the method for extracting a laser SLAM-oriented feature line segment according to the first aspect.
In a fourth aspect, the present application provides a storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for extracting a characteristic line segment for laser SLAM according to the first aspect.
Compared with the related art, the method, the device, the electronic device and the storage medium for extracting the characteristic line segment oriented to the laser SLAM provided by the embodiment of the application solve the problems of complexity and low accuracy in the concentrated extraction of the characteristic line segment from the laser radar scanning points through the scheme of filtering interference data and grouping based on the data of the segmentation points, and realize a scheme for simply, conveniently and accurately extracting the characteristic line segment in the laser radar scanning point set.
The details of one or more embodiments of the application are set forth in the accompanying drawings and the description below to provide a more thorough understanding of the application.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the application and together with the description serve to explain the application and not to limit the application. In the drawings:
fig. 1 is a flowchart of a laser SLAM-oriented feature line segment extraction method according to an embodiment of the present application;
fig. 2 is a block diagram of a characteristic line segment extraction device facing a laser SLAM according to an embodiment of the present application.
Detailed Description
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be described and illustrated below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the present application and are not intended to limit the present application. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments provided in the present application without any inventive step are within the scope of protection of the present application. Moreover, it should be appreciated that in the development of any such actual implementation, as in any engineering or design project, numerous implementation-specific decisions must be made to achieve the developers' specific goals, such as compliance with system-related and business-related constraints, which may vary from one implementation to another.
Reference in the specification to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the specification. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. Those of ordinary skill in the art will explicitly and implicitly appreciate that the embodiments described herein may be combined with other embodiments without conflict.
Unless defined otherwise, technical or scientific terms referred to herein shall have the ordinary meaning as understood by those of ordinary skill in the art to which this application belongs. Reference to "a," "an," "the," and similar words throughout this application are not to be construed as limiting in number, and may refer to the singular or the plural. The present application is directed to the use of the terms "including," "comprising," "having," and any variations thereof, which are intended to cover non-exclusive inclusions; for example, a process, method, system, article, or apparatus that comprises a list of steps or modules (elements) is not limited to the listed steps or elements, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus. Reference to "connected," "coupled," and the like in this application is not intended to be limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. Reference herein to "a plurality" means greater than or equal to two. "and/or" describes an association relationship of associated objects, meaning that three relationships may exist, for example, "A and/or B" may mean: a exists alone, A and B exist simultaneously, and B exists alone. Reference herein to the terms "first," "second," "third," and the like, are merely to distinguish similar objects and do not denote a particular ordering for the objects.
The method provided by the embodiment can be executed in a terminal, a computer or a similar operation device. Taking the example of the terminal, the terminal may include one or more processors (the processors may include but are not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory for storing data, and optionally, the terminal may further include a transmission device for communication function and an input/output device. It will be understood by those skilled in the art that the above-described structure is illustrative only and is not intended to limit the structure of the above-described terminal. For example, the terminal may also include more or fewer components than the structures described above, or have a different configuration than the structures described above.
The memory may be used to store a computer program, for example, a software program and a module of application software, such as a computer program corresponding to the laser SLAM-oriented feature line segment extraction method in the embodiment of the present invention, and the processor executes various functional applications and data processing by running the computer program stored in the memory, that is, implementing the method described above. The memory may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some embodiments, the memory may further include memory located remotely from the processor, and these remote memories may be connected to the terminal through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the terminal. In one example, the transmission device includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device may be a Radio Frequency (RF) module, which is used for communicating with the internet in a wireless manner.
The embodiment of the present application provides a method for extracting a characteristic line segment facing a laser SLAM, and fig. 1 is a flowchart of the method for extracting a characteristic line segment facing a laser SLAM according to the embodiment of the present application, and as shown in fig. 1, the flowchart includes the following steps:
step S101, a first data set is obtained, and coordinate transformation is carried out on the first data set to determine a second data set.
The first data set may be raw data obtained by scanning with a laser radar, and the coordinate transformation may be to transform the raw data into a rectangular coordinate system, so as to obtain a second data set based on the rectangular coordinate system, thereby facilitating further processing of the data set.
And step S102, deleting the interference data in the second data set to determine a third data set.
In the second data set determined in step S101, a certain amount of interference data may exist, and the interference data needs to be eliminated through a distance relationship, so as to obtain a third data set that does not include the interference data. And implementing subsequent steps based on the third data set, thereby more accurately extracting the characteristic line segment and improving the accuracy of positioning and constructing the map.
Step S103, determining the data of the segmentation points in the third data set.
In this embodiment, the determination of the division point data is performed based on the relationship such as the slope with respect to the third data set specified in step S102. The point corresponding to the above-mentioned segmentation point data is the data point of the end point that needs to be segmented to form the line segment in the image.
And step S104, grouping the third data set according to the segmentation point data to determine a grouped data set.
After the segmentation point data is determined, in order to obtain data required for fitting each line segment, the third data set needs to be grouped based on the position where the segmentation point data appears to form a grouped data set.
And step S105, extracting the characteristic line segment corresponding to the grouped data set.
In this embodiment, the head and the tail of each packet data set are segmentation point data, the segmentation point data corresponds to an end point of a line segment, and non-segmentation point data is arranged between the head segmentation point data and the tail segmentation point data. Therefore, the characteristic line segments can be fitted in a one-to-one correspondence mode through the grouped data sets.
Through the steps, the laser radar scanning shop set is converted into a rectangular coordinate system, and the interference data in the data set are deleted, so that the data of the segmentation points are found out based on the filtered data; and further, grouping the filtered data through the data of the division points, and finally fitting a characteristic line segment through a grouped data set. Compared with the prior art, the method for extracting the characteristic line segment oriented to the laser SLAM is simple and convenient to implement, high in line segment characteristic fitting precision, capable of simplifying the process of extracting the characteristic line segment from the laser radar scanning points in a centralized mode and improving accuracy.
In some of these embodiments, step S103 further comprises:
step S1031, the third data set is { (x)m,ym) 1,2, …, M, wherein M is a positive integer greater than 2;
according to data (x) in the third data setm,ym) Respectively determining a first slope parameter H1A second slope parameter H2A third slope parameter H3：
When | H1-H2Is > beta and is H3-H1|+|H3-H2When | is greater than 2 β, data (x) is determinedm,ym) For initial segmentation point data, otherwise determine data (x)m,ym) Initial non-split point data, where β ∈ (0, 0.1);
in the embodiment, the segmentation point data is preliminarily determined through slope calculation and processing, and the implementation scheme is simple and high in feasibility.
Step S1032, in the determination data (x)m,ym) After the initial segmentation point data or the initial non-segmentation point data, according to the data (x)m,ym) Determining the fourth slope parameters respectively4Fifth slope parameter5The sixth slope parameter6：
In the data (x)m,ym) In the case of the initial segmentation point data, | H4-H5Gamma 1 is less than or equal to | H6-H4|+H6-H5When | ≦ 2 γ 1, the data (x) is determinedm,ym) Is non-split-point data, otherwise determines the data (x)m,ym) Is segmentation point data, where γ 1 ∈ (β, 0.1);
in the data (x)m,ym) In the case of the initial non-split dot data, | H4-H5| is > γ 2 and | H6-H4|+H6-H5When | is > 2 γ 2, the data (x) is determinedm,ym) For segmentation point data, otherwise determining said data (x)m,ym) The data are non-split points, where γ 2 ∈ (β, 0.1).
And further processing the initial segmentation point data and the initial non-segmentation point data determined in the step S1031 to obtain accurate segmentation point data, so that the extracted feature line segment is more accurate.
In some embodiments, step S104 further comprises:
step S1041, traversing data (x) in the third data set in sequencem,ym) Acquiring adjacent segmentation point data in the third data set, and taking the adjacent segmentation point data as head and tail numbersAccording to the data set, determined as a packet data set Zs＝{(xs,1,ys,1),…,(xs,ds,ys,ds) Where S is a positive integer and ds is a positive integer greater than 1.
In some of these embodiments, step S105 further comprises:
step S1051, when the grouped data set ZsWhen ds is less than or equal to 3, the data (x) is useds,1,ys,1) And data (x)s,ds,ys,ds) Extracting the characteristic line segment:
wherein x, y are coordinates used to fit the characteristic line segment.
In some of these embodiments, step S105 further comprises:
step S1052, when the packet data set ZsWhen ds > 3, the first reference data (x) is determined1,y1) And second reference data (x)2,y2)：
Based on the first reference data (x)1,y1) And second reference data (x)2,y2) Extracting the characteristic line segment:
wherein x, y are coordinates used to fit the characteristic line segment.
In some embodiments, step S101 further comprises:
step S1012, acquiring a first data set { (ρ)n,θn) 1,2, …, N, coordinate transforming the first data set by the following transformation formula to determine a second data set { (x)n,yn)|n＝1,2,…,N}：
Where N is the number of data in the first data set, ρnIs the distance, theta, measured by the nth laser beamnIs the angle of the nth laser beam.
In some embodiments, step S102 further comprises:
step S1021, according to the second data set { (x)n,yn) Data (x) in | N | 1,2, …, N }n,yn) Determining a first distance D1A second distance D2A third distance D3：
When D is present1+D2＞(2+α)D3Determining said data (x)n,yn) Deleting said data (x) for disturbing the datan,yn) Wherein α ∈ [0,1 ]]；
And traversing the second data set, deleting all the interference data in the second data set, and determining the second data set after the interference data is deleted as a third data set.
The embodiment of the present application further provides a laser SLAM-oriented feature line segment extraction device, which is used to implement the foregoing embodiments and preferred embodiments, and the description of the device is omitted. As used hereinafter, the terms "module," "unit," "subunit," and the like may implement a combination of software and/or hardware for a predetermined function. Although the means described in the embodiments below are preferably implemented in software, an implementation in hardware, or a combination of software and hardware is also possible and contemplated.
Fig. 2 is a block diagram of a characteristic line segment extraction device facing a laser SLAM according to an embodiment of the present application, and as shown in fig. 2, the device includes: the data acquisition module 20, the interference data deletion module 30, the segmentation point data determination module 40, the grouping data set determination module 50 and the feature line segment extraction module 60:
a data obtaining module 20, configured to obtain a first data set, and perform coordinate transformation on the first data set to determine a second data set;
an interference data deleting module 30, configured to delete interference data in the second data set to determine a third data set;
a dividing point data determining module 40, configured to determine dividing point data in the third data set;
a grouping data set determining module 50, configured to group the third data set according to the division point data to determine a grouping data set;
and a feature line segment extraction module 60, configured to extract a feature line segment corresponding to the packet data set.
The above modules may be functional modules or program modules, and may be implemented by software or hardware. For a module implemented by hardware, the modules may be located in the same processor; or the modules can be respectively positioned in different processors in any combination.
The present embodiment also provides an electronic device comprising a memory having a computer program stored therein and a processor configured to execute the computer program to perform the steps of any of the above method embodiments.
Optionally, the electronic apparatus may further include a transmission device and an input/output device, wherein the transmission device is connected to the processor, and the input/output device is connected to the processor.
Optionally, in this embodiment, the processor may be configured to execute the following steps by a computer program:
acquiring a first data set, and performing coordinate transformation on the first data set to determine a second data set;
deleting the interfering data in the second data set to determine a third data set;
determining segmentation point data in the third data set;
grouping the third data set according to the segmentation point data to determine a grouped data set;
and extracting the characteristic line segment corresponding to the grouped data set.
It should be noted that, for specific examples in this embodiment, reference may be made to examples described in the foregoing embodiments and optional implementations, and details of this embodiment are not described herein again.
In addition, in combination with the method for extracting the characteristic line segment oriented to the laser SLAM in the above embodiment, the embodiment of the present application may provide a storage medium to implement. The storage medium having stored thereon a computer program; the computer program, when executed by a processor, implements any one of the laser SLAM-oriented feature line segment extraction methods in the above embodiments.
It should be understood by those skilled in the art that various features of the above-described embodiments can be combined in any combination, and for the sake of brevity, all possible combinations of features in the above-described embodiments are not described in detail, but rather, all combinations of features which are not inconsistent with each other should be construed as being within the scope of the present disclosure.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (10)
1. A characteristic line segment extraction method facing to laser SLAM is characterized by comprising the following steps:
acquiring a first data set, and performing coordinate transformation on the first data set to determine a second data set;
deleting the interfering data in the second data set to determine a third data set;
determining segmentation point data in the third data set;
grouping the third data set according to the segmentation point data to determine a grouped data set;
and extracting the characteristic line segment corresponding to the grouped data set.
2. The laser SLAM-oriented feature line segment extraction method of claim 1, wherein the determining segmentation point data in the third data set comprises:
the third data set is { (x)m,ym) 1,2, …, M, wherein M is a positive integer greater than 2;
according to data (x) in the third data setm,ym) Respectively determining a first slope parameter H1A second slope parameter H2A third slope parameter H3：
When | H1-H2Is > beta and is H3-H1|+|H3-H2When | is greater than 2 β, data (x) is determinedm,ym) For initial segmentation point data, otherwise determine data (x)m,ym) Initial non-split point data, where β ∈ (0, 0.1);
in determining data (x)m,ym) After the initial segmentation point data or the initial non-segmentation point data, according to the data (x)m,ym) Determining the fourth slope parameters respectively4Fifth slope parameter5The sixth slope parameter6：
In the data (x)m,ym) Is a stand forIn the case of the initial division point data, | H4-H5Gamma 1 is less than or equal to | H6-H4|+|H6-H5When | ≦ 2 γ 1, the data (x) is determinedm,ym) Is non-split-point data, otherwise determines the data (x)m,ym) Is segmentation point data, where γ 1 ∈ (β, 0.1);
in the data (x)m,ym) In the case of the initial non-split dot data, | H4-H5| is > γ 2 and | H6-H4|+|H6-H5When | is > 2 γ 2, the data (x) is determinedm,ym) For segmentation point data, otherwise determining said data (x)m,ym) The data are non-split points, where γ 2 ∈ (β, 0.1).
3. The laser SLAM-oriented feature line segment extraction method of claim 2, wherein the grouping the third data set to determine a grouped data set from the split point data comprises:
sequentially traversing the data (x) in the third data setm,ym) Acquiring adjacent segmentation point data in the third data set, taking the adjacent segmentation point data as a data set of head and tail data, and determining the data set as a grouped data set Zs＝{(xs,1,ys,1),…,(xs,ds,ys,ds) Where S is a positive integer and ds is a positive integer greater than 1.
4. The method of claim 3, wherein the extracting the feature line segment corresponding to the grouped data set comprises:
when the packet data set ZsWhen ds is less than or equal to 3, the data (x) is useds,1,ys,1) And data (x)s,ds,ys,ds) Extracting the characteristic line segment:
wherein x, y are coordinates used to fit the characteristic line segment.
5. The method of claim 3, wherein the extracting the feature line segment corresponding to the grouped data set comprises:
when the packet data set ZsWhen ds > 3, the first reference data (x) is determined1,y1) And second reference data (x)2,y2)：
Based on the first reference data (x)1,y1) And second reference data (x)2,y2) Extracting the characteristic line segment:
wherein x, y are coordinates used to fit the characteristic line segment.
6. The method of any of claims 1 to 5, wherein the obtaining a first data set and the coordinate transforming the first data set to determine a second data set comprises:
obtaining a first data set { (ρ)n,θn) 1,2, …, N, coordinate transforming the first data set by the following transformation formula to determine a second data set { (x)n,yn)|n＝1,2,…,N}：
Where N is the number of data in the first data set, ρnIs the distance, theta, measured by the nth laser beamnIs the angle of the nth laser beam.
7. The method of claim 6, wherein the removing the interference data from the second data set to determine a third data set comprises:
according to the second data set { (x)n,yn) Data (x) in | N | 1,2, …, N }n,yn) Determining a first distance D1A second distance D2A third distance D3：
When D is present1+D2＞(2+α)D3Determining said data (x)n,yn) Deleting said data (x) for disturbing the datan,yn) Wherein α ∈ [0,1 ]]；
And traversing the second data set, deleting all the interference data in the second data set, and determining the second data set after the interference data is deleted as a third data set.
8. The characteristic line segment extraction device for the laser SLAM is characterized by comprising a data acquisition module, an interference data deletion module, a segmentation point data determination module, a grouping data set determination module and a characteristic line segment extraction module, wherein the data acquisition module comprises a data acquisition module, a data deletion module, a segmentation point data determination module, a grouping data set determination module and a characteristic line segment extraction module:
the data acquisition module is used for acquiring a first data set and performing coordinate transformation on the first data set to determine a second data set;
the interference data deleting module is used for deleting the interference data in the second data set to determine a third data set;
the segmentation point data determination module is used for determining segmentation point data in the third data set;
the grouped data set determining module is used for grouping the third data set according to the division point data to determine a grouped data set;
and the characteristic line segment extraction module is used for extracting the characteristic line segment corresponding to the grouped data set.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute the computer program to perform the laser SLAM-oriented feature line segment extraction method of any one of claims 1 to 7.
10. A storage medium having stored thereon a computer program, wherein the computer program is configured to execute the method for extracting a characteristic line segment for a laser SLAM according to any one of claims 1 to 7 when running.
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| CN115114345B (en) * | 2022-04-02 | 2024-04-09 | 腾讯科技（深圳）有限公司 | Feature representation extraction method, device, equipment, storage medium and program product |
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