CN117761722A - Laser radar SLAM degradation detection method, system, electronic equipment and storage medium - Google Patents
Laser radar SLAM degradation detection method, system, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a laser radar SLAM degradation detection method, a system, electronic equipment and a storage medium, wherein the laser radar SLAM degradation detection method comprises the following steps: and calculating the represented moment physical quantity according to the normal vector of the point of the first point cloud and the geometric information of the point, and judging whether the current scene is degraded or not according to the relation between the moment physical quantity and a preset value. The laser radar SLAM degradation detection method provided by the embodiment of the invention can be used for robots with 3D laser radars, and the degradation of the laser SLAM in the rotation direction can be detected by utilizing the geometric information of the laser radar point cloud. The laser radar SLAM degradation detection method can detect the degradation direction of the laser SLAM in the rotation direction by utilizing the normal vector of the midpoint of the laser radar point cloud and the physical quantity of the moment of the point, and calculates the represented moment physical quantity by utilizing the normal vector of the point and the geometric information of the point, so that the understanding is more visual.
Description
Technical Field
The invention relates to the technical field of ground mobile robots, in particular to a laser radar SLAM degradation detection method, a laser radar SLAM degradation detection system, electronic equipment and a computer readable storage medium.
Background
In the prior art, the degradation direction of the laser slam is obtained by adopting pose constraint or disturbance of point cloud residual error. For the robot with the 3D laser radar, the support of a point cloud matching algorithm is needed for synchronous positioning and mapping (i.e. slam), the point cloud matching can cause failure in a certain direction in a degraded scene, the position and the gesture can possibly fail, and the degradation direction of the laser slam in certain scenes needs to be detected at the moment, so that early warning is performed in advance or a corresponding strategy is made.
For the prior art, after disturbance is obtained on pose constraint or point cloud residual error, the degradation direction of the laser slam is obtained, the derivation relation is complex, the visual understanding is inconvenient, and the calculation mode is slightly complex.
Disclosure of Invention
The invention aims to provide a novel technical scheme of a laser radar SLAM degradation detection method, a system, electronic equipment and a computer readable storage medium, which can solve the problem of complex detection modes.
In a first aspect of the present invention, there is provided a laser radar SLAM degradation detection method, including the steps of: and calculating the represented moment physical quantity according to the normal vector of the point of the first point cloud and the geometric information of the point, and judging whether the current scene is degraded or not according to the relation between the moment physical quantity and a preset value.
Optionally, the step of calculating the moment physical quantity represented by the point normal vector of the first point cloud according to the point normal vector and the point geometric information, and determining whether the current scene is degraded according to the relation between the moment physical quantity and the preset value comprises the following steps: solving a normal vector for each point in the first point cloud; according to the normal vector corresponding to each point in the first point cloud, a moment vector corresponding to each point in the first point cloud is obtained; and constructing a matrix for all the moment vectors, carrying out feature decomposition on the matrix to obtain a group of feature vectors, and judging that the current scene is degraded when the minimum feature value is smaller than a preset value.
Optionally, the step of determining the normal vector corresponding to each point in the first point cloud includes: traversing all points in the first point cloud for any one point in the first point cloud, and putting the points in a second point cloud when the distance between other points and the points is smaller than a threshold value; and carrying out plane fitting on the second point cloud to obtain a normal vector.
Optionally, the plane fitting is performed according to a random sample consensus algorithm.
Optionally, the step of performing plane fitting according to the random sampling consistency algorithm includes: randomly selecting a plurality of points in the point cloud of the determined range points, and calculating an equation of a plane corresponding to the points; calculating the distances from all the point clouds to the plane, if the distances are smaller than a threshold value, the point is a sample point in the model, if the distances are not smaller than the threshold value, the point is a sample point outside the model, and recording the number of the sample points in the current model; repeating the steps, setting iteration times, and outputting a plane model with the largest number of sample points in the model as an optimal plane model; and calculating a plane normal vector from the equation of the obtained plane.
Optionally, three points are randomly selected from the point cloud of the points in the determined range, and the equation of the corresponding plane is calculated.
Optionally, the first point cloud is obtained by downsampling an original point cloud of the laser radar.
In a second aspect of the present invention, there is provided a laser radar SLAM degradation detection system applied to the laser radar SLAM degradation detection method described in any one of the above embodiments, the laser radar SLAM degradation detection system including: the normal vector acquisition module is used for acquiring normal vectors of points; the geometric information acquisition module is used for acquiring geometric information of the points; and the judging module judges whether the current scene is degraded or not according to the relation between the moment physical quantity and the preset value.
In a third aspect of the present invention, there is provided an electronic apparatus comprising: a processor and a memory, in which computer program instructions are stored, wherein the computer program instructions, when executed by the processor, cause the processor to perform the steps of any of the above-described lidar SLAM degradation detection methods.
In a fourth aspect of the present invention, there is provided a computer-readable storage medium storing a computer program which, when executed by a processor, causes the processor to perform the steps of any one of the above-described laser radar SLAM degradation detection methods.
The laser radar SLAM degradation detection method provided by the embodiment of the invention can be used for robots with 3D laser radars, and the degradation of the laser SLAM in the rotation direction can be detected by utilizing the geometric information of the laser radar point cloud. The laser radar SLAM degradation detection method can detect the degradation direction of the laser SLAM in the rotation direction by utilizing the normal vector of the midpoint of the laser radar point cloud and the physical quantity of the moment of the point, and calculates the represented moment physical quantity by utilizing the normal vector of the point and the geometric information of the point, so that the understanding is more visual.
Other features of the present invention and its advantages will become apparent from the following detailed description of exemplary embodiments of the invention, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description, serve to explain the principles of the invention.
FIG. 1 is a flow chart of a laser radar SLAM degradation detection method according to an embodiment of the present invention;
FIG. 2 is a schematic illustration of a point-to-z axis corresponding rotational direction constraint in accordance with an embodiment of the present invention;
fig. 3 is a schematic diagram of the operation of an electronic device according to an embodiment of the invention.
Reference numerals:
a processor 201;
a memory 202; an operating system 2021; an application 2022;
a network interface 203;
an input device 204;
a hard disk 205;
a display device 206.
Detailed Description
Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
A laser radar SLAM degradation detection method according to an embodiment of the present invention is specifically described below with reference to the accompanying drawings.
As shown in fig. 1, the laser radar SLAM degradation detection method according to an embodiment of the present invention includes the steps of:
and calculating the represented moment physical quantity according to the normal vector of the point of the first point cloud and the geometric information of the point, and judging whether the current scene is degraded or not according to the relation between the moment physical quantity and a preset value.
That is, the laser radar SLAM degradation detection method of the embodiment of the present invention mainly includes the following steps: and obtaining the normal vector and geometric information of the point of the first point cloud, and obtaining the moment physical quantity of the point. And judging whether the current scene is degraded or not through the moment physical quantity of the point, thereby realizing the laser radar SLAM degradation detection method.
It should be noted that, the laser radar SLAM degradation detection method of the embodiment of the present invention may be used for a robot with a 3D laser radar, and may detect degradation of the laser SLAM in a rotation direction by using geometric information of a point cloud of the laser radar.
Therefore, the laser radar SLAM degradation detection method of the embodiment of the invention can detect the degradation direction of the laser SLAM in the rotation direction by utilizing the normal vector of the midpoint of the laser radar point cloud and the physical quantity of the moment thereof, and calculate the represented moment physical quantity by utilizing the normal vector of the point and the geometric information of the point, so that the understanding is more visual.
In some embodiments of the present invention, the first point cloud is obtained by downsampling the original point cloud of the lidar.
That is, in the present embodiment, the original point cloud may be downsampled. For example, a point cloud of a frame of lidar is acquired and downsampled.
In this embodiment, by downsampling the original point cloud, the data size of the lidar can be reduced, the number of sampling points can be reduced, and the amount of calculation can be reduced. It should be noted that, the downsampling method adopted in this embodiment may be a general point cloud downsampling method, which is not further limited in this embodiment, and only needs to implement downsampling of the original point cloud data. For ease of illustration, the down-sampled point cloud may be denoted as pc_d.
According to one embodiment of the present invention, the step of calculating the moment physical quantity represented by the point normal vector of the first point cloud according to the point normal vector and the point geometric information, and determining whether the current scene is degraded according to the relation between the moment physical quantity and the preset value comprises:
solving a normal vector for each point in the first point cloud;
according to the normal vector corresponding to each point in the first point cloud, a moment vector corresponding to each point in the first point cloud is obtained;
and constructing a matrix for all the moment vectors, carrying out characteristic decomposition on the matrix, obtaining a group of characteristic vectors, and judging that the current scene is degraded when the minimum characteristic value is smaller than a preset value.
For example, each point in the point cloud pc_d finds a normal vector, and each point in the point cloud pc_d finds a representative moment direction in the following manner
In the formula (1), pc_di is a vector of lidar points,the normal vector represented by the point is the vector f (i) after vector cross multiplication, and the direction represents the acting direction of the moment, which indicates that the corresponding rotation direction of the shaft is restrained. As shown in the top view of fig. 2, the vector of the laser measurement point is cross-multiplied with the normal vector of the plane in which the point lies, which point represents a constraint on the direction of rotation corresponding to the z-axis.
Constructing vectors F for all laser radar points, constructing a matrix F for all the vectors F:
F ＝ [ f(1) … F(n)] (2)；
in the formula (2), 1-n represent the calculated vector of each laser point; performing feature decomposition on the F matrix to obtain a group of feature vectors FF T ＝U F ∑U F T Its minimum eigenvalue is used to determine the direction of degradation of the rotation. Given a threshold value V_th, when the minimum characteristic value is smaller than V_th, the current scene is degraded, and the minimum characteristic value is representedThe eigenvector corresponding to the eigenvalue is the direction of degradation in the direction of rotation.
It can be seen that in this embodiment, whether degradation occurs is determined by the minimum feature value, which has the advantages of simple calculation and no complicated formula evolution and calculation.
In some embodiments of the present invention, the step of determining the normal vector corresponding to each point in the first point cloud includes:
traversing all points in the first point cloud for any point in the first point cloud, and putting the points in the second point cloud when the distance between other points and the points is smaller than a threshold value;
and carrying out plane fitting on the second point cloud to obtain a normal vector.
For example, any point pc_di (xi yi zi) in the point cloud pc_d traverses all points in the point cloud, and when the distance between the point and the point pc_di is smaller than the threshold dis_th, the point pc_di is put into the point cloud pd_dic, and the classical dis_th is 0.5m. And carrying out plane fitting on the point cloud pd_dic to obtain a normal vector, wherein the normal vector represents the normal vector of the point pc_di.
In the embodiment, the normal vector is calculated by matching the first point cloud and the second point cloud, so that the method has the advantages of being simple in calculation mode and convenient to intuitively understand.
According to one embodiment of the invention, plane fitting is performed according to a random sampling consistency algorithm, and the calculation mode is simple.
In some embodiments of the present invention, the step of performing a plane fit according to a random sample consensus algorithm comprises:
randomly selecting a plurality of points in the point cloud of the determined range points, and calculating an equation of a plane corresponding to the points;
calculating the distance between all point clouds and a plane, if the distance is smaller than a threshold value, the point is a sample point in the model, if the distance is not smaller than the threshold value, the point is a sample point outside the model, and recording the number of the sample points in the current model;
repeating the steps, setting iteration times, and outputting a plane model with the largest number of sample points in the model as an optimal plane model;
and calculating a plane normal vector from the equation of the obtained plane.
In this embodiment, the plane fitting is performed in the above manner, so that the calculation rate is improved.
According to one embodiment of the invention, three points are randomly selected from the point cloud of the points in the determined range, and the equation of the corresponding plane is calculated, so that the calculation is further simplified.
The laser radar SLAM degradation detection method and system according to the embodiment of the present invention are described in detail below with reference to specific embodiments.
The plane fitting method is RANSAC (random sampling consistency algorithm), and the specific process is as follows:
(1) Three points are randomly selected from the point cloud of the determined range points, and the corresponding plane equation ax+by+cz+d=0 is calculated.
(2) By calculating the distances d from all point clouds to the plane i ＝|Ax i +By i +Cz i +D|. Selecting a threshold d th If d i <d th And if the point is considered to be a sample point in the model, otherwise, the point is a sample point outside the model, and the number of the current inner points is recorded.
(3) Repeating the steps, setting iteration times, and outputting the plane model with the largest number of sample points in the model as an optimal plane model.
(4) From the resulting plane equation A 1 x+B 1 y+C 1 z+D 1 =0, find plane normal vector
The invention also provides a laser radar SLAM degradation detection system, which is applied to the laser radar SLAM degradation detection method of any embodiment, and comprises the following steps: the system comprises a normal vector acquisition module, a geometric information acquisition module and a judging module, wherein the normal vector acquisition module is used for acquiring normal vectors of points, the geometric information acquisition module is used for acquiring geometric information of the points, and the judging module judges whether the current scene is degraded or not according to the relation between the moment physical quantity and a preset value.
The present invention also provides an electronic device including: a processor 201 and a memory 202, wherein computer program instructions are stored in the memory 202, wherein the computer program instructions, when executed by the processor 201, cause the processor 201 to perform the steps of the lidar SLAM degradation detection method in the above-described embodiments.
Further, as shown in fig. 3, the electronic device further comprises a network interface 203, an input device 204, a hard disk 205, and a display device 206.
The interfaces and devices described above may be interconnected by a bus architecture. The bus architecture may include any number of interconnected buses and bridges. One or more central processing units 201 (CPUs), in particular represented by processor 201, and various circuits of one or more memories 202, represented by memories 202, are connected together. The bus architecture may also connect various other circuits together, such as peripheral devices, voltage regulators, and power management circuits. It is understood that a bus architecture is used to enable connected communications between these components. The bus architecture includes, in addition to a data bus, a power bus, a control bus, and a status signal bus, all of which are well known in the art and therefore will not be described in detail herein.
The network interface 203 may be connected to a network (e.g., the internet, a local area network, etc.), and may obtain relevant data from the network and store the relevant data in the hard disk 205.
Input device 204 may receive various instructions entered by an operator and send to processor 201 for execution. The input device 204 may include a keyboard or pointing device (e.g., a mouse, a trackball, a touch pad, or a touch screen, among others).
A display device 206 may display results obtained by the execution of instructions by the processor 201.
The memory 202 is used for storing programs and data necessary for the operation of the operating system 2021, and data such as intermediate results in the calculation process of the processor 201.
It will be appreciated that the memory 202 in embodiments of the invention can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory. The nonvolatile memory may be Read Only Memory (ROM), programmable Read Only Memory (PROM), erasable Programmable Read Only Memory (EPROM), electrically Erasable Programmable Read Only Memory (EEPROM), or flash memory, among others. Volatile memory can be Random Access Memory (RAM), which acts as external cache memory. The memory 202 of the apparatus and methods described herein is intended to comprise, without being limited to, these and any other suitable types of memory 202.
In some implementations, the memory 202 stores the following elements, executable modules or data structures, or a subset thereof, or an extended set thereof: an operating system 2021 and application programs 2022.
The operating system 2021 contains various system programs, such as a framework layer, a core library layer, a driver layer, and the like, for implementing various basic services and processing hardware-based tasks. The application programs 2022 include various application programs 2022, such as a Browser (Browser), for implementing various application services. The program implementing the method of the embodiment of the present invention may be contained in the application program 2022.
The above-described processor 201 executes the steps of the laser radar SLAM degradation detection method according to the above-described embodiment when calling and executing the application program 2022 and data stored in the memory 202, specifically, programs or instructions stored in the application program 2022.
The method disclosed in the above embodiment of the present invention may be applied to the processor 201 or implemented by the processor 201. The processor 201 may be an integrated circuit chip with signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware in the processor 201 or by instructions in the form of software. The processor 201 may be a general purpose processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or may implement or perform the methods, steps, and logic blocks disclosed in embodiments of the present invention. A general purpose processor may be a microprocessor or the processor 201 may be any conventional processor 201 or the like. The steps of the method disclosed in connection with the embodiments of the present invention may be embodied directly in the execution of a hardware decoding processor, or in the execution of a combination of hardware and software modules in a decoding processor. The software modules may be located in a random access memory, flash memory, read only memory, programmable read only memory, or electrically erasable programmable memory, registers, etc. as well known in the art. The storage medium is located in the memory 202, and the processor 201 reads the information in the memory 202 and, in combination with its hardware, performs the steps of the method described above.
It is to be understood that the embodiments described herein may be implemented in hardware, software, firmware, middleware, microcode, or a combination thereof. For a hardware implementation, the processing units may be implemented within one or more Application Specific Integrated Circuits (ASICs), digital Signal Processors (DSPs), digital Signal Processing Devices (DSPDs), programmable Logic Devices (PLDs), field Programmable Gate Arrays (FPGAs), general purpose processors, controllers, micro-controllers, microprocessors, other electronic units designed to perform the functions of the invention, or a combination thereof.
For a software implementation, the techniques herein may be implemented with modules (e.g., procedures, functions, and so on) that perform the functions herein. The software codes may be stored in the memory 202 and executed by the processor 201. The memory 202 may be implemented within the processor 201 or external to the processor 201.
Specifically, the processor 201 is further configured to read the computer program and perform the steps of predicting a stake pocket method and outputting answers to questions asked by the user.
In a fourth aspect of the present invention, there is also provided a computer-readable storage medium storing a computer program, which when executed by the processor 201, causes the processor 201 to perform the steps of the laser radar SLAM degradation detection method of the above-described embodiment.
In the several embodiments provided in the present invention, it should be understood that the disclosed methods and apparatus may be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of elements is merely a logical functional division, and there may be additional divisions of actual implementation, e.g., multiple elements or components may be combined or integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other form.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may be physically included separately, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in hardware plus software functional units.
The integrated units implemented in the form of software functional units described above may be stored in a computer readable storage medium. The software functional unit is stored in a storage medium, and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform part of the steps of the transceiving method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
While certain specific embodiments of the invention have been described in detail by way of example, it will be appreciated by those skilled in the art that the above examples are for illustration only and are not intended to limit the scope of the invention. It will be appreciated by those skilled in the art that modifications may be made to the above embodiments without departing from the scope and spirit of the invention. The scope of the invention is defined by the appended claims.
Claims (10)
1. The laser radar SLAM degradation detection method is characterized by comprising the following steps:
and calculating the represented moment physical quantity according to the normal vector of the point of the first point cloud and the geometric information of the point, and judging whether the current scene is degraded or not according to the relation between the moment physical quantity and a preset value.
2. The laser radar SLAM degradation detection method of claim 1, wherein the step of calculating a moment physical quantity represented by the point normal vector of the first point cloud and the point geometrical information according to the relation between the moment physical quantity and a preset value, and determining whether degradation occurs in the current scene comprises:
solving a normal vector for each point in the first point cloud;
according to the normal vector corresponding to each point in the first point cloud, a moment vector corresponding to each point in the first point cloud is obtained;
and constructing a matrix for all the moment vectors, carrying out feature decomposition on the matrix to obtain a group of feature vectors, and judging that the current scene is degraded when the minimum feature value is smaller than a preset value.
3. The laser radar SLAM degradation detection method of claim 2, wherein the step of determining the normal vector for each point in the first point cloud comprises:
traversing all points in the first point cloud for any one point in the first point cloud, and putting the points in a second point cloud when the distance between other points and the points is smaller than a threshold value;
and carrying out plane fitting on the second point cloud to obtain a normal vector.
4. The lidar SLAM degradation detection method of claim 3, wherein the plane fitting is performed according to a random sample consensus algorithm.
5. The method for detecting laser radar SLAM degradation according to claim 4, wherein said step of performing a plane fitting according to a random sampling consistency algorithm comprises:
randomly selecting a plurality of points in the point cloud of the determined range points, and calculating an equation of a plane corresponding to the points;
calculating the distances from all the point clouds to the plane, if the distances are smaller than a threshold value, the point is a sample point in the model, if the distances are not smaller than the threshold value, the point is a sample point outside the model, and recording the number of the sample points in the current model;
repeating the steps, setting iteration times, and outputting a plane model with the largest number of sample points in the model as an optimal plane model;
and calculating a plane normal vector from the equation of the obtained plane.
6. The laser radar SLAM degradation detection method of claim 5, wherein three points are randomly selected from a point cloud of points of a determined range, and an equation of a plane corresponding thereto is calculated.
7. The laser radar SLAM degradation detection method according to any one of claims 2 to 6, wherein the first point cloud is obtained by downsampling an original point cloud of a laser radar.
8. A laser radar SLAM degradation detection system, applied to the laser radar SLAM degradation detection method of any one of claims 1-7, characterized in that the laser radar SLAM degradation detection system comprises:
the normal vector acquisition module is used for acquiring normal vectors of points;
the geometric information acquisition module is used for acquiring geometric information of the points;
and the judging module judges whether the current scene is degraded or not according to the relation between the moment physical quantity and the preset value.
9. An electronic device, comprising: a processor and a memory in which computer program instructions are stored, wherein the computer program instructions, when executed by the processor, cause the processor to perform the steps of the lidar SLAM degradation detection method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, causes the processor to perform the steps of the lidar SLAM degradation detection method of any of claims 1-7.
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| CN117789198A (en) * | 2024-02-28 | 2024-03-29 | 上海几何伙伴智能驾驶有限公司 | Method for realizing point cloud degradation detection based on 4D millimeter wave imaging radar |
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| CN117789198A (en) * | 2024-02-28 | 2024-03-29 | 上海几何伙伴智能驾驶有限公司 | Method for realizing point cloud degradation detection based on 4D millimeter wave imaging radar |
| CN117789198B (en) * | 2024-02-28 | 2024-05-14 | 上海几何伙伴智能驾驶有限公司 | Method for realizing point cloud degradation detection based on 4D millimeter wave imaging radar |
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