CN113739712B - Vehicle wheel base measuring method and device and vehicle wheel base measuring instrument - Google Patents
Vehicle wheel base measuring method and device and vehicle wheel base measuring instrument Download PDFInfo
- Publication number
- CN113739712B CN113739712B CN202010477311.2A CN202010477311A CN113739712B CN 113739712 B CN113739712 B CN 113739712B CN 202010477311 A CN202010477311 A CN 202010477311A CN 113739712 B CN113739712 B CN 113739712B
- Authority
- CN
- China
- Prior art keywords
- point cloud
- wheel
- cloud data
- area
- face
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/14—Measuring arrangements characterised by the use of optical techniques for measuring distance or clearance between spaced objects or spaced apertures
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B5/00—Measuring arrangements characterised by the use of mechanical techniques
- G01B5/0025—Measuring of vehicle parts
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Optical Radar Systems And Details Thereof (AREA)
Abstract
The disclosure provides a vehicle wheel base measuring method and device and a vehicle wheel base measuring instrument. The vehicle wheel base measuring device acquires corresponding detection data by utilizing a plurality of laser rays detected towards a road; acquiring corresponding three-dimensional point cloud data according to the detection data, wherein the point cloud data comprises three-dimensional coordinates and reflectivity; clustering the three-dimensional point cloud data to obtain a first vehicle body point cloud area; updating the coordinate value in the first direction by utilizing the reflectivity in each point cloud data in the first vehicle body point cloud area to obtain a second vehicle body point cloud area, wherein the first direction is vertical to a second direction extending along the road on the horizontal plane; clustering the second vehicle body point cloud area to obtain a first wheel point cloud area and a second wheel point cloud area; and determining the wheelbase of the vehicle by using the distance between the first wheel cloud point and the second wheel cloud point. The automatic measurement of the vehicle wheel base can be realized no matter the vehicle is in a static state or a moving state.
Description
Technical Field
The disclosure relates to the technical field of measurement, in particular to a vehicle wheel base measuring method and device and a vehicle wheel base measuring instrument.
Background
The vehicle contour dimension measurement technique is an inspection and measurement performed on a vehicle without disassembling the vehicle using various detection devices. The vehicle wheel base detection means that the distance between the front wheel and the rear wheel is acquired when the vehicle is in a normal state.
In the related art, tools such as a steel tape, an angle ruler and a marking post are mainly used for manual measurement. In addition, in the related art, there is a vehicle wheel base measuring method based on a photoelectric switch, in which a moving trolley passes through two adjacent wheels on the same side of a vehicle to be measured, a time difference between a front rising edge and a rear rising edge or a rear falling edge is calculated, and the speed of the moving trolley is combined, so as to calculate the wheel base of the vehicle to be measured.
Disclosure of Invention
The inventor notices that the method for measuring by tools such as a steel tape, an angle ruler, a mark post and the like needs manual participation, has low intelligent degree and low precision, and can only measure the vehicle axle length in a static state. In addition, when the wheel base of the vehicle is measured by using the mobile trolley, the vehicle is also required to be in a static state, and a mobile carrier is required to be configured for the mobile trolley, so that the system complexity is high, and the practicability is low.
Accordingly, the present disclosure provides a vehicle wheel base measuring scheme that enables automatic measurement of a vehicle wheel base whether the vehicle is in a stationary or moving state.
According to a first aspect of an embodiment of the present disclosure, there is provided a vehicle wheel base measuring method including: acquiring corresponding detection data by using a plurality of laser rays detected towards a road; acquiring corresponding three-dimensional point cloud data according to the detection data, wherein the point cloud data comprises three-dimensional coordinates and reflectivity; clustering the three-dimensional point cloud data to obtain a first vehicle body point cloud area; updating the coordinate value in a first direction by utilizing the reflectivity in each point cloud data in the first vehicle body point cloud area to obtain a second vehicle body point cloud area, wherein the first direction is vertical to a second direction extending along the road on a horizontal plane; clustering the second vehicle body point cloud area to obtain a first wheel point cloud area and a second wheel point cloud area; and determining the wheel base of the vehicle by using the distance between the first wheel cloud point area and the second wheel cloud point area.
In some embodiments, determining the wheel base of the vehicle using the distance between the first wheel cloud zone and the second wheel cloud zone comprises: determining a first end face and a second end face which are positioned at two sides of a first wheel by utilizing the first wheel point cloud area; determining a third end surface and a fourth end surface which are positioned at two sides of a second wheel by using the second wheel point cloud area, wherein the first end surface, the second end surface and the fourth end surface are parallel to each other, and the third end surface is positioned at one side of the second wheel close to the second end surface; determining a first distance between the first end face and the third end face, determining a second distance between the second end face and the fourth end face; determining a wheelbase of the vehicle using the first distance and the second distance.
In some embodiments, determining the wheel base of the vehicle using the first distance and the second distance comprises: and taking the average value of the first distance and the second distance as the wheelbase of the vehicle.
In some embodiments, the first to fourth end faces are perpendicular to the first direction.
In some embodiments, determining the first end face and the second end face on both sides of the first wheel using the first wheel cloud spot comprises: performing circle fitting on the first wheel point cloud area to obtain a first circular area; determining the first end face and the second end face through the first circular area; determining third and fourth end surfaces on either side of a second wheel using the second wheel cloud point region comprises: performing circle fitting on the second wheel point cloud area to obtain a second circular area; the third end face and the fourth end face are defined by the second circular area.
In some embodiments, updating the coordinate values in the first direction with the reflectivity in each point cloud data in the first vehicle body point cloud zone comprises: and taking the product of the reflectivity and the coordinate value in the first direction as the updated coordinate value in the first direction in each point cloud data in the first vehicle body point cloud area.
In some embodiments, clustering the three-dimensional point cloud data comprises: performing filtering processing on the three-dimensional point cloud data to obtain a filtering result, wherein the filtering processing comprises at least one of first filtering processing, second filtering processing or third filtering processing, the first filtering processing is used for removing ground interference, the second filtering processing is used for removing interference in the first direction, and the third filtering processing is used for removing interference in the second direction; and clustering by using the filtering result.
In some embodiments, the first filtering process comprises: and removing point cloud data corresponding to the ground from the three-dimensional point cloud data.
In some embodiments, the second filtering process comprises: and removing the point cloud data of which the coordinate value in the first direction exceeds a first preset threshold from the three-dimensional point cloud data.
In some embodiments, the third filtering process comprises: calculating an average value of coordinate values of all point cloud data in the first direction in the three-dimensional point cloud data; and removing the point cloud data of which the difference between the coordinate value in the first direction and the average value exceeds a second preset threshold.
According to a second aspect of the embodiments of the present disclosure, there is provided a vehicle wheel base measuring device including: a data detection module configured to acquire corresponding detection data using a plurality of laser rays detected toward a road; a point cloud data acquisition module configured to acquire corresponding three-dimensional point cloud data according to the detection data, wherein the point cloud data comprises three-dimensional coordinates and reflectivity; the first clustering module is configured to perform clustering processing on the three-dimensional point cloud data to obtain a first vehicle body point cloud area; an updating module configured to update, in each point cloud data in the first vehicle body point cloud area, a coordinate value in a first direction with a reflectivity to obtain a second vehicle body point cloud area, wherein the first direction is perpendicular to a second direction extending along the road on a horizontal plane; the second clustering module is configured to perform clustering processing on the second vehicle body point cloud area to obtain a first wheel point cloud area and a second wheel point cloud area; a wheel base determination module configured to determine a wheel base of a vehicle using a distance between the first wheel cloud point and the second wheel cloud point.
In some embodiments, the wheel base determination module is configured to determine a first end surface and a second end surface on either side of a first wheel using the first wheel cloud spot, determine a third end surface and a fourth end surface on either side of a second wheel using the second wheel cloud spot, wherein the first through fourth end surfaces are parallel to each other and the third end surface is on a side of the second wheel proximate the second end surface, determine a first distance between the first end surface and the third end surface, determine a second distance between the second end surface and the fourth end surface, and determine a wheel base of the vehicle using the first distance and the second distance.
In some embodiments, the wheelbase determination module is configured to take an average of the first distance and the second distance as the wheelbase of the vehicle.
In some embodiments, the first to fourth end faces are perpendicular to the first direction.
In some embodiments, the wheel base determination module is further configured to perform a circle fitting on the first wheel point cloud area to obtain a first circle area, determine the first end surface and the second end surface from the first circle area, perform a circle fitting on the second wheel point cloud area to obtain a second circle area, and determine the third end surface and the fourth end surface from the second circle area.
In some embodiments, the updating module is configured to take a product of the reflectivity and the coordinate value in the first direction as the updated coordinate value in the first direction in each point cloud data in the first vehicle body point cloud zone.
In some embodiments, the first clustering module is configured to perform a filtering process on the three-dimensional point cloud data to obtain a filtering result, the filtering process includes at least one of a first filtering process, a second filtering process, or a third filtering process, wherein the first filtering process is used for removing ground interference, the second filtering process is used for removing interference in the first direction, and the third filtering process is used for removing interference in the second direction, and the clustering process is performed by using the filtering result.
In some embodiments, the first clustering module is configured to remove point cloud data corresponding to the ground from the three-dimensional point cloud data during the first filtering process.
In some embodiments, the first clustering module is configured to remove point cloud data in which a coordinate value in the first direction exceeds a first preset threshold from the three-dimensional point cloud data during the second filtering process.
In some embodiments, the first clustering module is configured to, during the third filtering process, calculate an average value of coordinate values of all point cloud data in the first direction in the three-dimensional point cloud data, and remove point cloud data in which a difference between the coordinate values in the first direction and the average value exceeds a second preset threshold.
According to a third aspect of the embodiments of the present disclosure, there is provided a vehicle wheel base measuring device including: a memory configured to store instructions; a processor coupled to the memory, the processor configured to perform a method implementing any of the embodiments described above based on instructions stored by the memory.
According to a fourth aspect of the embodiments of the present disclosure, there is provided a vehicle wheel base measuring instrument including: a multiline laser radar; and a vehicle wheel base measuring device as described in any of the above embodiments.
According to a fifth aspect of the embodiments of the present disclosure, a computer-readable storage medium is provided, in which computer instructions are stored, and when executed by a processor, the computer-readable storage medium implements the method according to any of the embodiments described above.
Other features of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments of the present disclosure, and for those skilled in the art, other drawings can be obtained according to the drawings without inventive exercise.
FIG. 1 is a schematic flow chart of a vehicle wheelbase measurement method according to an embodiment of the present disclosure;
FIG. 2 is a schematic view of a vehicle wheelbase measurement according to one embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of a vehicle wheelbase measurement method according to another embodiment of the present disclosure;
FIG. 4 is a schematic structural diagram of a vehicle wheel base measuring device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural view of a vehicle wheel base measuring device according to another embodiment of the present disclosure;
FIG. 6 is a schematic structural view of a vehicle wheelbase gauge according to an embodiment of the present disclosure;
Detailed Description
The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, and it is obvious that the described embodiments are only a part of the embodiments of the present disclosure, and not all of the embodiments. The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
The relative arrangement of parts and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
Meanwhile, it should be understood that the sizes of the respective portions shown in the drawings are not drawn in an actual proportional relationship for the convenience of description.
Techniques, methods, and apparatus known to those 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 particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
Fig. 1 is a schematic flow chart of a vehicle wheel base measuring method according to an embodiment of the present disclosure. In some embodiments, the following vehicle wheel base measuring method steps are performed by a vehicle wheel base measuring device.
In step 101, corresponding detection data is acquired using a plurality of laser rays detected toward a road.
In some embodiments, a side area of the vehicle is illuminated with a multiline lidar to acquire probe data for the side of the vehicle.
In step 102, corresponding three-dimensional point cloud data is obtained according to the detection data, wherein the point cloud data comprises three-dimensional coordinates and reflectivity.
It should be noted that the multiline lidar collects three-dimensional polar coordinate data, and for convenience of processing, coordinates of the point cloud data are converted into world coordinates. In addition, since the materials of the vehicle body and the wheels are greatly different, the reflectance to the laser light is also significantly different.
In step 103, clustering is performed on the three-dimensional point cloud data to obtain a first vehicle body point cloud area.
In some embodiments, a first vehicle body point cloud region on the side of the vehicle body can be obtained by clustering the coordinate values of the point cloud data in the first direction. The first direction (i.e., the Y-axis direction) is perpendicular to a second direction (i.e., the X-axis direction) extending along the road on a horizontal plane. For example, a KDTree euclidean distance algorithm may be used for clustering.
In some implementations, in the clustering process of the three-dimensional point cloud data, a filtering process is first performed on the three-dimensional point cloud data to obtain a filtering result, where the filtering process includes at least one of a first filtering process, a second filtering process, or a third filtering process. The first filtering process is used for removing ground interference, the second filtering process is used for removing interference in a first direction, and the third filtering process is used for removing interference in a second direction. Next, clustering is performed using the filtering result.
In some embodiments, the first filtering process comprises: and removing the point cloud data corresponding to the ground from the three-dimensional point cloud data. For example, the point cloud data of the ground reflection is detected by a region growing method and deleted, so as to avoid the interference of the ground reflection on the point cloud data of the vehicle body.
In some embodiments, the second filtering process comprises: and removing the point cloud data of which the coordinate value in the first direction exceeds a first preset threshold from the three-dimensional point cloud data. It should be noted that, because the detection distance of the lidar is long, the three-dimensional point cloud data may include point cloud data generated by irradiating other objects far away with laser light in the first direction. Through carrying out the second filtering processing, can effectively avoid other objects to the influence of automobile body point cloud data in the first direction.
In some embodiments, the third filtering process comprises: in the three-dimensional point cloud data, calculating the average value of the coordinate values of all the point cloud data in the first direction, and removing the point cloud data of which the difference between the coordinate values in the first direction and the average value exceeds a second preset threshold. Due to the fact that the detection angle of the laser radar is wide, more point cloud data can be obtained in the second direction, and the point cloud data can include point cloud data of the vehicle to be detected and point cloud data of other objects in front of and behind the vehicle to be detected. Through carrying out the second filtering processing, can effectively avoid other objects to the influence of automobile body point cloud data in the second direction.
In step 104, in each point cloud data in the first vehicle body point cloud area, the coordinate value in the first direction is updated by using the reflectivity so as to obtain a second vehicle body point cloud area.
In some embodiments, in each point cloud data in the first vehicle body point cloud region, a product of the reflectance and the coordinate value in the first direction is taken as the updated coordinate value in the first direction.
Since the reflectivity of the vehicle body and the wheel to the laser is greatly different, in each point cloud data, the coordinate value in the first direction is updated by using the reflectivity so as to distinguish the vehicle body from the wheel through the clustering process.
In step 105, the second vehicle body point cloud area is clustered to obtain a first wheel point cloud area and a second wheel point cloud area.
Since the coordinate value of each point cloud data in the first direction is corrected by the reflectivity, the point cloud regions of the two wheels can be obtained by clustering using the coordinate values of the point cloud data in the first direction. For example, a KDTree euclidean distance algorithm may be used for clustering.
At step 106, the wheel base of the vehicle is determined using the distance between the first wheel cloud and the second wheel cloud.
In some embodiments, the first wheel cloud point region is used to define a first end surface and a second end surface on both sides of the first wheel, and the second wheel cloud point region is used to define a third end surface and a fourth end surface on both sides of the second wheel, wherein the first to fourth end surfaces are parallel to each other and the third end surface is on a side of the second wheel adjacent to the second end surface. For example, the first to fourth end faces are perpendicular to the first direction.
Next, a first distance between the first end face and the third end face is determined, and a second distance between the second end face and the fourth end face is determined. And determining the wheelbase of the vehicle by using the first distance and the second distance.
In some embodiments, the average of the first distance and the second distance is taken as the wheelbase of the vehicle.
FIG. 2 is a schematic view of a vehicle wheelbase measurement according to one embodiment of the present disclosure.
As shown in fig. 2, a first distance L1 between the first end face 11 of the first wheel 1 and the third end 21 of the second wheel 2 is determined, and a second distance L2 between the second end face 12 of the first wheel 1 and the fourth end face 22 of the second wheel 2 is determined. The wheel base L of the vehicle is calculated by the following formula (1).
L＝(L1+L2)/2 (1)
In some embodiments, to improve the accuracy of the wheel end face, a circle fit is first performed on the first wheel cloud area and the second wheel cloud area, respectively, to obtain corresponding first and second circular areas. For example, the wheel circle fitting may be performed using an average method. The first and second end faces of the first wheel are then defined by the first circular area and the third and fourth end faces of the second wheel are defined by the second circular area. Through carrying out the circular fitting to first wheel point cloud district and second wheel point cloud district, can make the wheel terminal surface position more accurate.
In the method for measuring the wheel base of the vehicle provided by the embodiment of the disclosure, since the reflectivity of the vehicle body and the wheel to the laser is different, the coordinate value in the first direction is updated by using the reflectivity of the point cloud data, the cloud area of the vehicle body point and the wheel point cloud area can be conveniently distinguished by clustering, and the wheel base of the vehicle is obtained by using the two obtained cloud areas of the wheel point. The method and the device are applicable to different working scenes, and can realize the axle distance measurement when the vehicle to be detected is in a static or moving state.
Fig. 3 is a schematic flow chart of a vehicle wheel base measuring method according to another embodiment of the disclosure. In some embodiments, the following vehicle wheel base measuring method steps are performed by a vehicle wheel base measuring device.
In step 301, corresponding detection data is acquired using a plurality of laser rays detected toward a roadway.
In step 302, corresponding three-dimensional point cloud data is obtained according to the detection data, wherein the point cloud data comprises three-dimensional coordinates and reflectivity.
In step 303, a first filtering process is performed on the three-dimensional point cloud data to remove ground interference.
In some embodiments, in the three-dimensional point cloud data, point cloud data corresponding to the ground is removed. For example, the point cloud data of the ground reflection is detected by a region growing method and deleted, so as to avoid the interference of the ground reflection on the point cloud data of the vehicle body.
In step 304, a second filtering process is performed on the three-dimensional point cloud data based on the first filtering process to remove interference in the first direction. The first direction (i.e., the Y-axis direction) is perpendicular to a second direction (i.e., the X-axis direction) extending along the road on a horizontal plane.
In some embodiments, in the three-dimensional point cloud data, point cloud data whose coordinate values in the first direction exceed a first preset threshold is removed. It should be noted that, because the detection distance of the lidar is long, the three-dimensional point cloud data may include point cloud data generated by irradiating other objects far away with laser light in the first direction. Through carrying out the second filtering processing, can effectively avoid other objects to the influence of automobile body point cloud data in the first direction.
In step 305, a third filtering process is performed on the three-dimensional point cloud data on the basis of the second filtering process to remove interference in the second direction.
In some embodiments, in the three-dimensional point cloud data, an average value of coordinate values of all the point cloud data in the first direction is calculated, and point cloud data with a difference between the coordinate values in the first direction and the average value exceeding a second preset threshold is removed. Due to the fact that the detection angle of the laser radar is wide, more point cloud data can be obtained in the second direction, and the point cloud data can include point cloud data of the vehicle to be detected and point cloud data of other objects in front of and behind the vehicle to be detected. Through carrying out the second filtering processing, can effectively avoid other objects to the influence of automobile body point cloud data in the second direction.
In step 306, the filtered three-dimensional point cloud data is clustered to obtain a first vehicle body point cloud area.
In some embodiments, a first vehicle body point cloud region on the side of the vehicle body can be obtained by clustering the coordinate values of the point cloud data in the first direction. For example, a KDTree euclidean distance algorithm may be used for clustering.
In step 307, in each point cloud data in the first vehicle body point cloud area, the coordinate value in the first direction is updated by using the reflectivity, so as to obtain a second vehicle body point cloud area.
In some embodiments, in each point cloud data in the first vehicle body point cloud region, a product of the reflectance and the coordinate value in the first direction is taken as the updated coordinate value in the first direction.
Since the reflectivity of the vehicle body and the wheel to the laser is greatly different, in each point cloud data, the coordinate value in the first direction is updated by using the reflectivity so as to distinguish the vehicle body from the wheel through the clustering process.
In step 308, the second vehicle body point cloud area is clustered to obtain a first vehicle wheel point cloud area and a second vehicle wheel point cloud area.
Since the coordinate value of each point cloud data in the first direction is corrected by the reflectivity, the point cloud regions of the two wheels can be obtained by clustering using the coordinate values of the point cloud data in the first direction. For example, the KDTree euclidean distance algorithm may be used for clustering.
In step 309, a circle fitting is performed on the first wheel cloud point area and the second wheel cloud point area, respectively, to obtain a corresponding first circular area and a corresponding second circular area. For example, the wheel circle fitting may be performed using an average method.
At step 310, a first end face and a second end face of the first wheel are determined from the first circular area and a third end face and a fourth end face of the second wheel are determined from the second circular area. The first end face, the second end face, the third end face and the fourth end face are perpendicular to the first direction, and the third end face is located on one side, close to the second end face, of the second wheel.
Through carrying out the circular fitting to first wheel point cloud district and second wheel point cloud district, can make the wheel terminal surface position more accurate.
In step 311, a wheelbase of the vehicle is determined using a first distance between the first end face and the third end face, and a second distance between the second end face and the fourth end face.
In some embodiments, the wheelbase of the vehicle is calculated using equation (1) above.
Fig. 4 is a schematic structural diagram of a vehicle wheel base measuring device according to an embodiment of the present disclosure. As shown in fig. 4, the vehicle wheel base measuring apparatus includes a data detection module 41, a point cloud data acquisition module 42, a first clustering module 43, an update module 44, a second clustering module 45, and a wheel base determination module 46.
The data detection module 41 is configured to acquire corresponding detection data using a plurality of laser rays detected toward the road.
In some embodiments, a side area of the vehicle is illuminated with a multiline lidar to acquire probe data for the side of the vehicle.
The point cloud data acquisition module 42 is configured to acquire corresponding three-dimensional point cloud data from the detection data, wherein the point cloud data includes three-dimensional coordinates and reflectivity.
The first clustering module 43 is configured to perform clustering processing on the three-dimensional point cloud data to obtain a first vehicle body point cloud area.
In some embodiments, a first vehicle body point cloud region on the side of the vehicle body can be obtained by clustering the coordinate values of the point cloud data in the first direction. The first direction (i.e., Y-axis direction) is perpendicular to the second direction (i.e., X-axis direction) extending along the road in the horizontal plane. For example, the KDTree euclidean distance algorithm may be used for clustering.
In some embodiments, the first clustering module 43 is configured to perform at least one of a first filtering process, a second filtering process, or a third filtering process on the three-dimensional point cloud data to obtain a filtering result, wherein the first filtering process is used for removing ground interference, the second filtering process is used for removing interference in a first direction, and the third filtering process is used for removing interference in a second direction, and the clustering process is performed by using the filtering result.
For example, the first clustering module 43 is configured to remove point cloud data corresponding to the ground from the three-dimensional point cloud data during the first filtering process. The first clustering module 43 is configured to remove point cloud data in which the coordinate value in the first direction exceeds a first preset threshold from the three-dimensional point cloud data during the second filtering process. The first clustering module 43 is configured to calculate an average value of coordinate values of all point cloud data in the first direction in the three-dimensional point cloud data during the third filtering process, and remove point cloud data whose difference between the coordinate values in the first direction and the average value exceeds a second preset threshold.
The updating module 44 is configured to update the coordinate values in the first direction with the reflectivity in each point cloud data in the first vehicle body point cloud zone to obtain a second vehicle body point cloud zone.
In some embodiments, in each point cloud data in the first vehicle body point cloud region, a product of the reflectance and the coordinate value in the first direction is taken as the updated coordinate value in the first direction.
Since the reflectivity of the vehicle body and the wheel to the laser is greatly different, in each point cloud data, the coordinate value in the first direction is updated by using the reflectivity so as to distinguish the vehicle body from the wheel through the clustering process.
The second clustering module 45 is configured to cluster the second body point cloud region to obtain a first wheel point cloud region and a second wheel point cloud region.
Since the coordinate value of each point cloud data in the first direction is corrected by the reflectivity, the point cloud regions of the two wheels can be obtained by clustering using the coordinate values of the point cloud data in the first direction. For example, a KDTree euclidean distance algorithm may be used for clustering.
The wheel base determination module 46 is configured to determine a wheel base of the vehicle using a distance between the first wheel cloud and the second wheel cloud.
In some embodiments, the wheel base determination module 46 is configured to determine the first end surface and the second end surface on both sides of the first wheel using a first wheel cloud spot, and determine the third end surface and the fourth end surface on both sides of the second wheel using a second wheel cloud spot, wherein the first to fourth end surfaces are parallel to each other and the third end surface is on a side of the second wheel adjacent to the second end surface. For example, the first to fourth end faces are perpendicular to the first direction.
The wheel base determination module 46 determines a second distance between the second end surface and the fourth end surface by determining a first distance between the first end surface and the third end surface, and determines a wheel base of the vehicle using the first distance and the second distance. For example, the wheel base of the vehicle can be calculated using the above formula (1).
In some embodiments, the wheel base determination module 46 is further configured to perform a circle fitting on the first wheel cloud of points to obtain a first circle region, determine the first end face and the second end face from the first circle region, perform a circle fitting on the second wheel cloud of points to obtain a second circle region, and determine the third end face and the fourth end face from the second circle region. Through carrying out the circular fitting to first wheel point cloud district and second wheel point cloud district, can make the wheel terminal surface position more accurate.
Fig. 5 is a schematic structural view of a vehicle wheel base measuring device according to another embodiment of the present disclosure. As shown in fig. 5, the vehicle wheel base measuring device includes a memory 51 and a processor 52.
The memory 51 is used for storing instructions, the processor 52 is coupled to the memory 51, and the processor 52 is configured to execute the method according to any one of the embodiments in fig. 1 or fig. 3 based on the instructions stored in the memory.
As shown in fig. 5, the apparatus further includes a communication interface 53 for information interaction with other devices. Meanwhile, the device also comprises a bus 54, and the processor 52, the communication interface 53 and the memory 51 are communicated with each other through the bus 54.
The memory 51 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 51 may also be a memory array. The storage 51 may also be partitioned and the blocks may be combined into virtual volumes according to certain rules.
Further, processor 52 may be a central processing unit CPU, or may be an application specific integrated circuit ASIC, or one or more integrated circuits configured to implement embodiments of the present disclosure.
The present disclosure also relates to a computer-readable storage medium, wherein the computer-readable storage medium stores computer instructions, and the instructions, when executed by a processor, implement a method according to any one of the embodiments shown in fig. 1 or fig. 3.
Fig. 6 is a schematic structural diagram of a vehicle wheel base measuring instrument according to an embodiment of the present disclosure.
As shown in fig. 6, the vehicle wheel base measuring instrument 60 includes a multiline laser radar 61 and a vehicle wheel base measuring device 62. The vehicle wheel base measuring device 62 is the vehicle wheel base measuring device according to any one of the embodiments of fig. 4 or 5.
In some embodiments, the height of the multiline lidar 61 from the ground does not exceed the diameter of the vehicle tire to be inspected, so that as much point cloud data of the wheel as possible is collected.
In some embodiments, the functional unit modules described above can be implemented as a general purpose Processor, a Programmable Logic Controller (PLC), a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable Logic device, discrete Gate or transistor Logic, discrete hardware components, or any suitable combination thereof for performing the functions described in this disclosure.
It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.
The description of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the disclosure in the form disclosed. Many modifications and variations will be apparent to practitioners skilled in this art. The embodiment was chosen and described in order to best explain the principles of the disclosure and the practical application, and to enable others of ordinary skill in the art to understand the disclosure for various embodiments with various modifications as are suited to the particular use contemplated.
Claims (21)
1. A vehicle wheel base measuring method comprising:
acquiring corresponding detection data by using a plurality of laser rays detected towards a road;
acquiring corresponding three-dimensional point cloud data according to the detection data, wherein the point cloud data comprises three-dimensional coordinates and reflectivity;
clustering the three-dimensional point cloud data to obtain a first vehicle body point cloud area;
updating the coordinate value in a first direction by utilizing the reflectivity in each point cloud data in the first vehicle body point cloud area to obtain a second vehicle body point cloud area, wherein the first direction is vertical to a second direction extending along the road on a horizontal plane;
clustering the second vehicle body point cloud area to obtain a first wheel point cloud area and a second wheel point cloud area;
determining the wheelbase of the vehicle by using the distance between the first wheel cloud point area and the second wheel cloud point area;
wherein determining a wheel base of the vehicle using the distance between the first wheel cloud zone and the second wheel cloud zone comprises:
determining a first end face and a second end face which are positioned at two sides of a first wheel by utilizing the first wheel point cloud area;
determining a third end surface and a fourth end surface which are positioned at two sides of a second wheel by using the second wheel point cloud area, wherein the first end surface, the second end surface and the fourth end surface are parallel to each other, and the third end surface is positioned at one side of the second wheel close to the second end surface;
determining a first distance between the first end face and the third end face, determining a second distance between the second end face and the fourth end face;
determining a wheelbase of the vehicle using the first distance and the second distance.
2. The method of claim 1, wherein determining the wheel base of the vehicle using the first distance and the second distance comprises:
and taking the average value of the first distance and the second distance as the wheel base of the vehicle.
3. The method of claim 2, wherein,
the first to fourth end faces are perpendicular to the first direction.
4. The method of claim 1, wherein determining, using the first wheel cloud, a first end face and a second end face on either side of a first wheel comprises:
performing circle fitting on the first wheel point cloud area to obtain a first circular area;
determining the first end face and the second end face through the first circular area;
determining third and fourth end surfaces on either side of a second wheel using the second wheel cloud point region comprises:
performing circle fitting on the second wheel point cloud area to obtain a second circular area;
the third end face and the fourth end face are defined by the second circular area.
5. The method of claim 1, wherein updating the coordinate values in the first direction with the reflectivity in each point cloud data in the first vehicle body point cloud zone comprises:
and taking the product of the reflectivity and the coordinate value in the first direction as the updated coordinate value in the first direction in each point cloud data in the first vehicle body point cloud area.
6. The method of any of claims 1-5, wherein clustering the three-dimensional point cloud data comprises:
performing filtering processing on the three-dimensional point cloud data to obtain a filtering result, wherein the filtering processing comprises at least one of first filtering processing, second filtering processing or third filtering processing, the first filtering processing is used for removing ground interference, the second filtering processing is used for removing interference in the first direction, and the third filtering processing is used for removing interference in the second direction;
and clustering by using the filtering result.
7. The method of claim 6, wherein the first filtering process comprises:
and removing point cloud data corresponding to the ground from the three-dimensional point cloud data.
8. The method of claim 6, wherein the second filtering process comprises:
and removing the point cloud data of which the coordinate value in the first direction exceeds a first preset threshold from the three-dimensional point cloud data.
9. The method of claim 6, wherein the third filtering process comprises:
calculating an average value of coordinate values of all point cloud data in the first direction in the three-dimensional point cloud data;
and removing the point cloud data of which the difference between the coordinate value in the first direction and the average value exceeds a second preset threshold.
10. A vehicle wheel base measuring device comprising:
a data detection module configured to acquire corresponding detection data using a plurality of laser rays detected toward a road;
a point cloud data acquisition module configured to acquire corresponding three-dimensional point cloud data according to the detection data, wherein the point cloud data comprises three-dimensional coordinates and reflectivity;
the first clustering module is configured to perform clustering processing on the three-dimensional point cloud data to obtain a first vehicle body point cloud area;
an updating module configured to update, in each point cloud data in the first vehicle body point cloud area, a coordinate value in a first direction with a reflectivity to obtain a second vehicle body point cloud area, wherein the first direction is perpendicular to a second direction extending along the road on a horizontal plane;
the second clustering module is configured to perform clustering processing on the second vehicle body point cloud area to obtain a first wheel point cloud area and a second wheel point cloud area;
a wheel base determination module configured to determine a wheel base of a vehicle using a distance between the first wheel cloud zone and the second wheel cloud zone, wherein the first wheel cloud zone is used to determine a first end face and a second end face on both sides of a first wheel, the second wheel cloud zone is used to determine a third end face and a fourth end face on both sides of a second wheel, wherein the first to fourth end faces are parallel to each other, the third end face is located on a side of the second wheel close to the second end face, a first distance between the first end face and the third end face is determined, a second distance between the second end face and the fourth end face is determined, and the first distance and the second distance are used to determine the wheel base of the vehicle.
11. The apparatus of claim 10, wherein,
the wheel base determination module is configured to take an average of the first distance and the second distance as a wheel base of the vehicle.
12. The apparatus of claim 11, wherein,
the first to fourth end faces are perpendicular to the first direction.
13. The apparatus of claim 10, wherein,
the wheel base determination module is further configured to perform circle fitting on the first wheel point cloud area to obtain a first circle area, determine the first end face and the second end face through the first circle area, perform circle fitting on the second wheel point cloud area to obtain a second circle area, and determine the third end face and the fourth end face through the second circle area.
14. The apparatus of claim 10, wherein,
the updating module is configured to take a product of a reflectivity and the coordinate value in the first direction as the updated coordinate value in the first direction in each point cloud data in the first vehicle body point cloud zone.
15. The apparatus of any one of claims 10-14,
the first clustering module is configured to perform filtering processing on the three-dimensional point cloud data to obtain a filtering result, wherein the filtering processing includes at least one of first filtering processing, second filtering processing or third filtering processing, the first filtering processing is used for removing ground interference, the second filtering processing is used for removing interference in the first direction, the third filtering processing is used for removing interference in the second direction, and clustering processing is performed by using the filtering result.
16. The apparatus of claim 15, wherein,
the first clustering module is configured to remove point cloud data corresponding to the ground from the three-dimensional point cloud data during the first filtering process.
17. The apparatus of claim 15, wherein,
the first clustering module is configured to remove point cloud data of which the coordinate value in the first direction exceeds a first preset threshold from the three-dimensional point cloud data in the process of performing second filtering processing.
18. The apparatus of claim 15, wherein,
the first clustering module is configured to calculate an average value of coordinate values of all point cloud data in the first direction in the three-dimensional point cloud data in the process of performing third filtering processing, and remove point cloud data of which the difference between the coordinate values in the first direction and the average value exceeds a second preset threshold.
19. A vehicle wheel base measuring device comprising:
a memory configured to store instructions;
a processor coupled to the memory, the processor configured to perform implementing the method of any of claims 1-9 based on instructions stored by the memory.
20. A vehicle wheel base gauge comprising:
a multi-line laser radar; and
the vehicle wheel base measuring device according to any one of claims 10 to 19.
21. A computer readable storage medium, wherein the computer readable storage medium stores computer instructions which, when executed by a processor, implement the method of any one of claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010477311.2A CN113739712B (en) | 2020-05-29 | 2020-05-29 | Vehicle wheel base measuring method and device and vehicle wheel base measuring instrument |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010477311.2A CN113739712B (en) | 2020-05-29 | 2020-05-29 | Vehicle wheel base measuring method and device and vehicle wheel base measuring instrument |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113739712A CN113739712A (en) | 2021-12-03 |
| CN113739712B true CN113739712B (en) | 2022-06-07 |
Family
ID=78724864
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202010477311.2A Active CN113739712B (en) | 2020-05-29 | 2020-05-29 | Vehicle wheel base measuring method and device and vehicle wheel base measuring instrument |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113739712B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106570446A (en) * | 2015-10-12 | 2017-04-19 | 腾讯科技（深圳）有限公司 | Lane line extraction method and device |
| CN108627112A (en) * | 2018-05-09 | 2018-10-09 | 广州市杜格科技有限公司 | Vehicle axis pin is away from dynamic measurement method |
| CN109782015A (en) * | 2019-03-21 | 2019-05-21 | 同方威视技术股份有限公司 | Laser velocimeter method, control device and laser velocimeter |
| CN110298891A (en) * | 2019-06-25 | 2019-10-01 | 北京智行者科技有限公司 | The method and device that Camera extrinsic precision is assessed automatically |
| CN110764108A (en) * | 2019-11-05 | 2020-02-07 | 畅加风行(苏州)智能科技有限公司 | Obstacle detection method and device for port automatic driving scene |
-
2020
- 2020-05-29 CN CN202010477311.2A patent/CN113739712B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106570446A (en) * | 2015-10-12 | 2017-04-19 | 腾讯科技（深圳）有限公司 | Lane line extraction method and device |
| CN108627112A (en) * | 2018-05-09 | 2018-10-09 | 广州市杜格科技有限公司 | Vehicle axis pin is away from dynamic measurement method |
| CN109782015A (en) * | 2019-03-21 | 2019-05-21 | 同方威视技术股份有限公司 | Laser velocimeter method, control device and laser velocimeter |
| CN110298891A (en) * | 2019-06-25 | 2019-10-01 | 北京智行者科技有限公司 | The method and device that Camera extrinsic precision is assessed automatically |
| CN110764108A (en) * | 2019-11-05 | 2020-02-07 | 畅加风行(苏州)智能科技有限公司 | Obstacle detection method and device for port automatic driving scene |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113739712A (en) | 2021-12-03 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110780307B (en) | Method for obtaining road cross section based on storage battery car-mounted laser point cloud mobile measurement system | |
| CN108444390B (en) | Unmanned automobile obstacle identification method and device | |
| CN101089548B (en) | 3D information detection method for pavement treadway | |
| CN102789698B (en) | Environment recognition device and environment recognition method | |
| CN205209441U (en) | Axle for vehicle is apart from automatic measuring device | |
| CN109544633B (en) | Target ranging method, device and equipment | |
| CN106441150B (en) | Three-dimensional vehicle measurement method of parameters and device | |
| CN112034431B (en) | External parameter calibration method and device for radar and RTK | |
| CN102915558A (en) | Method for quickly extracting building three-dimensional outline information in onboard LiDAR (light detection and ranging) data | |
| CN106080397B (en) | Self-adaption cruise system and mobile unit | |
| CN109827516B (en) | Method for measuring distance through wheel | |
| CN203310400U (en) | Limit detection system | |
| CN113739712B (en) | Vehicle wheel base measuring method and device and vehicle wheel base measuring instrument | |
| CN113409467A (en) | Method, device, system, medium and equipment for detecting road surface unevenness | |
| CN104236866B (en) | Car headlamp error information detection correcting method based on direction of traffic | |
| CN112744217A (en) | Collision detection method, travel path recommendation device, and storage medium | |
| CN110728747A (en) | Vehicle three-dimensional outline drawing method based on laser point cloud data and OpenGL | |
| CN108240798A (en) | Gap size measuring method based on 2D laser profiles sensor and machine vision | |
| CN105783873A (en) | Target object measuring method and high-precision map generating method and device | |
| US20050177336A1 (en) | Method for identifying object constellations using distance signals | |
| CN116129137A (en) | Tunnel point cloud feature processing method, device, equipment and storage medium | |
| JPH06225308A (en) | Running course detector | |
| CN113063396B (en) | Method, device and equipment for detecting included angle of semitrailer and storage medium | |
| CN115798207A (en) | Signal intersection rear-end collision recognition method based on LiDAR point cloud | |
| CN113743483B (en) | Road point cloud error scene analysis method based on spatial plane offset analysis model |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |