CN116500645A - Vehicle-mounted laser point cloud intrusion recognition method and device - Google Patents
Vehicle-mounted laser point cloud intrusion recognition method and device Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01D—MEASURING NOT SPECIALLY ADAPTED FOR A SPECIFIC VARIABLE; ARRANGEMENTS FOR MEASURING TWO OR MORE VARIABLES NOT COVERED IN A SINGLE OTHER SUBCLASS; TARIFF METERING APPARATUS; MEASURING OR TESTING NOT OTHERWISE PROVIDED FOR
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- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
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- G—PHYSICS
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- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
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Abstract
The invention discloses a vehicle-mounted laser point cloud intrusion recognition method and device, wherein the method comprises the following steps: measuring data of a train body on a railway; calculating a vehicle body side roll angle; calculating the transverse short string displacement and the vertical short string displacement of the accelerometer on the railway; calculating the lateral displacement and the vertical displacement of the laser radar relative to the rail plane, and the roll angle of the laser radar relative to the rail plane; transforming laser point cloud data from a body coordinate system to a rail plane coordinate system; according to the displacement information, converting laser point cloud data into a three-dimensional space under an in-orbit plane coordinate system, obtaining a three-dimensional point cloud, selecting the three-dimensional point cloud data in a preset range for analysis, and identifying out-of-limit foreign matters. The invention can realize the detection and identification of the limit-intrusion object of the rail train, and the visual ranging unit and the laser radar do not need to be arranged on a vertical section.
Description
Technical Field
The invention relates to the technical field of railway intrusion recognition, in particular to a vehicle-mounted laser point cloud intrusion recognition method and device.
Background
This section is intended to provide a background or context to the embodiments of the invention that are recited in the claims. The description herein is not admitted to be prior art by inclusion in this section.
In the related art, because the positioning and attitude determination system relies on the positioning information of the navigation satellite system and the real-time attitude angle of the inertial navigation device to perform combined navigation calculation, attitude information of the system under a geocentric fixed coordinate system is obtained, and then the three-dimensional point cloud is reconstructed according to the external parameter coefficients of the laser radar and the inertial measurement unit obtained through calibration, and foreign matter invasion is identified and judged. In the environment with weak navigation satellite signals such as tunnels, pure inertial navigation solution pose information is affected by accumulated errors, so that a laser spot Yun Xingbian is serious, and measurement accuracy is low. Under a railway scene, the signals of cross-domain resource sharing base stations around a line are generally weak, so that the measurement accuracy of a railway is reduced when the railway passes through a measurement environment with weak navigation satellite signals. Because of high cost of manually arranging and maintaining the differential base station, the large-scale application of judging the invasion of the foreign matters in the railway scene according to the identification of the navigation satellite system is limited.
Disclosure of Invention
The embodiment of the invention provides a vehicle-mounted laser point cloud intrusion recognition method, which is used for realizing detection and recognition of an intrusion object of a track train in a measurement environment with weak navigation satellite signals, such as a tunnel, and the like, and comprises the following steps:
measuring the side rolling angular speed, the shaking angular speed, the transverse acceleration, the vertical acceleration, the driving speed, the displacement information, the transverse displacement and the vertical displacement of the train body relative to the rail surface on the railway, and obtaining laser point cloud data under a body coordinate system;
calculating a vehicle body side roll angle according to the side roll angular speed, the shaking angular speed, the transverse acceleration and the running speed;
according to the transverse acceleration, the vertical acceleration, the side roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail on the railway;
according to the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail and a preset filter, calculating the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the side roll angle of the laser radar relative to the rail plane;
according to the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the roll angle of the laser radar relative to the rail plane, converting laser point cloud data from a body coordinate system to a rail plane coordinate system;
according to the displacement information, converting laser point cloud data into a three-dimensional space under an in-orbit plane coordinate system, obtaining a three-dimensional point cloud, selecting the three-dimensional point cloud data in a preset range for analysis, and identifying out-of-limit foreign matters.
The embodiment of the invention also provides a vehicle-mounted laser point cloud intrusion recognition device for realizing detection and recognition of the railway train intrusion object in a measurement environment with weak navigation satellite signals such as a tunnel, and the device comprises:
the measuring module is used for measuring the side rolling angular speed, the head shaking angular speed, the transverse acceleration, the vertical acceleration, the running speed, the displacement information, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface on the railway and obtaining laser point cloud data under a body coordinate system;
the calculation module is used for calculating the side roll angle of the vehicle body according to the side roll angular speed, the shaking angular speed, the transverse acceleration and the running speed; according to the transverse acceleration, the vertical acceleration, the side roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail on the railway; according to the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail and a preset filter, calculating the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the side roll angle of the laser radar relative to the rail plane;
the coordinate transformation module is used for transforming laser point cloud data from a body coordinate system to a rail plane coordinate system according to the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the roll angle of the laser radar relative to the rail plane;
the identification module is used for converting laser point cloud data into a three-dimensional space under an in-orbit plane coordinate system according to the displacement information to obtain a three-dimensional point cloud, and selecting the three-dimensional point cloud data in a preset range for analysis to identify out-of-limit foreign matters.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the vehicle-mounted laser point cloud intrusion identification method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the vehicle-mounted laser point cloud intrusion recognition method when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, wherein the computer program realizes the vehicle-mounted laser point cloud intrusion recognition method when being executed by a processor.
In the embodiment of the invention, compared with the technical scheme that the measurement accuracy is reduced when a railway passes through a measurement environment with weak navigation satellite signals in the prior art, and the cost of manually laying and maintaining a differential base station is high, the method limits the large-scale application of judging that foreign matter is invading in a railway scene according to the identification of a navigation satellite system, and obtains laser point cloud data under a body coordinate system by measuring the side roll angular velocity, the shaking angular velocity, the transverse acceleration, the vertical acceleration, the running velocity, the displacement information and the transverse displacement and the vertical displacement of the vehicle body relative to a rail surface of the vehicle body on the railway; calculating a vehicle body side roll angle according to the side roll angular speed, the shaking angular speed, the transverse acceleration and the running speed; according to the transverse acceleration, the vertical acceleration, the side roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail on the railway; according to the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail and a preset filter, calculating the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the side roll angle of the laser radar relative to the rail plane; according to the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the roll angle of the laser radar relative to the rail plane, converting laser point cloud data from a body coordinate system to a rail plane coordinate system; according to the displacement information, converting laser point cloud data into a three-dimensional space under an in-orbit plane coordinate system, obtaining a three-dimensional point cloud, selecting the three-dimensional point cloud data in a preset range for analysis, and identifying out-of-limit foreign matters. The method can realize detection and identification of the limit intrusion object of the rail train in a measurement environment with weak navigation satellite signals such as a tunnel.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a diagram of a coordinate system definition in the prior art;
FIG. 2 is a schematic view of a prior art visual ranging unit and lidar installation;
FIG. 3 is a schematic diagram of the principle of the geometrical relationship between the train body and the track in the embodiment of the invention;
FIG. 4 is a graph showing the amplitude-frequency characteristic of the rail-bound transfer function in an embodiment of the present invention;
FIG. 5 is a graph showing the amplitude-frequency characteristics of a filter according to an embodiment of the present invention;
fig. 6 is a flow chart of a vehicle-mounted laser point cloud intrusion recognition method in an embodiment of the invention;
FIG. 7 is a flowchart of a method for identifying cloud intrusion of a vehicle-mounted laser point according to another embodiment of the present invention;
FIG. 8 is a diagram illustrating a result of determining foreign object invasion according to an embodiment of the present invention;
fig. 9 is a schematic structural diagram of a vehicle-mounted laser point cloud intrusion recognition device according to an embodiment of the present invention;
fig. 10 is a schematic structural diagram of a measurement module according to an embodiment of the invention.
Description of the reference numerals
1-detection beam, 2-visual ranging unit, 3-inertial measurement unit, 4-laser radar, 5-laser and 6-CCD camera
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
The term "and/or" is used herein to describe only one relationship, meaning that there may be three relationships, e.g., a and/or B, which may mean: a exists alone, A and B exist together, and B exists alone. In addition, the term "at least one" herein means any one of a plurality or any combination of at least two of a plurality, for example, including at least one of A, B, C, and may mean including any one or more elements selected from the group consisting of A, B and C.
In the description of the present specification, the terms "comprising," "including," "having," "containing," and the like are open-ended terms, meaning including, but not limited to. Reference to the terms "one embodiment," "a particular embodiment," "some embodiments," "for example," etc., means that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiments or examples. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. The sequence of steps involved in the embodiments is used to schematically illustrate the practice of the present application, and is not limited thereto and may be appropriately adjusted as desired.
In the related art, because the positioning and attitude determination system relies on the positioning information of the navigation satellite system and the real-time attitude angle of the inertial navigation device to perform combined navigation calculation, attitude information of the system under a geocentric fixed coordinate system is obtained, and then the three-dimensional point cloud is reconstructed according to the external parameter coefficients of the laser radar and the inertial measurement unit obtained through calibration, and foreign matter invasion is identified and judged. In the environment with weak navigation satellite signals such as tunnels, pure inertial navigation solution pose information is affected by accumulated errors, so that a laser spot Yun Xingbian is serious, and measurement accuracy is low. Under a railway scene, the signals of cross-domain resource sharing base stations around a line are generally weak, so that the measurement accuracy of a railway is reduced when the railway passes through a measurement environment with weak navigation satellite signals. Because of high cost of manually arranging and maintaining the differential base station, the large-scale application of judging the invasion of the foreign matters in the railway scene according to the identification of the navigation satellite system is limited.
Railroad intrusion detection is typically based on a rail plane coordinate system that identifies objects in the surrounding environment that are beyond the limit. The method is characterized in that point cloud data reconstructed under a geocentric fixed coordinate system is processed according to mileage segmentation, then a rail plane coordinate system is established after tracks on two sides are extracted, then the point cloud data is converted into the rail plane coordinate system, and finally foreign matter intrusion recognition is carried out, and fig. 1 is a schematic diagram of coordinate system definition in the prior art.
If only the odometer and the laser radar are adopted for point cloud reconstruction, a point cloud area of the track is required to be selected, translation and rotation amounts of front and rear frames are calculated through an iterative nearest point algorithm, or a track vertex is searched through a specific method, the origin position of a track surface coordinate system is determined, and then point cloud reconstruction is carried out based on the track surface coordinate system. However, due to the complex change of the longitudinal section of the turnout area, the method is poor in performance in the turnout area, and front and back frame data registration cannot be directly carried out based on point cloud data. Fig. 2 is a schematic diagram illustrating installation of a visual ranging unit and a laser radar in the prior art, wherein the visual ranging unit can measure displacement of a vehicle body relative to a rail surface, and can be theoretically related to a rail plane coordinate system and point cloud data so as to effectively compensate the point cloud data.
Based on the above, the embodiment of the invention provides a vehicle-mounted laser point cloud intrusion recognition method, which is used for realizing detection and recognition of an intrusion object of a track train in a measurement environment with weak navigation satellite signals such as a tunnel and the like, and can meet the real-time intrusion detection requirement, and a visual ranging unit can acquire the displacement of the laser radar relative to a track surface without the need of being on the same vertical section with the laser radar. ( The visual ranging unit refers to the patent: a train track detection system and method, publication number: 108032868A )
In the point cloud data processing, the lateral displacement calculation mode is introduced here, and the lateral calculation mode is similar to the vertical calculation mode:
in one embodiment, fig. 3 is a schematic diagram of the geometrical relationship between the train body and the track in the embodiment of the present invention, where the track is set to have a track direction irregularity, and the lidar and the vision ranging unit are installed on different longitudinal sections of the train body, as can be seen from the figure, d c For the transverse displacement value of the laser radar to be measured relative to the rail surface, according to an inertial reference method and the geometric relation in the figure, the method comprises the following steps:
wherein: d, d 1 For the first transverse displacement value, d, measured by the visual ranging unit 2 For the second lateral displacement value, d, measured by the visual ranging unit c For the transverse displacement value of the laser radar relative to the rail surface, L 1 Represents x 1 And x 2 Distance L of (2) 2 Represents x 2 To x 3 Is set to y c Is the observed value.
As can be seen from fig. 3, the track plane x 1 、x 2 And x 3 The positions form an asymmetric chord, and the track direction values at 3 positions are respectively expressed as y 1 、y 2 And y 3 . Due to d 1 、d 2 Having been measured by the visual ranging unit, the observed value y is based on the geometrical relationship in the figure c Can be expressed as:
wherein: y is c To be observed value, y 1 A first orbital value of the orbit relative to the inertial reference line and a second orbital value y of the orbit relative to the inertial reference line 2 A third orbital value y which is the relative inertial reference line of the orbit 3 ，y 3 ' is expressed as x 1 And x 2 Pulling a reference string, the string being at x 3 Distance between the position and inertial reference line, L 1 Represents x 1 And x 2 Distance L of (2) 2 Represents x 2 To x 3 Is a distance of (3).
Third orbital value y due to orbital relative to inertial reference line 3 It is also possible that less than y',
in one embodiment, x in FIG. 3 3 Third orbital value y of the orbit relative to the inertial reference line 3 When less than y', i.e. at this pointObserved value y at this time c Can be expressed as:
wherein: y is c To be observed value, y 1 A first orbital value of the orbit relative to the inertial reference line and a second orbital value y of the orbit relative to the inertial reference line 2 A third orbital value y which is the relative inertial reference line of the orbit 3 ，y 3 ' is expressed as x 1 And x 2 Pulling a reference string, the string being at x 3 Distance between the position and inertial reference line, L 1 Represents x 1 And x 2 Distance L of (2) 2 Represents x 2 To x 3 Is a distance of (3).
Fourier transform was performed on the above to obtain the transfer function as follows:
wherein: Ω is angular frequency, Ω=2pi f, where f is frequency, y c (Ω) is y c Regarding the transfer function of Ω, e is the base of the natural logarithm, j is the imaginary number in the fourier transform, y 1 (Ω) is y 1 Regarding the transfer function of Ω, L 1 Represents x 1 And x 2 Distance L of (2) 2 Represents x 2 To x 3 Is a distance of (3).
Setting L 1 Is 3m, L 2 At 4m, a transfer function y is obtained c (Ω) amplitude versus frequency characteristic, FIG. 4 is a graph showing the amplitude versus frequency characteristic of the rail-to-value transfer function in the embodiment of the present invention, where the ordinate is y in the above 1 The coefficient of (Ω), the abscissa being the wavelength, the wavelength being inversely proportional to the frequency f,as shown, observed value y c Mainly concentrate on 1 ~ 30m shortwave wave band, this wave band is less influenced by automobile body self vibration.
Transfer function y c (Ω) transformation to the z-domain, transfer function y c (z) can be expressed as:
wherein: y is c (z) is y c In the z-domain transfer function, z is the complex frequency domain operator of the discrete system, L 1 Represents x 1 And x 2 Distance L of (2) 2 Represents x 2 To x 3 Δx is the sampling interval, typically 0.25m.
Defining the relationship of "short chord medium pitch" to track irregularity is expressed in the z-domain as:
s(z)＝(1-2z -1 +z -2 )y c (z)
wherein: s (z) is the transfer function of s in the z domain, z is the complex frequency domain operator of the discrete system, y 1 (z) is y 1 Transfer function in the z-domain.
According to y c (z) and s (z) design filter F (z) as follows:
wherein: s (z) is the transfer function of s in the z domain, z is the complex frequency domain operator of the discrete system, y c (z) is y c Transfer function in z-domain
The final filter F (z) is as follows:
wherein: z is the complex frequency domain operator of the discrete system, L 1 Represents x 1 And x 2 Distance L of (2) 2 Represents x 2 To x 3 Δx is the sampling interval, typically 0.25m.
As can be seen from the above, y can be obtained from the short chord middle pitch measurement by passing through the filter F (z) c . Wherein the short chord middle leg can be measured using an accelerometer or gyroscope. FIG. 5 is a graph showing the amplitude-frequency characteristics of a filter according to an embodiment of the present invention, wherein the ordinate is the amplitude of the filter F (z), the abscissa is the wavelength, the wavelength is inversely proportional to the frequency F, and the relation between z and F isΔx is the sampling interval, typically 0.25m as shown, and as the wavelength increases, the magnitude of the filter F (z) gradually increases and then stabilizes.
Fig. 6 is a flow chart of a vehicle-mounted laser point cloud intrusion recognition method according to an embodiment of the present invention, as shown in the drawing, the method includes:
step 601: measuring the side rolling angular speed, the shaking angular speed, the transverse acceleration, the vertical acceleration, the driving speed, the displacement information, the transverse displacement and the vertical displacement of the train body relative to the rail surface on the railway, and obtaining laser point cloud data under a body coordinate system;
step 602: calculating a vehicle body side roll angle according to the side roll angular speed, the shaking angular speed, the transverse acceleration and the running speed;
step 603: according to the transverse acceleration, the vertical acceleration, the side roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail on the railway;
step 604: according to the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail and a preset filter, calculating the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the side roll angle of the laser radar relative to the rail plane;
step 605: according to the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the roll angle of the laser radar relative to the rail plane, converting laser point cloud data from a body coordinate system to a rail plane coordinate system;
step 606: according to the displacement information, converting laser point cloud data into a three-dimensional space under an in-orbit plane coordinate system, obtaining a three-dimensional point cloud, selecting the three-dimensional point cloud data in a preset range for analysis, and identifying out-of-limit foreign matters.
In one embodiment, in step 602, the body roll angle includes a high frequency portion and a low frequency portion;
calculating a high-frequency part of the vehicle body roll angle according to the roll angle speed;
and calculating the low-frequency part of the side roll angle of the vehicle body according to the shaking angular speed, the transverse acceleration and the running speed.
In particular, the high frequency portion and the low frequency portion of the vehicle body roll angle are expressed as:
wherein:is the high-frequency part of the side roll angle of the car body, +.>Omega is the low-frequency part of the roll angle of the vehicle body x To the roll angular velocity omega z For angular velocity of shaking head, a y The transverse acceleration, g is the gravitational acceleration, and t is the sampling time.
The vehicle body roll angle is calculated by the following formula:
wherein: θ b Is the side rolling angle of the vehicle body,is a vehicle body side rollingCorner high frequency part->Is the low-frequency part of the side roll angle of the vehicle body.
In one embodiment, two roll angle filters are designed, namely a high-pass filter and a low-pass filter, the frequency response sum of the two roll angle filters is equal to 1, the cut-off wavelength is larger than 500m, the high-pass filter is used for filtering low-frequency drift in a high-frequency part of the roll angle, and the low-pass filter is used for filtering high-frequency noise in a low-frequency part of the roll angle.
In one embodiment, in step 303, calculating the lateral mid-chord leg and the vertical mid-chord leg of the rail comprises:
calculating the transverse short chord displacement and the vertical short chord displacement of the accelerometer according to the transverse acceleration and the vertical acceleration;
and compensating the transverse short chord displacement and the vertical short chord displacement of the accelerometer by utilizing the lateral roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, obtaining the compensated track transverse displacement and the track vertical displacement, and calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail according to the compensated track transverse displacement and the track vertical displacement.
In particular implementations, the lateral and vertical short chord displacements of the accelerometer can be expressed as:
wherein: s is(s) V Is the vertical displacement of the accelerometer, s L For lateral displacement of accelerometer, a z For vertical acceleration, a y For lateral acceleration, Δt is the sampling time.
The compensated track lateral displacement and track vertical displacement are expressed as:
wherein: s is S V,i To be compensated for trackVertical displacement S L,i Delta for compensating lateral displacement of the track L,i For lateral displacement, delta, of the vehicle body relative to the rail surface V,i For vertical displacement of the vehicle body relative to the rail surface, l represents the left rail, r represents the right rail, θ b G is a fixed gauge value 1435mm for the body roll angle.
Thus, the short chord middle leg of a rail can be expressed as:
wherein:is S V,i Second order difference value,/, of>Is S L,i The second order differential value of (2) is the middle branch distance of the short string, and z is a complex frequency domain operator.
In the above formula, when i takes l, the calculation resultsRespectively the vertical short chord middle support distance and the horizontal short chord middle support distance of the left steel rail, and when i is r, calculating to obtain +.>The vertical short chord middle support distance and the transverse short chord middle support distance of the right steel rail are respectively.
In one embodiment, the vertical short chord middle support distanceAnd transverse short chord middle support distance->Substitution of the previously obtained filter +.>Obtaining y c,L,i And y c,V,i Wherein the subscript L represents lateral direction, V represents vertical direction, i=l, r, L tableLeft rail is shown and r represents right rail.
Will y c,L,i And y c,V,i Substituting the formula obtained aboveObtaining a transverse displacement value d of the laser radar relative to the rail surface c,L,i And a vertical displacement value d c,V,i The roll angle of the lidar relative to the rail plane is expressed as:
wherein: θ bt Represents the roll angle of the lidar relative to the rail plane, d c,V,r Vertical displacement value d of laser radar relative to right steel rail surface c,V,l And the vertical displacement value of the laser radar relative to the rail surface of the left steel rail is G which is a fixed gauge value 1435mm.
The 2-dimensional rotation matrix is set as follows:
the coordinate of the laser point cloud data output by the laser radar under the body coordinate system is P b Setting the coordinates under the in-orbit plane coordinate system as P t The following steps are:
wherein: p (P) b The laser point cloud data output by the laser radar is coordinated under the body coordinate system,for a 2-dimensional rotation matrix d c,L,l Transverse displacement value d of laser radar relative to left steel rail surface c,L,r Is the transverse displacement value d of the laser radar relative to the right steel rail surface c,V,l Is the vertical displacement value d of the laser radar relative to the rail surface of the left steel rail c,V,r For laser radar relative to right railVertical displacement value of the face.
In one embodiment, in step 606, according to the displacement information, converting laser point cloud data into a three-dimensional space under an in-orbit plane coordinate system to obtain a three-dimensional point cloud, selecting three-dimensional point cloud data within a preset range for analysis, and identifying out-of-limit foreign objects, including:
reconstructing the point cloud data in a three-dimensional space under a track plane coordinate system to obtain a three-dimensional point cloud, wherein a first axis of the three-dimensional space is determined according to displacement information, and a second axis and a third axis of the three-dimensional space are parallel to the track plane coordinate system;
performing semantic segmentation and rendering coloring on the three-dimensional point cloud to obtain a processed three-dimensional point cloud;
selecting a limit frame for the three-dimensional point cloud processed under the rail plane coordinate system;
judging whether foreign matters enter the area in the limit frame or not;
if yes, the foreign matter is determined to be the overrun foreign matter, and the overrun part of the overrun foreign matter is changed in color.
In one embodiment, fig. 7 is a flow chart of a vehicle-mounted laser point cloud intrusion recognition method according to another embodiment of the present invention, and as shown in fig. 7, the vehicle-mounted laser point cloud intrusion recognition method may be divided into the following steps:
s1: calculating the roll angle of the vehicle body according to the data measured by the gyroscope, the transverse accelerometer and the odometer;
s2: calculating the middle support distance of the transverse short chord and the middle support distance of the vertical short chord of the steel rail on the railway;
s3: calculating transverse and vertical displacement values of the laser radar relative to the rail surface, and calculating the coordinates of laser point cloud data under the coordinate system of the rail surface;
s4: according to the displacement information, converting laser point cloud data into a three-dimensional space, obtaining a three-dimensional point cloud, selecting the three-dimensional point cloud data in a preset range for analysis, and identifying out-of-limit foreign matters.
Fig. 8 is a schematic diagram of a result of determining the intrusion of foreign matters according to an embodiment of the present invention, where the black square frame circles out a position that is identified out of limit foreign matters (color of out-of-limit foreign matters has been changed in original image, and due to black-and-white processing of a picture, no color change is shown in fig. 8, and here, a color change position is circled out by the black square frame).
The embodiment of the invention also provides a vehicle-mounted laser point cloud intrusion recognition device, which is described in the following embodiment. Because the principle of the device for solving the problem is similar to that of the vehicle-mounted laser point cloud intrusion recognition method, the implementation of the device can be referred to the implementation of the vehicle-mounted laser point cloud intrusion recognition method, and the repetition is not repeated.
Fig. 9 is a schematic structural diagram of a vehicle-mounted laser point cloud intrusion recognition device according to an embodiment of the present invention, where, as shown in fig. 9, the device includes:
the measuring module 01 is used for measuring the side rolling angular speed, the head shaking angular speed, the transverse acceleration, the vertical acceleration, the running speed, the displacement information, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface on the railway and obtaining laser point cloud data under a body coordinate system;
the calculation module 02 is used for calculating the side roll angle of the vehicle body according to the side roll angular speed, the shaking angular speed, the transverse acceleration and the running speed; according to the transverse acceleration, the vertical acceleration, the side roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail on the railway; according to the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail and a preset filter, calculating the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the side roll angle of the laser radar relative to the rail plane;
the coordinate transformation module 03 is used for transforming the laser point cloud data from the body coordinate system to the rail plane coordinate system according to the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the roll angle of the laser radar relative to the rail plane;
the identification module 04 converts laser point cloud data into a three-dimensional space under an in-orbit plane coordinate system according to the displacement information to obtain a three-dimensional point cloud, selects the three-dimensional point cloud data in a preset range for analysis, and identifies out-of-limit foreign matters.
In one embodiment, the vehicle body roll angle includes a high frequency portion and a low frequency portion, and the calculation module 02 is specifically configured to:
calculating a high-frequency part of the vehicle body roll angle according to the roll angle speed;
and calculating the low-frequency part of the side roll angle of the vehicle body according to the shaking angular speed, the transverse acceleration and the running speed.
In one embodiment, the computing module 02 is specifically configured to:
calculating the transverse short chord displacement and the vertical short chord displacement of the accelerometer according to the transverse acceleration and the vertical acceleration;
and compensating the transverse short chord displacement and the vertical short chord displacement of the accelerometer by utilizing the lateral roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, obtaining the compensated track transverse displacement and the track vertical displacement, and calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail according to the compensated track transverse displacement and the track vertical displacement.
In one embodiment, the identification module 04 is specifically configured to:
reconstructing the point cloud data in a three-dimensional space under a track plane coordinate system to obtain a three-dimensional point cloud, wherein a first axis of the three-dimensional space is determined according to displacement information, and a second axis and a third axis of the three-dimensional space are parallel to the track plane coordinate system;
performing semantic segmentation and rendering coloring on the three-dimensional point cloud to obtain a processed three-dimensional point cloud;
selecting a limit frame for the three-dimensional point cloud processed under the rail plane coordinate system;
judging whether foreign matters enter the area in the limit frame or not;
if yes, the foreign matter is determined to be the overrun foreign matter, and the overrun part of the overrun foreign matter is changed in color.
Fig. 10 is a schematic structural diagram of a measurement module according to an embodiment of the present invention, and as shown in fig. 10, the measurement module includes:
a detection beam 1 mounted on two different longitudinal sections of the rail train;
the visual ranging unit 2 is arranged on the detection beam and comprises a laser 5 and a charge coupled device camera 6, and is used for measuring the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface;
the inertial measurement unit 3 is arranged on the detection beam and is used for measuring and measuring the side rolling angular velocity, the shaking angular velocity, the transverse acceleration and the vertical acceleration;
the laser radar 4 is arranged at the end part of the vehicle body and is used for acquiring laser point cloud data;
and the odometer is arranged on wheels of the rail train and is used for measuring driving speed and displacement information.
The detection beam 1 may be mounted on two different vertical sections of the rail train, and the specific position of the mounting position vertical section is not limited.
The embodiment of the invention also provides computer equipment, which comprises a memory, a processor and a computer program stored on the memory and capable of running on the processor, wherein the processor realizes the vehicle-mounted laser point cloud intrusion identification method when executing the computer program.
The embodiment of the invention also provides a computer readable storage medium, wherein the computer readable storage medium stores a computer program, and the computer program realizes the vehicle-mounted laser point cloud intrusion recognition method when being executed by a processor.
The embodiment of the invention also provides a computer program product, which comprises a computer program, wherein the computer program realizes the vehicle-mounted laser point cloud intrusion recognition method when being executed by a processor.
In the embodiment of the invention, the vehicle-mounted laser point cloud intrusion recognition is carried out, and compared with the technical scheme that the detection and recognition of the intrusion object of the rail train cannot be realized in the measurement environment with weak navigation satellite signals such as tunnels and the like and the real-time intrusion detection requirement cannot be met in the prior art, the laser point cloud data under a body coordinate system is obtained by measuring the side roll angular velocity, the head angular velocity, the transverse acceleration, the vertical acceleration, the driving speed, the displacement information, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface of the railway; calculating a vehicle body side roll angle according to the side roll angular speed, the shaking angular speed, the transverse acceleration and the running speed; according to the transverse acceleration, the vertical acceleration, the side roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail on the railway; according to the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail and a preset filter, calculating the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the side roll angle of the laser radar relative to the rail plane; according to the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the roll angle of the laser radar relative to the rail plane, converting laser point cloud data from a body coordinate system to a rail plane coordinate system; according to displacement information, laser point cloud data are converted into a three-dimensional space under an in-orbit plane coordinate system, three-dimensional point cloud is obtained, three-dimensional point cloud data in a preset range are selected for analysis, out-of-limit foreign matters are identified, detection and identification of an out-of-limit object of a rail train can be realized in a measurement environment with weak navigation satellite signals such as a tunnel, real-time out-of-limit detection requirements are met, and a visual ranging unit can obtain displacement of the laser radar relative to a rail surface without being on the same vertical section with the laser radar.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The foregoing description of the embodiments has been provided for the purpose of illustrating the general principles of the invention, and is not meant to limit the scope of the invention, but to limit the invention to the particular embodiments, and any modifications, equivalents, improvements, etc. that fall within the spirit and principles of the invention are intended to be included within the scope of the invention.
Claims (12)
1. The vehicle-mounted laser point cloud intrusion recognition method is characterized by comprising the following steps of:
measuring the side rolling angular speed, the shaking angular speed, the transverse acceleration, the vertical acceleration, the driving speed, the displacement information, the transverse displacement and the vertical displacement of the train body relative to the rail surface on the railway, and obtaining laser point cloud data under a body coordinate system;
calculating a vehicle body side roll angle according to the side roll angular speed, the shaking angular speed, the transverse acceleration and the running speed;
according to the transverse acceleration, the vertical acceleration, the side roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail on the railway;
according to the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail and a preset filter, calculating the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the side roll angle of the laser radar relative to the rail plane;
according to the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the roll angle of the laser radar relative to the rail plane, converting laser point cloud data from a body coordinate system to a rail plane coordinate system;
according to the displacement information, converting laser point cloud data into a three-dimensional space under an in-orbit plane coordinate system, obtaining a three-dimensional point cloud, selecting the three-dimensional point cloud data in a preset range for analysis, and identifying out-of-limit foreign matters.
2. The method of claim 1, wherein the body roll angle includes a high frequency portion and a low frequency portion;
according to the side roll angular velocity, the shaking angular velocity, the transverse acceleration and the running speed, the side roll angle of the vehicle body is calculated, and the method comprises the following steps:
calculating a high-frequency part of the vehicle body roll angle according to the roll angle speed;
and calculating the low-frequency part of the side roll angle of the vehicle body according to the shaking angular speed, the transverse acceleration and the running speed.
3. The method of claim 1, wherein calculating the lateral mid-chord and vertical mid-chord distances of the rail comprises:
calculating the transverse short chord displacement and the vertical short chord displacement of the accelerometer according to the transverse acceleration and the vertical acceleration;
and compensating the transverse short chord displacement and the vertical short chord displacement of the accelerometer by utilizing the lateral roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, obtaining the compensated track transverse displacement and the track vertical displacement, and calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail according to the compensated track transverse displacement and the track vertical displacement.
4. The method of claim 1, wherein converting laser point cloud data into a three-dimensional space in an in-orbit plane coordinate system according to the displacement information to obtain a three-dimensional point cloud, selecting the three-dimensional point cloud data within a preset range for analysis, and identifying the overrun foreign matter, comprises:
reconstructing the point cloud data in a three-dimensional space under a track plane coordinate system to obtain a three-dimensional point cloud, wherein a first axis of the three-dimensional space is determined according to displacement information, and a second axis and a third axis of the three-dimensional space are parallel to the track plane coordinate system;
performing semantic segmentation and rendering coloring on the three-dimensional point cloud to obtain a processed three-dimensional point cloud;
selecting a limit frame for the three-dimensional point cloud processed under the rail plane coordinate system;
judging whether foreign matters enter the area in the limit frame or not;
if yes, the foreign matter is determined to be the overrun foreign matter, and the overrun part of the overrun foreign matter is changed in color.
5. The utility model provides a vehicle-mounted laser point cloud limit of intrusion recognition device which characterized in that includes:
the measuring module is used for measuring the side rolling angular speed, the head shaking angular speed, the transverse acceleration, the vertical acceleration, the running speed, the displacement information, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface on the railway and obtaining laser point cloud data under a body coordinate system;
the calculation module is used for calculating the side roll angle of the vehicle body according to the side roll angular speed, the shaking angular speed, the transverse acceleration and the running speed; according to the transverse acceleration, the vertical acceleration, the side roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail on the railway; according to the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail and a preset filter, calculating the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the side roll angle of the laser radar relative to the rail plane;
the coordinate transformation module is used for transforming laser point cloud data from a body coordinate system to a rail plane coordinate system according to the transverse displacement and the vertical displacement of the laser radar relative to the rail plane and the roll angle of the laser radar relative to the rail plane;
the identification module is used for converting laser point cloud data into a three-dimensional space under an in-orbit plane coordinate system according to the displacement information to obtain a three-dimensional point cloud, and selecting the three-dimensional point cloud data in a preset range for analysis to identify out-of-limit foreign matters.
6. The apparatus of claim 5, wherein the measurement module comprises:
a detection beam (1) which is arranged on two different longitudinal sections of the rail train;
the visual ranging unit (2) is arranged on the detection beam and comprises a laser (5) and a charge coupled device camera (6) for measuring the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface;
the inertial measurement unit (3) is arranged on the detection beam and is used for measuring and measuring the side rolling angular velocity, the shaking angular velocity, the transverse acceleration and the vertical acceleration;
the laser radar (4) is arranged at the end part of the vehicle body and is used for acquiring laser point cloud data;
and the odometer is arranged on wheels of the rail train and is used for measuring driving speed and displacement information.
7. The apparatus of claim 5, wherein the body roll angle includes a high frequency portion and a low frequency portion;
the computing module is specifically used for:
calculating a high-frequency part of the vehicle body roll angle according to the roll angle speed;
and calculating the low-frequency part of the side roll angle of the vehicle body according to the shaking angular speed, the transverse acceleration and the running speed.
8. The apparatus of claim 5, wherein the computing module is specifically configured to:
calculating the transverse short chord displacement and the vertical short chord displacement of the accelerometer according to the transverse acceleration and the vertical acceleration;
and compensating the transverse short chord displacement and the vertical short chord displacement of the accelerometer by utilizing the lateral roll angle of the vehicle body, the transverse displacement and the vertical displacement of the vehicle body relative to the rail surface, obtaining the compensated track transverse displacement and the track vertical displacement, and calculating the transverse short chord middle support distance and the vertical short chord middle support distance of the steel rail according to the compensated track transverse displacement and the track vertical displacement.
9. The apparatus of claim 5, wherein the identification module is specifically configured to:
reconstructing the point cloud data in a three-dimensional space under a track plane coordinate system to obtain a three-dimensional point cloud, wherein a first axis of the three-dimensional space is determined according to displacement information, and a second axis and a third axis of the three-dimensional space are parallel to the track plane coordinate system;
performing semantic segmentation and rendering coloring on the three-dimensional point cloud to obtain a processed three-dimensional point cloud;
selecting a limit frame for the three-dimensional point cloud processed under the rail plane coordinate system;
judging whether foreign matters enter the area in the limit frame or not;
if yes, the foreign matter is determined to be the overrun foreign matter, and the overrun part of the overrun foreign matter is changed in color.
10. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 4 when executing the computer program.
11. A computer readable storage medium, characterized in that the computer readable storage medium stores a computer program which, when executed by a processor, implements the method of any of claims 1 to 4.
12. A computer program product, characterized in that it comprises a computer program which, when executed by a processor, implements the method of any of claims 1 to 4.
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| CN117289281A (en) * | 2023-11-24 | 2023-12-26 | 无锡车联天下信息技术有限公司 | On-vehicle monitoring intelligent visualization method based on ADAS-DMRW |
| CN117289281B (en) * | 2023-11-24 | 2024-01-26 | 无锡车联天下信息技术有限公司 | On-vehicle monitoring intelligent visualization method based on ADAS-DMRW |
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