CN113721234B - Dynamic-static separation filtering method and device for vehicle-mounted millimeter wave Lei Dadian cloud data - Google Patents
Dynamic-static separation filtering method and device for vehicle-mounted millimeter wave Lei Dadian cloud data Download PDFInfo
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- CN113721234B CN113721234B CN202111002031.7A CN202111002031A CN113721234B CN 113721234 B CN113721234 B CN 113721234B CN 202111002031 A CN202111002031 A CN 202111002031A CN 113721234 B CN113721234 B CN 113721234B
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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
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/02—Systems using reflection of radio waves, e.g. primary radar systems; Analogous systems
- G01S13/50—Systems of measurement based on relative movement of target
- G01S13/58—Velocity or trajectory determination systems; Sense-of-movement determination systems
- G01S13/589—Velocity or trajectory determination systems; Sense-of-movement determination systems measuring the velocity vector
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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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
- G01S7/415—Identification of targets based on measurements of movement associated with the target
Abstract
The application provides a vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering method and device, which are characterized in that firstly, the motion speed of a sensor mounted on a vehicle is converted from a vehicle-mounted coordinate system to a radar coordinate system, the measurement speed of a measuring point under the radar coordinate system is obtained, the motion speed of the sensor mounted on the vehicle under the radar coordinate system is calculated, the projection of the measuring point on a coordinate vector in the radar coordinate system is calculated, and the sum of the projection vector and the speed vector of the measuring point in the radar coordinate system is recorded as a first measurement speed vector; and finally judging whether the measuring point is static or not based on the first speed vector, wherein the measuring point is the point in the point cloud data, so that the identification of the point cloud data in a static state in the point cloud data is realized.
Description
Technical Field
The application relates to the technical field of automatic driving, in particular to a vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering method and device.
Background
In the fields of millimeter wave radar and automatic driving, a filtering algorithm of point cloud data is one of the core problems. In the past, point cloud filtering and noise removal are often carried out through Kalman filtering, extended Kalman algorithm and other algorithms, but the track generated by the method is often greatly influenced by noise, and the instability of the track is easily caused.
Dynamic and static data of the point cloud in the vehicle-mounted scene are mixed together and are difficult to process. Static point clouds can become noise influence results under application scenes such as track tracking; in the application scenarios such as grid construction, dynamic data becomes noise.
Disclosure of Invention
In view of this, the embodiment of the application provides a vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering method and device, so as to accurately identify a stationary point cloud in point cloud data.
In order to achieve the above object, the embodiment of the present application provides the following technical solutions:
a vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method comprises the following steps:
acquiring a calibration position of the millimeter wave radar in a vehicle model;
acquiring the running speed and the steering angular speed of the vehicle;
acquiring point cloud data detected by a millimeter wave radar;
converting the motion speed of a sensor mounted on the vehicle from a vehicle-mounted coordinate system to a radar coordinate system;
acquiring the measuring speed of a measuring point under a radar coordinate system;
calculating the motion speed of a sensor installed on the vehicle under a radar coordinate system, projecting a measuring point on a coordinate vector of the measuring point in the radar coordinate system, calculating the sum of the projection vector and the speed vector of the measuring point in the radar coordinate system, and recording the sum as a first measuring speed vector;
and judging whether the measuring point is static or not based on the first speed vector.
Optionally, in the vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method, an origin of a coordinate system of the vehicle model is a midpoint of a rear wheel axle of the vehicle;
the horizontal axis of the coordinate system of the vehicle model is a straight line from the center point of the left wheel to the center point of the right wheel:
the vertical axis of the coordinate system of the vehicle model is a straight line from the origin of coordinates to the midpoint of the vehicle head;
the positive direction of the transverse axis of the coordinate system of the vehicle model is the direction from the left rear wheel to the right rear wheel;
the positive direction of the vertical axis of the coordinate system of the vehicle model is the vehicle advancing direction;
the calibration positions of the millimeter wave radar in the vehicle model comprise a transverse axis coordinate x and a longitudinal axis coordinate y of the millimeter wave radar in a coordinate system of the vehicle model and an included angle between the normal direction of the millimeter wave radar and the vehicle advancing direction.
Optionally, in the vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method, acquiring a running speed and a steering angular speed of a vehicle includes:
the running speed of the vehicle and the steering angular speed are obtained through the vehicle central control or vehicle-mounted integrated navigation equipment.
Optionally, in the vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method, the point cloud data includes:
measuring the polar coordinates of the measuring point under a radar coordinate system;
the radial velocity of the measurement point relative to the millimeter wave radar.
Optionally, in the vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method, the motion speed of a sensor installed on a vehicle is converted from a vehicle-mounted coordinate system to a radar coordinate system;
acquiring the movement speed of a sensor mounted on a vehicle under a vehicle-mounted coordinate system: level (v) normal And vel linear Wherein, the level normal For the speed of the sensor in the direction of the central axis of the vehicle, said level linear A linear velocity generated for an angular velocity of vehicle steering;wherein ω is the angular velocity of the vehicle steering, x is the horizontal axis coordinate of the millimeter wave radar in the vehicle model, and y is the vertical axis coordinate of the millimeter wave radar in the vehicle model;
the motion speed vel of the sensor under the vehicle-mounted coordinate system normal And vel linear And converting into a radar coordinate system.
Optionally, in the vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method, the calculating step calculates a motion speed of a sensor installed on the vehicle under a radar coordinate system, projects a measurement point on a coordinate vector in the radar coordinate system, calculates a sum of the projection vector and a speed vector of the measurement point in the radar coordinate system, and records the sum as a first measurement speed vector; comprising the following steps:
acquiring the driving speed of a sensor under a radar coordinate system and the projection of a linear speed generated on a millimeter wave radar installation position under the vehicle coordinate system, which is caused by the angular speed of vehicle steering, on a coordinate vector of the measuring point in the radar coordinate system;
and calculating the running speed of the sensor under the radar coordinate system and the sum of the speed of the measuring point on the coordinate vector of the measuring point in the radar coordinate system and the speed of the measuring point in the radar coordinate system, which are caused by the angular speed of the steering of the vehicle and are generated at the millimeter wave radar installation position under the vehicle coordinate system, and recording the sum as a first measurement speed vector.
Optionally, in the vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method, determining whether the measuring point is stationary based on the first measuring speed vector includes:
and when the first measured speed vector is smaller than a preset speed scalar threshold, indicating that the measuring point is stationary.
Vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering device comprises:
the data acquisition unit is used for acquiring the calibration position of the millimeter wave radar in the vehicle model; acquiring the running speed and the steering angular speed of the vehicle; acquiring point cloud data detected by a millimeter wave radar; acquiring the measuring speed of a measuring point under a radar coordinate system;
a coordinate system conversion unit for converting a movement speed of a sensor mounted on a vehicle from a vehicle-mounted coordinate system to a radar coordinate system;
a speed calculation unit for calculating the sum of the projection of the movement speed of the sensor mounted on the vehicle on the coordinate vector of the measuring point in the radar coordinate system and the coordinate vector of the measuring point in the radar coordinate system, and recording the sum as a first measurement speed vector;
and the judging unit is used for judging whether the measuring point is static or not based on the first speed vector.
Optionally, in the vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering device, an origin of a coordinate system of the vehicle model is a midpoint of a rear wheel axle of the vehicle;
the horizontal axis of the coordinate system of the vehicle model is a straight line from the center point of the left wheel to the center point of the right wheel:
the vertical axis of the coordinate system of the vehicle model is a straight line from the origin of coordinates to the midpoint of the vehicle head;
the positive direction of the transverse axis of the coordinate system of the vehicle model is the direction from the left rear wheel to the right rear wheel;
the positive direction of the vertical axis of the coordinate system of the vehicle model is the vehicle advancing direction;
the calibration positions of the millimeter wave radar in the vehicle model comprise a transverse axis coordinate x and a longitudinal axis coordinate y of the millimeter wave radar in a coordinate system of the vehicle model and an included angle between the normal direction of the millimeter wave radar and the vehicle advancing direction.
Optionally, in the vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering device, acquiring a running speed of a vehicle and a steering angular speed includes:
the running speed of the vehicle and the steering angular speed are obtained through the vehicle central control or vehicle-mounted integrated navigation equipment.
Based on the above technical solution, according to the above solution provided by the embodiments of the present application, firstly, the motion speed of a sensor installed on a vehicle is converted from a vehicle-mounted coordinate system to a radar coordinate system by a vehicle sensor, the measurement speed of a measurement point under the radar coordinate system is obtained, the motion speed of the sensor installed on the vehicle under the radar coordinate system is calculated, the projection of the measurement point on a coordinate vector of the measurement point in the radar coordinate system is calculated, and the sum of the projection vector and the speed vector of the measurement point in the radar coordinate system is recorded as a first measurement speed vector; and finally judging whether the measuring point is static or not based on the first speed vector, wherein the measuring point is the point in the point cloud data, so that the identification of the point cloud data in a static state in the point cloud data is realized.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present application, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a dynamic-static separation filtering method for vehicle millimeter wave Lei Dadian cloud data, which is disclosed by the embodiment of the application;
fig. 2 is a schematic diagram of a millimeter wave radar according to an embodiment of the present application in a vehicle-mounted coordinate system;
FIG. 3 is a schematic diagram of a measurement point in a radar coordinate system;
fig. 4 is a schematic structural diagram of a vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In order to effectively distinguish dynamic and static data in point cloud data in a vehicle-mounted scene, the application discloses a vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering method, which can comprise the following steps of:
step S101: and acquiring a calibration position of the millimeter wave radar in the vehicle model.
In this step, the method is mainly used for positioning a vehicle-mounted millimeter wave radar, and is mainly used for measuring and obtaining a calibration position (x, y, angle) of the millimeter wave radar in a coordinate system corresponding to a vehicle model, wherein the angle refers to an included angle between a radar normal direction and a vehicle advancing direction, and the coordinate system corresponding to the vehicle model refers to a vehicle-mounted coordinate system, wherein, as shown in fig. 2, in the coordinate system, coordinates (x, y) take a midpoint of a rear wheel axle of a vehicle as a coordinate origin, a straight line from a center point of a left wheel to a center point of a right wheel as a transverse axis, and a straight line from the coordinate origin to the midpoint of the vehicle head as a longitudinal axis, and a direction from the left rear wheel to the right rear wheel as a transverse axis positive direction.
Referring to fig. 2, the radar refers to a millimeter wave radar (millimeter wave radar), radar normal direction refers to a radar normal, and the angle refers to an angle between a radar normal direction of the millimeter wave radar and a vehicle forward direction.
In the scheme, the calibration parameters of the millimeter wave radar under the vehicle-mounted coordinate system are set as follows:
radar cali ＝(x radar ，y radar ，angle radar )。
step S102: the running speed of the vehicle and the angular speed of the steering are acquired.
In the step, the running speed vel of the vehicle running can be obtained through the vehicle central control or vehicle-mounted integrated navigation equipment normal And the angular velocity ω at which the vehicle turns.
Step S103: and acquiring point cloud data detected by the millimeter wave radar.
Referring to fig. 3, in this step, as the point cloud data detected by the millimeter wave radar, measurement= (range, velocity, azimuth) is obtained, where Lei Dadian cloud data (range, azimuth) is the polar coordinates of the measuring point in the radar coordinate system, and velocity is the radial velocity of the measuring point relative to the radar.
Referring to fig. 3, the radar coordinate system is a polar coordinate system, a midpoint of the radar antenna is a pole (O), a radar normal (normal direction) is a polar axis, and a clockwise direction is positive. In fig. 3, the measurement point (measurement) has a coordinate of (range, azimuth) in the coordinate system. The measurement point velocity vector (vel) can be decomposed into radial (radial) and tangential (tangential) velocities (vel) redial ) And tangential velocity (vel) tangential ). In this scheme, the velocity (velocity) in the Lei Dadian cloud data is the radial velocity (vel) redial ). I.e. velocity of two side points = level radial Vel when equivalent measuring point moves far away from radar radial Positive and negative in the opposite direction.
Step S104: the movement speed of a sensor mounted on the vehicle is converted from an on-board coordinate system to a radar coordinate system.
In the technical scheme disclosed by the embodiment of the application, in a vehicle-mounted coordinate system, a sensor arranged on a vehicleThe movement speed is synthesized by two speeds: one is the velocity vel along the vehicle's central axis normal The method comprises the steps of carrying out a first treatment on the surface of the And secondly, the linear velocity vel generated by the angular velocity of the vehicle steering linear . Wherein:
the x and y are coordinate data of the sensor in a coordinate system of the vehicle;
and the vel_linear direction is perpendicular to the radar normal direction:
that is to say,
at the determination of the velocity vector vel linear And vel normal Thereafter, the velocity vector vel linear And vel normal And converting from the vehicle-mounted coordinate system to the radar coordinate system.
For a specific conversion process, reference may be made to the following process:
step S105: and obtaining the measuring speed of the measuring point under the radar coordinate system.
In the step, the polar coordinate information of the measuring point under the radar coordinate system is obtained through a millimeter wave radar, and m= (r) m ，v m ，a m ) Wherein said (r m ，v m ，a m ) It is understood that (range, capability, azimuth) is abbreviated, and simplified writing is performed by the initial of (range, capability, azimuth).
Step S106: and calculating the motion speed of a sensor installed on the vehicle under a radar coordinate system, projecting a measuring point on a coordinate vector of the measuring point in the radar coordinate system, calculating the sum of the projection vector and the speed vector of the measuring point in the radar coordinate system, and recording the sum as a first measuring speed vector.
In this step, the movement speed (vel) of the sensor on the vehicle is measured normal And vel linear ) After conversion to the radar coordinate system, the motion velocity (vel) of the sensor in the radar coordinate system normal And vel linear ) Coordinate vector (r) projected at measuring point in radar coordinate system m ，a m ) And (3) the following steps:
at this time, the movement speed vel of the sensor in the radar coordinate system normal And vel linear At (r) m ，a m ) Projection onto, and velocity direction v of measuring point m m In the same direction, calculating the sum speed of the three speeds in the direction, and taking the sum speed as a first measured speed vector combination ：
Step S107: and judging whether the measuring point is static or not based on the first speed vector.
In this step, it may be determined whether the measuring point is stationary based on the first velocity vector, for example, in a standard state, if the value of the first velocity vector is 0, it is indicated that the measuring point is stationary.
This is because:
let the normal vector of the radar beThe true velocity vector of the radar measuring point m isIf ideal, i.e. all measurements are absolutely accurate:
that is, if the product of the radar normal vector and the true velocity vector of the measuring point m is 0, the value of the first measured velocity vector is 0, and the measuring point m is in a stationary state.
In this step, the measurement point m is determined to be in a stationary state based on the determination method.
In the technical scheme disclosed in another embodiment of the present application, it is considered that under an application scenario, various measurement results are not absolutely accurate, so that the present application can preset a preset speed scalar threshold level threshold When level combination ＜vel threshold And when the measuring point is considered to be a stationary target.
In the embodiment, the vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering device is disclosed, and specific working contents of each unit in the device are referred to in the embodiment of the method, and the vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering device provided by the embodiment of the application is described below, and the vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering device described below and the vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering method described above can be referred to correspondingly.
Referring to fig. 4, the vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering device disclosed in the embodiment of the present application may include:
a data acquisition unit 100, corresponding to the above method, for acquiring a calibration position of the millimeter wave radar in the vehicle model; acquiring the running speed and the steering angular speed of the vehicle; acquiring point cloud data detected by a millimeter wave radar; acquiring the measuring speed of a measuring point under a radar coordinate system;
a coordinate system conversion unit 200, corresponding to the above method, for converting the movement speed of the sensor mounted on the vehicle from the vehicle-mounted coordinate system to the radar coordinate system;
a speed calculating unit 300, corresponding to the above method, for calculating a movement speed of the sensor mounted on the vehicle under the radar coordinate system, projecting the measurement point on a coordinate vector in the radar coordinate system, calculating a sum of the projection vector and the coordinate vector of the measurement point in the radar coordinate system, and recording as a first measurement speed vector;
the judging unit 400 is corresponding to the above method, and is configured to judge whether the measuring point is stationary based on the first velocity vector.
Corresponding to the method, the origin of the coordinate system of the vehicle model is the midpoint of the rear wheel axle of the vehicle;
the horizontal axis of the coordinate system of the vehicle model is a straight line from the center point of the left wheel to the center point of the right wheel:
the vertical axis of the coordinate system of the vehicle model is a straight line from the origin of coordinates to the midpoint of the vehicle head;
the positive direction of the transverse axis of the coordinate system of the vehicle model is the direction from the left rear wheel to the right rear wheel;
the positive direction of the vertical axis of the coordinate system of the vehicle model is the vehicle advancing direction;
the calibration positions of the millimeter wave radar in the vehicle model comprise a transverse axis coordinate x and a longitudinal axis coordinate y of the millimeter wave radar in a coordinate system of the vehicle model and an included angle between the normal direction of the millimeter wave radar and the vehicle advancing direction.
Corresponding to the method, in the device, the acquiring the running speed of the vehicle and the steering angular speed includes:
the running speed of the vehicle and the steering angular speed are obtained through the vehicle central control or vehicle-mounted integrated navigation equipment.
Corresponding to the method, the point cloud data comprises:
measuring the polar coordinates of the measuring point under a radar coordinate system;
the radial velocity of the measurement point relative to the millimeter wave radar.
Corresponding to the method, converting the motion speed of a sensor installed on a vehicle from a vehicle-mounted coordinate system to a radar coordinate system;
acquiring the movement speed of a sensor mounted on a vehicle: the speed control method comprises the steps of (1) carrying out level_normal and level_linear, wherein the level_normal is the speed of a sensor in the direction of a central axis of a vehicle, and the level_linear is the linear speed generated by the angular speed of steering of the vehicle;wherein ω is the angular velocity of the vehicle steering, x is the horizontal axis coordinate of the millimeter wave radar in the vehicle model, and y is the vertical axis coordinate of the millimeter wave radar in the vehicle model;
and converting the motion speeds vel_normal and vel_linear of the sensor under the vehicle-mounted coordinate system into the radar coordinate system.
Corresponding to the method, the calculating the motion speed of the sensor installed on the vehicle under the radar coordinate system, projecting the projection vector on the coordinate vector of the measuring point in the radar coordinate system, calculating the sum of the projection vector and the coordinate vector of the measuring point in the radar coordinate system, and recording as a first measuring speed vector comprises:
acquiring the driving speed of a sensor under a radar coordinate system and the projection of a linear speed generated on a millimeter wave radar installation position under the vehicle coordinate system, which is caused by the angular speed of vehicle steering, on a coordinate vector of the measuring point in the radar coordinate system;
and calculating the running speed of the sensor under the radar coordinate system and the sum of the speed of the measuring point on the coordinate vector of the measuring point in the radar coordinate system and the speed of the measuring point in the radar coordinate system, which are caused by the angular speed of the steering of the vehicle and are generated at the millimeter wave radar installation position under the vehicle coordinate system, and recording the sum as a first measurement speed vector.
Corresponding to the method, the determining whether the measuring point is stationary based on the first measured velocity vector includes:
when the first measured velocity vector is less than a velocity scalar threshold, the measurement point is indicated to be stationary.
Through verification, the point cloud data in a static state in the point cloud data can be effectively identified by the scheme disclosed by the embodiment of the application.
For convenience of description, the above system is described as being functionally divided into various modules, respectively. Of course, the functions of each module may be implemented in the same piece or pieces of software and/or hardware when implementing the present application.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for a system or system embodiment, since it is substantially similar to a method embodiment, the description is relatively simple, with reference to the description of the method embodiment being made in part. The systems and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present application without undue burden.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in Random Access Memory (RAM), memory, read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
It is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (8)
1. The vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method is characterized by comprising the following steps of:
acquiring a calibration position of the millimeter wave radar in a vehicle model;
acquiring the running speed and the steering angular speed of the vehicle;
acquiring point cloud data detected by a millimeter wave radar;
acquiring the movement speed of a sensor mounted on a vehicle under a vehicle-mounted coordinate system: level (v) normal And vel linear Wherein, the level normal For the speed of the sensor in the direction of the central axis of the vehicle, said level linear A linear velocity generated for an angular velocity of vehicle steering;wherein ω is the angular velocity of the vehicle steering, x is the horizontal axis coordinate of the millimeter wave radar in the vehicle model, and y is the vertical axis coordinate of the millimeter wave radar in the vehicle model;
the motion speed vel of the sensor under the vehicle-mounted coordinate system normal And vel linear Converting into a radar coordinate system;
acquiring the measuring speed of a measuring point under a radar coordinate system;
acquiring the driving speed of a sensor under a radar coordinate system and the projection of a linear speed generated on a millimeter wave radar installation position under the vehicle coordinate system, which is caused by the angular speed of vehicle steering, on a coordinate vector of the measuring point in the radar coordinate system;
calculating the running speed of a sensor under the radar coordinate system and the sum of the speed of a linear speed generated on a millimeter wave radar installation position under the vehicle coordinate system, which is caused by the angular speed of vehicle steering, on a coordinate vector of the measuring point in the radar coordinate system and the speed of the measuring point in the radar coordinate system, and recording the sum as a first measurement speed vector;
and judging whether the measuring point is static or not based on the first measuring speed vector.
2. The vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method according to claim 1, wherein an origin of a coordinate system of the vehicle model is a midpoint of a rear wheel axle of the vehicle;
the horizontal axis of the coordinate system of the vehicle model is a straight line from the center point of the left wheel to the center point of the right wheel:
the vertical axis of the coordinate system of the vehicle model is a straight line from the origin of coordinates to the midpoint of the vehicle head;
the positive direction of the transverse axis of the coordinate system of the vehicle model is the direction from the left rear wheel to the right rear wheel;
the positive direction of the vertical axis of the coordinate system of the vehicle model is the vehicle advancing direction;
the calibration positions of the millimeter wave radar in the vehicle model comprise a transverse axis coordinate x and a longitudinal axis coordinate y of the millimeter wave radar in a coordinate system of the vehicle model and an included angle between the normal direction of the millimeter wave radar and the vehicle advancing direction.
3. The vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering method according to claim 1, wherein obtaining the running speed of the vehicle and the steering angular speed comprises:
the running speed of the vehicle and the steering angular speed are obtained through the vehicle central control or vehicle-mounted integrated navigation equipment.
4. The vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method according to claim 1, wherein the point cloud data comprises:
measuring the polar coordinates of the measuring point under a radar coordinate system;
the radial velocity of the measurement point relative to the millimeter wave radar.
5. The vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering method of claim 3, wherein determining whether the measurement point is stationary based on the first measurement velocity vector comprises:
and when the first measured speed vector is smaller than a preset speed scalar threshold, indicating that the measuring point is stationary.
6. Vehicle millimeter wave Lei Dadian cloud data dynamic and static separation filter device, which is characterized by comprising:
the data acquisition unit is used for acquiring the calibration position of the millimeter wave radar in the vehicle model; acquiring the running speed and the steering angular speed of the vehicle; acquiring point cloud data detected by a millimeter wave radar; acquiring the measuring speed of a measuring point under a radar coordinate system;
a coordinate system conversion unit for acquiring a movement speed of a sensor mounted on the vehicle in a vehicle-mounted coordinate system: level (v) normal And vel linear Wherein, the level normal For the speed of the sensor in the direction of the central axis of the vehicle, said level linear A linear velocity generated for an angular velocity of vehicle steering;wherein ω is the angular velocity of the vehicle steering, x is the horizontal axis coordinate of the millimeter wave radar in the vehicle model, and y is the vertical axis coordinate of the millimeter wave radar in the vehicle model; the motion speed vel of the sensor under the vehicle-mounted coordinate system normal And vel linear Converting into a radar coordinate system;
a speed calculation unit for acquiring a running speed of the sensor in a radar coordinate system and a projection of a linear speed generated at a millimeter wave radar installation position in the vehicle coordinate system, caused by an angular speed of vehicle steering, on a coordinate vector of the measuring point in the radar coordinate system; calculating the running speed of a sensor under the radar coordinate system and the sum of the speed of a linear speed generated on a millimeter wave radar installation position under the vehicle coordinate system, which is caused by the angular speed of vehicle steering, on a coordinate vector of the measuring point in the radar coordinate system and the speed of the measuring point in the radar coordinate system, and recording the sum as a first measurement speed vector;
and the judging unit is used for judging whether the measuring point is static or not based on the first measuring speed vector.
7. The vehicle millimeter wave Lei Dadian cloud data dynamic-static separation filtering device according to claim 6, wherein an origin of a coordinate system of the vehicle model is a midpoint of a rear wheel axle of the vehicle;
the horizontal axis of the coordinate system of the vehicle model is a straight line from the center point of the left wheel to the center point of the right wheel:
the vertical axis of the coordinate system of the vehicle model is a straight line from the origin of coordinates to the midpoint of the vehicle head;
the positive direction of the transverse axis of the coordinate system of the vehicle model is the direction from the left rear wheel to the right rear wheel;
the positive direction of the vertical axis of the coordinate system of the vehicle model is the vehicle advancing direction;
the calibration positions of the millimeter wave radar in the vehicle model comprise a transverse axis coordinate x and a longitudinal axis coordinate y of the millimeter wave radar in a coordinate system of the vehicle model and an included angle between the normal direction of the millimeter wave radar and the vehicle advancing direction.
8. The vehicle-mounted millimeter wave Lei Dadian cloud data dynamic-static separation filtering device according to claim 6, wherein obtaining the running speed of the vehicle and the angular speed of steering comprises:
the running speed of the vehicle and the steering angular speed are obtained through the vehicle central control or vehicle-mounted integrated navigation equipment.
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