CN111776948B - Tire crane positioning method and device - Google Patents
Tire crane positioning method and device Download PDFInfo
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- CN111776948B CN111776948B CN202010621767.1A CN202010621767A CN111776948B CN 111776948 B CN111776948 B CN 111776948B CN 202010621767 A CN202010621767 A CN 202010621767A CN 111776948 B CN111776948 B CN 111776948B
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/18—Control systems or devices
- B66C13/48—Automatic control of crane drives for producing a single or repeated working cycle; Programme control
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/16—Applications of indicating, registering, or weighing devices
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B66—HOISTING; LIFTING; HAULING
- B66C—CRANES; LOAD-ENGAGING ELEMENTS OR DEVICES FOR CRANES, CAPSTANS, WINCHES, OR TACKLES
- B66C13/00—Other constructional features or details
- B66C13/18—Control systems or devices
- B66C13/46—Position indicators for suspended loads or for crane elements
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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
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
- G01S17/42—Simultaneous measurement of distance and other co-ordinates
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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
- G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
- G01S17/06—Systems determining position data of a target
- G01S17/46—Indirect determination of position data
- G01S17/48—Active triangulation systems, i.e. using the transmission and reflection of electromagnetic waves other than radio waves
Abstract
The application provides a tire crane positioning method and device, is applied to tire crane positioning system, tire crane positioning system includes: the method comprises the steps that the reflecting plate is fixedly installed on the tire crane, the 40-line laser radar is installed on the intelligent heavy truck, the 40-line laser radar is used for sensing the environment of the intelligent heavy truck and extracting point cloud data with reflectivity larger than a set threshold value, accurate positioning of the position of the tire crane in the port automatic loading and unloading process is achieved, the tire crane meets the positioning accuracy requirement of the tire crane, and therefore the intelligent heavy truck is assisted to complete port automatic loading and unloading operation.
Description
Technical Field
The application relates to the technical field of intelligent transportation, in particular to a tire crane positioning method and device, which can be used for realizing accurate positioning of the position of a tire crane in the process of automatically loading and unloading a box at a port and assisting an intelligent heavy truck to finish the operation of automatically loading and unloading the box at the port.
Background
In the technical field of intelligent transportation, port automation is taken as a key technology, a port automation loading and unloading box is a very key step in the port automation operation process, in the port automation loading and unloading box process, the relative position relation between an intelligent heavy truck and a tire crane needs to be accurately positioned in real time, and then the port automation loading and unloading box operation is carried out on the basis, so that the positioning precision of the tire crane needs to meet certain requirements.
At present, the conventional tire crane positioning method on the intelligent transportation vehicle comprises the following steps: the method comprises the following steps that an inertial navigation and GPS combined system, an UWB (Ultra Wide Band) system and an SLAM (simultaneous localization and mapping) map building and positioning method are adopted, wherein the inertial navigation and GPS combined system is mainly used for vehicle global positioning, the positioning precision is about 20 cm, and if a container in a tire crane area shields a GPS signal, the positioning result is easy to generate jump; the UWB system is mainly used for vehicle local positioning, but the UWB system is expensive, and environmental factors such as temperature, humidity and the like easily influence UWB signals, so that the use conditions of the UWB signals are influenced; the positioning is realized by adopting a laser radar SLAM mapping positioning method, the positioning accuracy is about 10 cm, the influence of the change of the surrounding environment is easy to realize, and the positioning requirement of the port automatic loading and unloading process is difficult to meet.
Therefore, how to realize accurate positioning of the tire crane position in the port automatic loading and unloading box process and make the tire crane meet the positioning accuracy requirement of the tire crane, so as to assist the intelligent heavy truck to complete port automatic loading and unloading box operation, which is a technical problem to be solved by technical personnel in the field.
Disclosure of Invention
The application provides a tire crane positioning method and device, aiming at: how to realize the accurate location of tire crane position among the automatic loading and unloading case in harbour, make it satisfy the positioning accuracy requirement of tire crane to help intelligent heavy truck to accomplish the automatic loading and unloading case operation in harbour.
In order to achieve the above object, the present application provides the following technical solutions:
a tire crane positioning method is applied to a tire crane positioning system, and the tire crane positioning system comprises: the method comprises the following steps of installing a 40-line laser radar on a smart heavy truck and a reflecting plate fixedly installed on a tire crane, wherein the reflecting plate is structurally and rigidly connected with the tire crane, and the method comprises the following steps:
acquiring point cloud data of the 40-line laser radar, and extracting point cloud reflectivity characteristics of the surface of the reflecting plate, wherein the point cloud reflectivity characteristics are point cloud data with reflectivity larger than a set threshold value;
filtering and surface fitting are carried out on the point cloud reflectivity characteristics to obtain the relative position relation between the fitting plane of the reflecting plate and the 40-line laser radar coordinate system;
and performing coordinate conversion through calibration parameters of the 40-line laser radar and the intelligent heavy card coordinate system to obtain the accurate position relation between the tyre crane and the intelligent heavy card.
Preferably, the acquiring point cloud data of the 40-line laser radar and extracting the point cloud reflectivity characteristics of the surface of the reflector plate includes:
the method comprises the steps of obtaining original laser radar point cloud data of the 40-line laser radar, wherein the original laser radar point cloud data comprise three-dimensional space coordinate positions and reflectivity information;
setting an algorithm application distance space range according to the position relation between the tire crane reflecting plate and the intelligent heavy card, removing irrelevant area data, and extracting a point cloud interesting area;
extracting the reflectivity of the point cloud interesting area within a preset threshold range to obtain reflectivity characteristics;
and according to the point cloud interesting area and the division of the reflectivity characteristics, filtering the original laser radar point cloud data to obtain the point cloud reflectivity characteristics of the surface of the reflecting plate.
Preferably, the filtering and surface fitting the point cloud reflectivity characteristics to obtain the relative position relationship between the fitting plane of the reflector and the 40-line laser radar coordinate system includes:
clustering the point cloud reflectivity characteristics of the surface of the reflector plate according to the spatial distance relationship between the points in the original laser radar point cloud data by using an Euclidean distance method to obtain a clustering result;
calculating a peripheral three-dimensional contour of the clustering result, and determining point cloud data contained in the peripheral three-dimensional contour, wherein the length and the width of the peripheral three-dimensional contour correspond to the size of the reflecting plate, and the thickness of the peripheral three-dimensional contour is set in a preset range;
and performing plane fitting on the point cloud data contained in the peripheral three-dimensional contour by using an RANSC algorithm to obtain a relative position relation between a fitting plane of the reflector and the 40-line laser radar coordinate system, and determining that a first plane equation is ax + by + cz + d as 0.
Preferably, the obtaining of the precise position relationship between the tire crane and the intelligent heavy truck by performing coordinate conversion on the calibration parameters of the 40-line laser radar and the intelligent heavy truck coordinate system comprises:
combining the first plane equation and the calibration parameters of the 40-line laser radar and the intelligent heavy truck coordinate system, and calculating a second plane equation a 'x + b' y + c 'z + d' 0, wherein the second plane equation is used for expressing a plane equation expression result under the reflector and the intelligent heavy truck coordinate system;
and selecting the center point coordinate of the clustering result, substituting the center point coordinate into the second plane equation, and calculating the position d' of the center point coordinate under the intelligent heavy-duty truck coordinate system to obtain the accurate position relation between the tire crane and the intelligent heavy-duty truck.
A tire crane positioning device is applied to a tire crane positioning system, and the tire crane positioning system comprises: the device comprises a 40-line laser radar arranged on a smart heavy truck and a reflecting plate fixedly arranged on a tire crane, wherein the reflecting plate is structurally and rigidly connected with the tire crane, and the device comprises:
the first processing unit is used for acquiring point cloud data of the 40-line laser radar and extracting point cloud reflectivity characteristics of the surface of the reflecting plate, wherein the point cloud reflectivity characteristics are point cloud data with reflectivity larger than a set threshold value;
the second processing unit is used for filtering and surface fitting the point cloud reflectivity characteristics to obtain the relative position relation between the fitting plane of the reflecting plate and the 40-line laser radar coordinate system;
and the third processing unit is used for carrying out coordinate conversion through the calibration parameters of the 40-line laser radar and the intelligent heavy card coordinate system to obtain the accurate position relation between the tyre crane and the intelligent heavy card.
Preferably, the first processing unit is specifically configured to:
the method comprises the steps of obtaining original laser radar point cloud data of the 40-line laser radar, wherein the original laser radar point cloud data comprise three-dimensional space coordinate positions and reflectivity information;
setting an algorithm application distance space range according to the position relation between the tire crane reflecting plate and the intelligent heavy card, removing irrelevant area data, and extracting a point cloud interesting area;
extracting the reflectivity of the point cloud interesting area within a preset threshold range to obtain reflectivity characteristics;
and according to the point cloud interesting area and the division of the reflectivity characteristics, filtering the original laser radar point cloud data to obtain the point cloud reflectivity characteristics of the surface of the reflecting plate.
Preferably, the second processing unit is specifically configured to:
clustering the point cloud reflectivity characteristics of the surface of the reflector plate according to the spatial distance relationship between the points in the original laser radar point cloud data by using an Euclidean distance method to obtain a clustering result;
calculating a peripheral three-dimensional contour of the clustering result, and determining point cloud data contained in the peripheral three-dimensional contour, wherein the length and the width of the peripheral three-dimensional contour correspond to the size of the reflecting plate, and the thickness of the peripheral three-dimensional contour is set in a preset range;
and performing plane fitting on the point cloud data contained in the peripheral three-dimensional contour by using an RANSC algorithm to obtain a relative position relation between a fitting plane of the reflector and the 40-line laser radar coordinate system, and determining that a first plane equation is ax + by + cz + d as 0.
Preferably, the third processing unit is specifically configured to:
combining the first plane equation and the calibration parameters of the 40-line laser radar and the intelligent heavy truck coordinate system, and calculating a second plane equation a 'x + b' y + c 'z + d' 0, wherein the second plane equation is used for expressing a plane equation expression result under the reflector and the intelligent heavy truck coordinate system;
and selecting the center point coordinate of the clustering result, substituting the center point coordinate into the second plane equation, and calculating the position d' of the center point coordinate under the intelligent heavy-duty truck coordinate system to obtain the accurate position relation between the tire crane and the intelligent heavy-duty truck.
A storage medium comprising a stored program, wherein the program, when executed, controls an apparatus on which the storage medium is located to perform a method of tyre crane positioning as described above.
An electronic device comprising at least one processor, and at least one memory, bus connected to the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to invoke program instructions in the memory to perform the tire crane positioning method described above.
The application provides a tire crane positioning method and device, which are applied to a tire crane positioning system, wherein the tire crane positioning system comprises: the method comprises the steps that a 40-line laser radar arranged on an intelligent heavy truck and a reflecting plate fixedly arranged on a tire crane are arranged, rigid connection is required to be kept between the reflecting plate and the tire crane on the structure, the method comprises the steps of obtaining point cloud data of the 40-line laser radar and extracting point cloud reflectivity characteristics of the surface of the reflecting plate; filtering and surface fitting are carried out on the point cloud reflectivity characteristics to obtain the relative position relation between the fitting plane of the reflecting plate and the 40-line laser radar coordinate system; and performing coordinate conversion through calibration parameters of the 40-line laser radar and the intelligent heavy card coordinate system to obtain the accurate position relation between the tyre crane and the intelligent heavy card.
This application is through with reflecting plate fixed mounting on the tire crane, and 40 line laser radar installs on the intelligent heavy truck, adopts 40 line laser radar to the perception of intelligent heavy truck environment to extract the point cloud data that the reflectivity is greater than the settlement threshold value, realized that harbour automation loading and unloading case in-process tire crane position is accurate to be fixed a position, make it satisfy the positioning accuracy requirement of tire crane, thereby help intelligent heavy truck to accomplish harbour automation loading and unloading case operation.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic structural diagram of a tire crane positioning system according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of a method for positioning a tire crane according to an embodiment of the present disclosure;
fig. 3 is a schematic flow chart of a specific implementation manner of acquiring point cloud data of the 40-line laser radar and extracting a point cloud reflectivity characteristic of the surface of the reflector plate according to the embodiment of the present application;
fig. 4 is a schematic flow chart of a specific implementation manner of performing filtering and surface fitting on the point cloud reflectivity features to obtain a relative positional relationship between the fitting plane of the reflector and the 40-line lidar coordinate system according to the embodiment of the present application;
fig. 5 is a schematic flow chart of a specific implementation manner of obtaining an accurate position relationship between the tire crane and the smart heavy card through coordinate conversion of calibration parameters of the 40-line laser radar and the smart heavy card coordinate system according to the embodiment of the present application;
FIG. 6 is a schematic structural diagram of a positioning device of a tire crane according to an embodiment of the present disclosure;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The application provides a tire crane positioning method and device, which are applied to a tire crane positioning system of an application scene shown in figure 1, wherein the tire crane positioning system comprises: the system comprises a 40-line laser radar 10 installed on the intelligent heavy truck and a reflecting plate 20 fixedly installed on a tire crane, wherein the reflecting plate 20 and the tire crane are structurally and rigidly connected. The relative position relation between the 40-line laser radar 10 and the reflecting plate 20 is calibrated, and the reflecting plate 20 can be scanned by the 40-line laser radar 10 when the intelligent heavy card passes by being hung from a tire. The 40-line laser radar 10 is used for collecting point cloud data, when the intelligent heavy truck passes through the tire crane, the point cloud data scanned by the 40-line laser radar 10 are read in, the point cloud data volume is required to be not less than 600, and laser radar wiring harnesses are required to cover most of the area of the surface of the reflecting plate. The reflecting plate 20 is made of 3M high-reflection material, so that the gain reflection effect is realized, and the flatness of the reflecting plate is within 2 mm.
The invention of this application aims at: how to realize the accurate location of tire crane position among the automatic loading and unloading case in harbour, make it satisfy the positioning accuracy requirement of tire crane to help intelligent heavy truck to accomplish the automatic loading and unloading case operation in harbour.
The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
As shown in fig. 2, an embodiment of the present application provides a flowchart of a tire crane positioning method, and the embodiment of the present application provides a tire crane positioning method applied to a tire crane positioning system, where the tire crane positioning system includes: the method comprises the following steps that a 40-line laser radar arranged on the intelligent heavy truck and a reflecting plate fixedly arranged on a tire crane are arranged, the reflecting plate and the tire crane are structurally required to be in rigid connection, accurate positioning of the tire crane position in the process of port automatic loading and unloading is realized, and the intelligent heavy truck is assisted to complete port automatic loading and unloading operation, and the method specifically comprises the following steps:
s201: and acquiring point cloud data of the 40-line laser radar, and extracting point cloud reflectivity characteristics of the surface of the reflecting plate, wherein the point cloud reflectivity characteristics are point cloud data with reflectivity larger than a set threshold value.
Before the 40-line laser radar acquires the point cloud data, the installation of the 40-line laser radar and the design, installation and calibration of the reflecting plate need to be completed.
It should be noted that the 40-line laser radar is installed on the smart heavy card, and the 40-line laser radar can output 360-degree environment-band spatial position information and point cloud data of the reflectivity.
In the embodiment of the application, the size of the reflecting plate can be designed according to the size of the view field when the intelligent heavy card passes by the tire crane, so that the number of the point cloud data of the laser radar scanned on the reflecting plate is not less than 600, and most of the area of the surface of the reflecting plate can be effectively covered; the reflecting plate surface material that this application adopted is 3M high reflecting material, can realize gain reflection effect, and the reflecting plate roughness is within 2 millimeters.
The reflecting plate is installed and fixed on the tire crane, the relative position relation between the reflecting plate and the tire crane is calibrated, the fact that when the intelligent heavy card passes under the tire crane is guaranteed, the laser radar can scan the reflecting plate, point cloud data of 40-line laser radar are recorded and stored in a memory space, then when the position of the tire crane is located, the point cloud data in the memory space are traversed, point cloud data with the reflectivity larger than a set threshold value are extracted, and point cloud data which are in accordance with the characteristics of the reflecting plate fixed on the tire crane are selected.
S202: and filtering and surface fitting the point cloud reflectivity characteristics to obtain the relative position relation between the fitting plane of the reflecting plate and the 40-line laser radar coordinate system.
Before calculating the relative position relationship between the fitting plane and the radar coordinate system, filtering the point cloud reflectivity characteristics obtained in step S201, filtering noise interference, screening out high reflector characteristic data fixed on the tire crane, clustering the filtered data, selecting data clusters belonging to the reflector, performing surface fitting on the filtered and clustered data, and calculating the relative position relationship between the fitting plane of the reflector and the 40-line laser radar coordinate system.
It should be noted that the above-mentioned related technologies such as filtering, clustering, and surface fitting belong to the technical means known to those skilled in the art, and in the embodiment of the present application, detailed descriptions of specific implementation manners are not repeated.
S203: and performing coordinate conversion through calibration parameters of the 40-line laser radar and the intelligent heavy card coordinate system to obtain the accurate position relation between the tyre crane and the intelligent heavy card.
On the basis of statistical classification of the point cloud data of the laser radar, the calculation of the relative position relationship between the intelligent heavy truck and the tire crane is realized through coordinate conversion by using the RANSC (random Sample consensus) plane fitting algorithm principle and calibration parameters between the 40-line laser radar and the coordinate system of the intelligent heavy truck.
The embodiment of the application provides a tire crane positioning method, which is applied to a tire crane positioning system, wherein the tire crane positioning system comprises: the method comprises the steps that the reflecting plate is fixedly installed on the tire crane, the 40-line laser radar is installed on the intelligent heavy truck, the 40-line laser radar is used for sensing the environment of the intelligent heavy truck and extracting point cloud data with reflectivity larger than a set threshold value, accurate positioning of the position of the tire crane in the port automatic loading and unloading process is achieved, the tire crane meets the positioning accuracy requirement of the tire crane, and therefore the intelligent heavy truck is assisted to complete port automatic loading and unloading operation.
Further, as shown in fig. 3, a specific implementation manner for acquiring the point cloud data of the 40-line laser radar and extracting the point cloud reflectivity characteristics of the surface of the reflector provided by the embodiment of the present application specifically includes the following steps:
s301: and acquiring original laser radar point cloud data of the 40-line laser radar, wherein the original laser radar point cloud data comprises three-dimensional space coordinate position and reflectivity information.
S302: and setting an algorithm application distance space range according to the position relation between the reflecting plate of the tyre crane and the intelligent heavy card, removing irrelevant area data, and extracting a point cloud interesting area.
S303: and extracting the reflectivity of the point cloud interesting area within a preset threshold range to obtain reflectivity characteristics.
The reflectivity range of the lidar is generally between 0 and 255, and in the embodiment of the application, since the surface of the reflector fixed on the tire crane is made of 3M high-reflectivity material, the reflectivity selection threshold is set to be between 150 and 200 in order to facilitate distinguishing from the surrounding environment characteristics.
S304: and according to the point cloud interesting area and the division of the reflectivity characteristics, filtering the original laser radar point cloud data to obtain the point cloud reflectivity characteristics of the surface of the reflecting plate.
According to the embodiment of the application, the reflecting plate is installed and fixed on the tire crane, the relative position relation between the reflecting plate and the tire crane is calibrated, the condition that the intelligent heavy card passes under the tire crane is guaranteed, the laser radar can scan the reflecting plate, point cloud data of 40-line laser radar is recorded and stored in a memory space, then when the position of the tire crane is located, the point cloud data in the memory space is traversed, the point cloud data with the reflectivity larger than a set threshold value is extracted, and the point cloud data which are consistent with the characteristic point cloud data of the high reflecting plate fixed on the tire crane are selected.
Further, as shown in fig. 4, a specific implementation manner for performing filtering and surface fitting on the point cloud reflectivity features to obtain a relative position relationship between the fitting plane of the reflector and the 40-line laser radar coordinate system, which is provided by the embodiment of the present application, specifically includes the following steps:
s401: and clustering the point cloud reflectivity characteristics of the surface of the reflector plate according to the spatial distance relationship between the points in the original laser radar point cloud data by using an Euclidean distance method to obtain a clustering result.
S402: and calculating a peripheral three-dimensional contour of the clustering result, determining point cloud data contained in the peripheral three-dimensional contour, wherein the length and the width of the peripheral three-dimensional contour correspond to the size of the reflecting plate, and the thickness of the peripheral three-dimensional contour is set in a preset range.
S403: and performing plane fitting on the point cloud data contained in the peripheral three-dimensional contour by using an RANSC algorithm to obtain a relative position relation between a fitting plane of the reflector and the 40-line laser radar coordinate system, and determining that a first plane equation is ax + by + cz + d as 0.
It should be noted that the clustering step length may be selected to be 0.2 m, and the thickness of the peripheral three-dimensional profile is set within a preset range, which is selected to be 0.2 m.
Before calculating the relative position relationship between the fitting plane and the radar coordinate system, filtering the point cloud reflectivity characteristics obtained in step S201, filtering noise interference, screening out high reflector characteristic data fixed on the tire crane, clustering the filtered data by using the euclidean distance method, selecting data clusters belonging to the reflector, performing surface fitting on the filtered and clustered data by using the RANSC algorithm, and calculating the relative position relationship between the fitting plane of the reflector and the 40-line laser radar coordinate system.
It should be noted that the above-mentioned filtering processing, clustering processing by using an euclidean distance method, and fitting by using an RANSC algorithm surface, and other related technologies belong to technical means known to those skilled in the art, and in the embodiment of the present application, detailed descriptions of specific implementation manners are not repeated.
Further, as shown in fig. 5, a specific implementation manner for obtaining the precise position relationship between the tire crane and the smart heavy card by performing coordinate transformation on the calibration parameters of the coordinate systems of the 40-line laser radar and the smart heavy card provided in the embodiment of the present application specifically includes the following steps:
s501: and calculating a second plane equation a 'x + b' y + c 'z + d' which is 0 by combining the first plane equation and the calibration parameters of the 40-line laser radar and smart heavy truck coordinate system, wherein the second plane equation is used for expressing a plane equation expression result under the reflector and smart heavy truck coordinate system.
S502: and selecting the center point coordinate of the clustering result, substituting the center point coordinate into the second plane equation, and calculating the position d' of the center point coordinate under the intelligent heavy-duty truck coordinate system to obtain the accurate position relation between the tire crane and the intelligent heavy-duty truck.
On the basis of statistical classification of the point cloud data of the laser radar, the calculation of the relative position relation between the intelligent heavy truck and the tire crane is realized through coordinate conversion by using the RANSC (random Sample consensus) plane fitting algorithm principle and calibration parameters between the 40-line laser radar and the coordinate system of the intelligent heavy truck.
Referring to fig. 6, based on the tire crane positioning method disclosed in the foregoing embodiment, the present embodiment correspondingly discloses a tire crane positioning device, which is applied to a tire crane positioning system, where the tire crane positioning system includes: install 40 line lidar on the smart heavy truck and the reflecting plate of fixed mounting on the tire crane, the reflecting plate with structural rigid connection that needs to keep between the tire crane, the device specifically includes: a first processing unit 601, a second processing unit 602, and a third processing unit 603, wherein:
the first processing unit 601 is configured to acquire point cloud data of the 40-line laser radar, and extract a point cloud reflectivity feature of the surface of the reflector, where the point cloud reflectivity feature is point cloud data with a reflectivity greater than a set threshold.
A second processing unit 602, configured to perform filtering and surface fitting on the point cloud reflectivity features to obtain a relative position relationship between the fitting plane of the reflector and the 40-line lidar coordinate system.
And the third processing unit 603 is configured to perform coordinate conversion on the calibration parameters of the 40-line laser radar and the intelligent heavy card coordinate system to obtain an accurate position relationship between the tire crane and the intelligent heavy card.
The first processing unit 601 is specifically configured to:
and acquiring original laser radar point cloud data of the 40-line laser radar, wherein the original laser radar point cloud data comprises three-dimensional space coordinate position and reflectivity information.
And setting an algorithm application distance space range according to the position relation between the reflecting plate of the tyre crane and the intelligent heavy card, removing irrelevant area data, and extracting a point cloud interesting area.
And extracting the reflectivity of the point cloud interesting area within a preset threshold range to obtain reflectivity characteristics.
And according to the point cloud interesting area and the division of the reflectivity characteristics, filtering the original laser radar point cloud data to obtain the point cloud reflectivity characteristics of the surface of the reflecting plate.
The second processing unit 602 is specifically configured to:
and clustering the point cloud reflectivity characteristics of the surface of the reflector plate according to the spatial distance relationship between the points in the original laser radar point cloud data by using an Euclidean distance method to obtain a clustering result.
And calculating a peripheral three-dimensional contour of the clustering result, and determining point cloud data contained in the peripheral three-dimensional contour, wherein the length and the width of the peripheral three-dimensional contour correspond to the size of the reflecting plate, and the thickness of the peripheral three-dimensional contour is set in a preset range.
And performing plane fitting on the point cloud data contained in the peripheral three-dimensional contour by using an RANSC algorithm to obtain a relative position relation between a fitting plane of the reflector and the 40-line laser radar coordinate system, and determining that a first plane equation is ax + by + cz + d as 0.
The third processing unit 603 is specifically configured to:
combining the first plane equation and the calibration parameters of the 40-line laser radar and the intelligent heavy truck coordinate system, and calculating a second plane equation a 'x + b' y + c 'z + d' 0, wherein the second plane equation is used for expressing a plane equation expression result under the reflector and the intelligent heavy truck coordinate system;
and selecting the center point coordinate of the clustering result, substituting the center point coordinate into the second plane equation, and calculating the position d' of the center point coordinate under the intelligent heavy-duty truck coordinate system to obtain the accurate position relation between the tire crane and the intelligent heavy-duty truck.
The tire crane positioning device comprises a processor and a memory, wherein the first processing unit, the second processing unit, the third processing unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions.
The processor comprises a kernel, and the kernel calls the corresponding program unit from the memory. The inner core can be set to one or more than one, the reflecting plate is fixedly installed on the tire crane, the 40-line laser radar is installed on the intelligent heavy truck, the 40-line laser radar is used for sensing the environment of the intelligent heavy truck, and the point cloud data with the reflectivity larger than a set threshold value is extracted, so that the accurate positioning of the position of the tire crane in the port automatic loading and unloading process is realized, the requirement of the positioning accuracy of the tire crane is met, and the intelligent heavy truck is assisted to complete the operation of the port automatic loading and unloading box.
Embodiments of the present application further provide a tire crane positioning system, which may be configured as shown in fig. 1, where:
the embodiment of the present application provides a tire crane positioning system, the structure of which can be referred to fig. 1, in the tire crane positioning system, including: the system comprises a 40-line laser radar 10 installed on the intelligent heavy truck and a reflecting plate 20 fixedly installed on a tire crane, wherein the reflecting plate 20 and the tire crane are structurally and rigidly connected. The relative position relation between the 40-line laser radar 10 and the reflecting plate 20 is calibrated, and the reflecting plate 20 can be scanned by the 40-line laser radar 10 when the intelligent heavy card passes by being hung from a tire. The 40-line laser radar 10 is used for collecting point cloud data, when the intelligent heavy truck passes through the tire crane, the point cloud data scanned by the 40-line laser radar 10 are read in, the point cloud data volume is required to be not less than 600, and laser radar wiring harnesses are required to cover most of the area of the surface of the reflecting plate. The reflecting plate 20 is made of 3M high-reflection material, so that the gain reflection effect is realized, and the flatness of the reflecting plate is within 2 mm.
The utility model provides a pair of tire crane positioning system, through with reflecting plate fixed mounting on the tire crane, 40 lines of laser radar install on the intelligent heavy truck, adopt 40 lines of laser radar to the perception of intelligent heavy truck environment to extract the point cloud data that the reflectivity is greater than the settlement threshold value, realized the automatic case in-process tire crane position accurate location of harbour, make it satisfy the positioning accuracy requirement of tire crane, thereby help the automatic case operation of unloading of harbour of accomplishing of intelligent heavy truck.
Embodiments of the present application provide a storage medium having a program stored thereon, which when executed by a processor, implements the tire crane positioning method.
The embodiment of the application provides a processor for running a program, wherein the program executes the tire crane positioning method during running.
An embodiment of the present application provides an electronic device, as shown in fig. 7, the electronic device 70 includes at least one processor 701, and at least one memory 702 and a bus 703, which are connected to the processor; the processor 701 and the memory 702 complete communication with each other through the bus 703; the processor 701 is configured to call program instructions in the memory 702 to perform the tire crane positioning method described above.
The electronic device herein may be a server, a PC, a PAD, a mobile phone, etc.
The present application further provides a computer program product adapted to perform a program for initializing the following method steps when executed on a data processing device:
acquiring point cloud data of the 40-line laser radar, and extracting point cloud reflectivity characteristics of the surface of the reflecting plate, wherein the point cloud reflectivity characteristics are point cloud data with reflectivity larger than a set threshold value;
filtering and surface fitting are carried out on the point cloud reflectivity characteristics to obtain the relative position relation between the fitting plane of the reflecting plate and the 40-line laser radar coordinate system;
and performing coordinate conversion through calibration parameters of the 40-line laser radar and the intelligent heavy card coordinate system to obtain the accurate position relation between the tyre crane and the intelligent heavy card.
The method comprises the following steps of obtaining point cloud data of the 40-line laser radar, and extracting point cloud reflectivity characteristics of the surface of the reflecting plate, wherein the method comprises the following steps:
the method comprises the steps of obtaining original laser radar point cloud data of the 40-line laser radar, wherein the original laser radar point cloud data comprise three-dimensional space coordinate positions and reflectivity information;
setting an algorithm application distance space range according to the position relation between the tire crane reflecting plate and the intelligent heavy card, removing irrelevant area data, and extracting a point cloud interesting area;
extracting the reflectivity of the point cloud interesting area within a preset threshold range to obtain reflectivity characteristics;
and according to the point cloud interesting area and the division of the reflectivity characteristics, filtering the original laser radar point cloud data to obtain the point cloud reflectivity characteristics of the surface of the reflecting plate.
Filtering and surface fitting are carried out on the point cloud reflectivity characteristics to obtain the relative position relation between the fitting plane of the reflecting plate and the 40-line laser radar coordinate system, and the method comprises the following steps:
clustering the point cloud reflectivity characteristics of the surface of the reflector plate according to the spatial distance relationship between the points in the original laser radar point cloud data by using an Euclidean distance method to obtain a clustering result;
calculating a peripheral three-dimensional contour of the clustering result, and determining point cloud data contained in the peripheral three-dimensional contour, wherein the length and the width of the peripheral three-dimensional contour correspond to the size of the reflecting plate, and the thickness of the peripheral three-dimensional contour is set in a preset range;
and performing plane fitting on the point cloud data contained in the peripheral three-dimensional contour by using an RANSC algorithm to obtain a relative position relation between a fitting plane of the reflector and the 40-line laser radar coordinate system, and determining that a first plane equation is ax + by + cz + d as 0.
The method comprises the following steps of performing coordinate conversion on calibration parameters of a 40-line laser radar and an intelligent heavy card coordinate system to obtain an accurate position relation between the tyre crane and the intelligent heavy card, wherein the method comprises the following steps:
combining the first plane equation and the calibration parameters of the 40-line laser radar and the intelligent heavy truck coordinate system, and calculating a second plane equation a 'x + b' y + c 'z + d' 0, wherein the second plane equation is used for expressing a plane equation expression result under the reflector and the intelligent heavy truck coordinate system;
and selecting the central point coordinate of the clustering result, substituting the central point coordinate into the second plane equation, and calculating the position d' of the central point coordinate under the intelligent heavy-duty card coordinate system to obtain the accurate position relation between the tire crane and the intelligent heavy-duty card.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams 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.
In a typical configuration, a device includes one or more processors (CPUs), memory, and a bus. The device may also include input/output interfaces, network interfaces, and the like.
The memory may include volatile memory in a computer readable medium, Random Access Memory (RAM) and/or nonvolatile memory such as Read Only Memory (ROM) or flash memory (flash RAM), and the memory includes at least one memory chip. The memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that 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 an … …" does not exclude the presence of other identical elements in the process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application 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 above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art to which the present application pertains. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (10)
1. A tire crane positioning method is characterized by being applied to a tire crane positioning system, wherein the tire crane positioning system comprises: the method comprises the following steps of installing a 40-line laser radar on a smart heavy truck and a reflecting plate fixedly installed on a tire crane, wherein the reflecting plate is structurally and rigidly connected with the tire crane, and the method comprises the following steps:
acquiring point cloud data of the 40-line laser radar, and extracting point cloud reflectivity characteristics of the surface of the reflector plate, wherein the point cloud reflectivity characteristics are point cloud data with the reflectivity larger than a set threshold value, the point cloud data of the laser radar scanned on the reflector plate can effectively cover most of the area of the surface of the reflector plate, and the reflector plate achieves the effect of gain reflection;
filtering and surface fitting are carried out on the point cloud reflectivity characteristics to obtain the relative position relation between the fitting plane of the reflecting plate and the 40-line laser radar coordinate system;
and performing coordinate conversion through calibration parameters of the 40-line laser radar and the intelligent heavy card coordinate system to obtain the accurate position relation between the tyre crane and the intelligent heavy card.
2. The method of claim 1, wherein the obtaining point cloud data of the 40-line lidar and extracting point cloud reflectivity features of the reflector plate surface comprises:
the method comprises the steps of obtaining original laser radar point cloud data of the 40-line laser radar, wherein the original laser radar point cloud data comprise three-dimensional space coordinate positions and reflectivity information;
setting an algorithm application distance space range according to the position relation between the tire crane reflecting plate and the intelligent heavy card, removing irrelevant area data, and extracting a point cloud interesting area;
extracting the reflectivity of the point cloud interesting area within a preset threshold range to obtain reflectivity characteristics;
and according to the point cloud interesting area and the division of the reflectivity characteristics, filtering the original laser radar point cloud data to obtain the point cloud reflectivity characteristics of the surface of the reflecting plate.
3. The method of claim 2, wherein the filtering and surface fitting the point cloud reflectivity features to obtain a relative position relationship between a fitting plane of the reflector and the 40-line lidar coordinate system comprises:
clustering the point cloud reflectivity characteristics of the surface of the reflector plate according to the spatial distance relationship between the points in the original laser radar point cloud data by using an Euclidean distance method to obtain a clustering result;
calculating a peripheral three-dimensional contour of the clustering result, and determining point cloud data contained in the peripheral three-dimensional contour, wherein the length and the width of the peripheral three-dimensional contour correspond to the size of the reflecting plate, and the thickness of the peripheral three-dimensional contour is set in a preset range;
and performing plane fitting on the point cloud data contained in the peripheral three-dimensional contour by using an RANSC algorithm to obtain a relative position relation between a fitting plane of the reflector and the 40-line laser radar coordinate system, and determining that a first plane equation is ax + by + cz + d as 0.
4. The method according to claim 3, wherein the obtaining of the precise position relationship between the tyre crane and the smart heavy card by coordinate transformation of the calibration parameters of the 40-line lidar and the smart heavy card coordinate system comprises:
combining the first plane equation and the calibration parameters of the 40-line laser radar and the intelligent heavy truck coordinate system, and calculating a second plane equation a 'x + b' y + c 'z + d' 0, wherein the second plane equation is used for expressing a plane equation expression result under the reflector and the intelligent heavy truck coordinate system;
and selecting the central point coordinate of the clustering result, substituting the central point coordinate into the second plane equation, and calculating the position d' of the central point coordinate under the intelligent heavy-duty card coordinate system to obtain the accurate position relation between the tire crane and the intelligent heavy-duty card.
5. A tire crane positioning device is applied to a tire crane positioning system, and the tire crane positioning system comprises: the device comprises a 40-line laser radar arranged on a smart heavy truck and a reflecting plate fixedly arranged on a tire crane, wherein the reflecting plate is structurally and rigidly connected with the tire crane, and the device comprises:
the first processing unit is used for acquiring point cloud data of the 40-line laser radar and extracting point cloud reflectivity characteristics of the surface of the reflector plate, wherein the point cloud reflectivity characteristics are point cloud data with reflectivity larger than a set threshold value, the point cloud data of the laser radar scanned on the reflector plate can effectively cover most of the area of the surface of the reflector plate, and the reflector plate achieves the effect of gain reflection;
the second processing unit is used for filtering and surface fitting the point cloud reflectivity characteristics to obtain the relative position relation between the fitting plane of the reflecting plate and the 40-line laser radar coordinate system;
and the third processing unit is used for carrying out coordinate conversion through the calibration parameters of the 40-line laser radar and the intelligent heavy card coordinate system to obtain the accurate position relation between the tyre crane and the intelligent heavy card.
6. The apparatus according to claim 5, wherein the first processing unit is specifically configured to:
acquiring original lidar point cloud data of the 40-line lidar, wherein the original lidar point cloud data comprises three-dimensional space coordinate position and reflectivity information;
setting an algorithm application distance space range according to the position relation between the tire crane reflecting plate and the intelligent heavy truck, eliminating irrelevant area data, and extracting a point cloud interesting area;
extracting the reflectivity of the point cloud interesting area within a preset threshold range to obtain reflectivity characteristics;
and according to the point cloud interesting area and the division of the reflectivity characteristics, filtering the original laser radar point cloud data to obtain the point cloud reflectivity characteristics of the surface of the reflecting plate.
7. The apparatus according to claim 6, wherein the second processing unit is specifically configured to:
clustering the point cloud reflectivity characteristics of the surface of the reflector plate according to the spatial distance relationship between the points in the original laser radar point cloud data by using an Euclidean distance method to obtain a clustering result;
calculating a peripheral three-dimensional contour of the clustering result, and determining point cloud data contained in the peripheral three-dimensional contour, wherein the length and the width of the peripheral three-dimensional contour correspond to the size of the reflecting plate, and the thickness of the peripheral three-dimensional contour is set in a preset range;
and performing plane fitting on the point cloud data contained in the peripheral three-dimensional contour by using an RANSC algorithm to obtain a relative position relation between a fitting plane of the reflector and the 40-line laser radar coordinate system, and determining that a first plane equation is ax + by + cz + d as 0.
8. The apparatus according to claim 7, wherein the third processing unit is specifically configured to:
combining the first plane equation and the calibration parameters of the 40-line laser radar and the intelligent heavy truck coordinate system, and calculating a second plane equation a 'x + b' y + c 'z + d' 0, wherein the second plane equation is used for expressing a plane equation expression result under the reflector and the intelligent heavy truck coordinate system;
and selecting the center point coordinate of the clustering result, substituting the center point coordinate into the second plane equation, and calculating the position d' of the center point coordinate under the intelligent heavy-duty truck coordinate system to obtain the accurate position relation between the tire crane and the intelligent heavy-duty truck.
9. A storage medium comprising a stored program, wherein the program, when executed, controls an apparatus on which the storage medium is located to perform a method of tire crane positioning as claimed in any one of claims 1 to 4.
10. An electronic device comprising at least one processor, and at least one memory, bus connected to the processor; the processor and the memory complete mutual communication through the bus; the processor is configured to invoke program instructions in the memory to perform the tire crane positioning method of any of claims 1-4.
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