CN113686330A - Road trafficability detection method based on map three-dimensional data - Google Patents
Road trafficability detection method based on map three-dimensional data Download PDFInfo
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- CN113686330A CN113686330A CN202110742768.6A CN202110742768A CN113686330A CN 113686330 A CN113686330 A CN 113686330A CN 202110742768 A CN202110742768 A CN 202110742768A CN 113686330 A CN113686330 A CN 113686330A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/20—Instruments for performing navigational calculations
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/93—Lidar systems specially adapted for specific applications for anti-collision purposes
- G01S17/931—Lidar systems specially adapted for specific applications for anti-collision purposes of land vehicles
Abstract
The invention discloses a road trafficability detection method based on map three-dimensional data, belonging to the technical field of road detection, and the detection method comprises the following steps: the method comprises the following steps of firstly, obtaining a two-dimensional map annotation graph based on a map data superposition technology; secondly, planning a vehicle passing route according to the original data of the map label; and thirdly, obtaining vehicle trafficability judgment by combining real-time measurement data based on the two-dimensional map label graph and the planned vehicle traffic route. The technology marks three-dimensional data of road height, width and turning radius on the basis of a two-dimensional map, and plans a passable driving route according to basic information of vehicles; the technology can be applied to route planning and real-time navigation systems of medium and large-sized vehicles, and can perform early warning, judgment and passing decision of passable road areas on vehicle planned routes and real-time driving routes in real time.
Description
The technical field is as follows:
the invention relates to a road trafficability detection method based on map three-dimensional data, and belongs to the technical field of road detection.
Background art:
with the increase of transportation markets of the oversize equipment year by year, however, due to the appearance and turning radius of special vehicles, certain requirements are imposed on a driving road, and therefore a method capable of planning a driving route for a driver and prompting that the road can pass through in real time is urgently needed. The trafficability detection method is characterized in that the trafficability detection method marks the width, the clearance height and the turning radius of a road on the basis of an original two-dimensional map, when a traffic route is planned, a trafficable route is planned in advance according to vehicle attributes, and in the traffic process, obstacles in the road are detected in real time through sensors such as a radar and the like, and a driver is prompted whether the obstacles can pass or not.
At present, a driving route can only be planned according to a destination and a departure place by using common map navigation, and when a vehicle passes through, the situation that the vehicle cannot pass normally due to the fact that a certain road section in the driving route is limited to be high or the turning radius is insufficient can be found, so that the vehicle cannot accurately reach the destination on time. Therefore, the three-dimensional data of the height, the width and the turning radius of the road are marked on the two-dimensional map by a road trafficability detection method based on the three-dimensional data of the map, a driving route through which the vehicle can correctly pass is planned in advance according to basic information of the vehicle when a navigation route is planned, and in the real-time traffic process of the vehicle, obstacles in the road are detected in real time according to multiple sensors to prompt a driver whether the vehicle can correctly pass.
Disclosure of Invention
In order to overcome the problems, the invention provides a road trafficability detection method based on map three-dimensional data, which realizes that three-dimensional data of road height, width and turning radius are marked on the basis of a two-dimensional map, and a trafficable driving route is planned according to basic information of a vehicle; and in the driving process, obstacles in the road are judged through the multiple sensors, and whether the driver can pass correctly is prompted.
The technical scheme for solving the technical problem is as follows:
a road trafficability detection method based on map three-dimensional data comprises the following steps:
the method comprises the following steps of firstly, obtaining a two-dimensional map annotation graph based on a map data superposition technology;
secondly, planning a vehicle passing route according to the original data of the map label;
and thirdly, obtaining vehicle trafficability judgment by combining real-time measurement data based on the two-dimensional map label graph and the planned vehicle traffic route.
Further, the first step specifically comprises: the method comprises the following steps that an acquisition vehicle acquires vehicle information through a laser radar and an inertial navigation sensor, acquires road detailed data according to synchronous time, and performs noise reduction processing on the data to generate an LAS file; calculating road attribute information through the point cloud data; on the basis of the existing two-dimensional map, the collected road attribute information is marked on the road in the map according to positioning.
Further, the second step is specifically: and (3) bringing the special vehicle mathematical model into a three-dimensional data model in a two-dimensional map, calculating and judging roads through which vehicles can pass, and planning a vehicle passing route according to an optimal path algorithm.
Further, the third step is specifically: a local road scene model is established through a road detection algorithm, a target detection algorithm and a scene construction algorithm, a mathematical model of a special vehicle model is brought into the local road scene model, the current road state of the vehicle is judged by combining laser radar real-time point cloud information and inertial navigation attitude information, whether obstacles in the road block the special vehicle to pass is judged, and a driver is prompted to drive cautiously.
Further, the step one specifically includes the following:
step 1.1, adopting a multi-sensor fusion technology, and acquiring laser radar point cloud information, inertial navigation equipment vehicle attitude information, odometer vehicle wheel speed and distance information and chassis vehicle state information according to synchronous time;
step 1.2, filtering noise points by a point cloud filtering method, adopting bilateral filtering, and analyzing and describing point cloud characteristics by characteristic values to generate an LAS file;
and step 1.3, processing the point cloud data to obtain road attribute information, judging whether obstacles exist or not by combining the point cloud data and the visual data of the camera, and marking all information on a two-dimensional map.
Further, the third step specifically includes the following steps:
step 3.1, width judgment:
measuring the transverse width of the environment model to ensure that the width of a road at each position in an environment range is greater than the width of a rear vehicle body;
step 3.2, height judgment:
when the vehicle passes through the tunnel and the culvert, the height is judged by combining the height and the width of the vehicle body of the fleet, the height of the vehicle body of the fleet is assumed to be H, the length L of the tunnel at the position of the highest vehicle is calculated, and if the length L is greater than the width of the vehicle body, the rear vehicle can pass through the tunnel;
step 3.3, judging the trafficability of the turning radius:
the method comprises the steps of comparing the curvature radius of a curved road with the turning radius of an automobile, utilizing road environment point data output by a laser radar in real time, utilizing an algorithm curve fitting mode to calculate an expression of the inner edge and the outer edge of the road in real time, further calculating the curvature and the road width of the inner edge and the outer edge of the road, regarding the curved road as an arc line segment, calculating the curvature radius of the curved road, comparing the curve line segment with the minimum turning radius of the automobile, and regarding the curved road as a passing curve when the curvature radius of the curved road is larger than the turning radius of a rear automobile.
Furthermore, in step 3.1, the following two methods are adopted for judging the passing property of the width:
in a global coordinate system, setting the x + direction as the width direction of a vehicle, the y + direction as the advancing direction of the vehicle and the z + direction as the vertical upward direction of the vehicle, directly solving the difference value of the x coordinates of the two under the condition that the y coordinates are equal, and determining that the minimum difference value is passed when the minimum difference value is larger than the vehicle body passing range;
in the road modeling part, curve fitting is carried out on the road side, namely polynomial representation of the road side is carried out, difference can be directly carried out on the two polynomials, the minimum value is found out through derivation, and then whether the road can pass or not is judged.
As a preferred embodiment of the present application, the road attribute includes width, height, curvature information of the road.
As a preferred embodiment of the application, the special vehicle mathematical model comprises the height, the width and the turning radius of the vehicle.
Has the advantages that:
through the map data superposition technology, the route planning technology and the passing detection method, the system can realize passing route planning of the special vehicle, mark the barriers in the driving route in real time and judge whether the vehicle can pass.
Description of the drawings:
in order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the present invention will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts;
FIG. 1 is a flow chart of the three-dimensional data annotation of a two-dimensional map in the present application;
FIG. 2 is a flow chart of map publishing;
FIG. 3 is a flow chart of a passable judgment process in the application;
FIG. 4 is a flow chart of the height and width passable judgment;
FIG. 5 is a flow chart of turning radius passable judgment;
FIG. 6 is a mathematical model diagram of a special type vehicle.
The specific implementation scheme is as follows:
in order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, 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 invention.
Example 1
The embodiment provides a road trafficability detection method based on map three-dimensional data, which comprises the following steps:
the method comprises the following steps of firstly, obtaining a two-dimensional map annotation graph based on a map data superposition technology;
secondly, planning a vehicle passing route according to the original data of the map label;
and thirdly, obtaining vehicle trafficability judgment by combining real-time measurement data based on the two-dimensional map label graph and the planned vehicle traffic route.
Further, the first step specifically comprises: the method comprises the following steps that an acquisition vehicle acquires vehicle information through a laser radar and an inertial navigation sensor, acquires road detailed data according to synchronous time, and performs noise reduction processing on the data to generate an LAS file; calculating road attribute information through the point cloud data; on the basis of the existing two-dimensional map, the collected road attribute information is marked on the road in the map according to positioning.
Further, the second step is specifically: and (3) bringing the special vehicle mathematical model into a three-dimensional data model in a two-dimensional map, calculating and judging roads through which vehicles can pass, and planning a vehicle passing route according to an optimal path algorithm.
Further, the third step is specifically: a local road scene model is established through a road detection algorithm, a target detection algorithm and a scene construction algorithm, a mathematical model of a special vehicle model is brought into the local road scene model, the current road state of the vehicle is judged by combining laser radar real-time point cloud information and inertial navigation attitude information, whether obstacles in the road block the special vehicle to pass is judged, and a driver is prompted to drive cautiously.
Further, the step one specifically includes the following:
step 1.1, adopting a multi-sensor fusion technology, and acquiring laser radar point cloud information, inertial navigation equipment vehicle attitude information, odometer vehicle wheel speed and distance information and chassis vehicle state information according to synchronous time;
step 1.2, filtering noise points by a point cloud filtering method, adopting bilateral filtering, and analyzing and describing point cloud characteristics by characteristic values to generate an LAS file;
and step 1.3, processing the point cloud data to obtain road attribute information, judging whether obstacles exist or not by combining the point cloud data and the visual data of the camera, and marking all information on a two-dimensional map.
FIG. 1 is a flow chart of three-dimensional data annotation of a two-dimensional map
In order to extract the technology of marking the road three-dimensional data into the two-dimensional map, the main measures adopted comprise:
a) adopting a multi-sensor fusion technology to acquire according to synchronous time;
b) and (3) data processing, namely filtering the point cloud, filtering noise points, adopting bilateral filtering, analyzing and describing the point cloud characteristics through characteristic values, and generating an LAS file.
c) Processing the point cloud data to obtain road attribute information such as width, height, curvature and the like; judging whether an obstacle exists or not by combining the point cloud data through the visual data of the camera; and marking all information on the two-dimensional map.
FIG. 2 map publishing
And converting the marked data to generate framing two-dimensional road network data, fusing and updating the road network data in batches with the same map amplitude, editing road shape points and nodes, modifying a fusion result, automatically optimizing the map amplitude of the road network data by connecting edges, and finally releasing a map.
Fig. 3 is a flow chart of passable judgment, and the main measures taken for the technique of extracting the passable attacking and conquering area include:
a) by adopting a characteristic point matching method, the rasterization fineness of the point cloud map is improved, and the problem of automatic matching of structured and unstructured roads is solved, wherein the characteristic point matching method specifically comprises the following steps:
the extension of the passable area between the ground grid and the unknown state grid is only suitable for grids in the same angle direction;
A. traversing grids in the same sector block along the radial direction by taking an original point as a starting point, if the current grid is a ground grid and the previous ground grid is the ground grid, expanding an unknown state grid between the grids into the ground grid, if the current grid is an obstacle grid, not expanding the unknown state grid between the grids, and continuously searching other grids of the sector block until the farthest grid of the sector block;
B. a vehicle neighborhood grid, namely a grid with a distance of the origin of the polar coordinates is expanded into a ground grid;
C. and marking the ground grid in the grid map as a passable state.
b) By adopting multi-frame synchronous processing, the data redundancy is effectively improved, and the description capability of characteristic objects such as obstacles is improved.
As a preferred embodiment of the present application, the road attribute includes width, height, curvature information of the road.
As a preferred embodiment of the application, the special vehicle mathematical model comprises the height, the width and the turning radius of the vehicle.
Further, the third step specifically includes the following steps:
step 3.1, width judgment:
the method is characterized in that the transverse width of the environment model is measured, the road width of each position in the environment range is larger than the width of a rear vehicle body, and the following two modes are adopted for judging the width trafficability characteristic:
in a global coordinate system, setting the x + direction as the width direction of a vehicle, the y + direction as the advancing direction of the vehicle and the z + direction as the vertical upward direction of the vehicle, directly solving the difference value of the x coordinates of the two under the condition that the y coordinates are equal, and determining that the minimum difference value is passed when the minimum difference value is larger than the vehicle body passing range;
in the road modeling part, curve fitting is carried out on the road side, namely polynomial representation of the road side is carried out, difference can be directly carried out on the two polynomials, the minimum value is found out through derivation, and then whether the road can pass or not is judged.
Step 3.2, height judgment:
when the vehicle passes through the tunnel and the culvert, the height is judged by combining the height and the width of the vehicle body of the fleet, the height of the vehicle body of the fleet is assumed to be H, the length L of the tunnel at the position of the highest vehicle is calculated, and if the length L is greater than the width of the vehicle body, the rear vehicle can pass through the tunnel;
step 3.3, judging the trafficability of the turning radius:
the method comprises the steps of comparing the curvature radius of a curved road with the turning radius of an automobile, utilizing road environment point data output by a laser radar in real time, utilizing an algorithm curve fitting mode to calculate an expression of the inner edge and the outer edge of the road in real time, further calculating the curvature and the road width of the inner edge and the outer edge of the road, regarding the curved road as an arc line segment, calculating the curvature radius of the curved road, comparing the curve line segment with the minimum turning radius of the automobile, and regarding the curved road as a passing curve when the curvature radius of the curved road is larger than the turning radius of a rear automobile.
FIG. 4 is a flow chart of the height and width passable judgment
By establishing the road environment model, a plurality of indexes required by the following vehicles when passing through the road can be calculated, and the road passability is determined. Mainly comprises the following aspects.
a) Width determination
The method mainly measures the transverse width of an environment model, and ensures that the width of a road (the vertical width of a Z axis on a space which is more than or equal to 0) at each position in an environment range is greater than the width of a rear vehicle body. Two methods are provided herein:
in a global coordinate system, under the condition that y coordinates are equal, the difference value of the x coordinates and the y coordinates is directly solved, and when the minimum difference value is larger than the vehicle body passable range, the vehicle body passable range is regarded as passable;
in the road modeling part, curve fitting is carried out on the road side, namely polynomial representation of the road side is carried out, difference can be directly carried out on the two polynomials, the minimum value is found out through derivation, and then whether the road can pass or not is judged.
b) Height determination
The judgment is mainly aimed at the road types such as tunnels, culverts and the like.
As shown in fig. 6, it is a schematic diagram of a mathematical model of a vehicle, and when passing through a tunnel or a culvert, the height needs to be determined in combination with the height and width of the rear vehicle body. Assuming that the height of the rear vehicle is H, the length of the AB can be directly calculated for the terrain such as tunnels, culverts and the like in general, and the rear vehicle can pass through the rear vehicle as long as the length of the AB is greater than the width of the vehicle body.
The calculation of the AB length is similar to the ground width calculation method: when the ground width is calculated, the z coordinate of the roadside in the global coordinate system is 0; and when the tunnel passing range is calculated, the z coordinate of the tunnel bending edge in the global coordinate system is H.
Fig. 5 is a flow chart of the turn radius passable judgment, and since the vehicle body length is long and the required turn radius is large, the road camber needs to be calculated.
When turning, the curvature radius of the curved road needs to be compared with the turning radius of the automobile. The method comprises the steps of extracting candidate points of the inner edge and the outer edge of a road by utilizing single-frame road environment point data output by a laser radar in real time, extracting expressions of the inner edge and the outer edge of the road in real time by utilizing an algorithm curve fitting mode, further calculating the curvature and the road width of the inner edge and the outer edge of the road, regarding the curved road as an arc line segment, calculating the curvature radius of the curved road, comparing the curvature radius with the minimum turning radius of an automobile, and regarding the curved road as a passing road when the curvature radius of the curved road is larger than the turning radius of a rear automobile.
It is noted that, herein, relational terms such as first and second, and the like may be 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. Furthermore, 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 elements inherent in the list. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element. In addition, parts of the above technical solutions provided in the embodiments of the present application, which are consistent with the implementation principles of corresponding technical solutions in the prior art, are not described in detail so as to avoid redundant description.
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 (9)
1. A road trafficability detection method based on map three-dimensional data is characterized by comprising the following steps:
the method comprises the following steps of firstly, obtaining a two-dimensional map annotation graph based on a map data superposition technology;
secondly, planning a vehicle passing route according to the original data of the map label;
and thirdly, obtaining vehicle trafficability judgment by combining real-time measurement data based on the two-dimensional map label graph and the planned vehicle traffic route.
2. The method for detecting road trafficability based on three-dimensional map data according to claim 1, wherein the first step is specifically: the method comprises the following steps that an acquisition vehicle acquires vehicle information through a laser radar and an inertial navigation sensor, acquires road detailed data according to synchronous time, and performs noise reduction processing on the data to generate an LAS file; calculating road attribute information through the point cloud data; on the basis of the existing two-dimensional map, the collected road attribute information is marked on the road in the map according to positioning.
3. The method for detecting road trafficability based on three-dimensional map data according to claim 1, wherein the second step is specifically: and (3) bringing the special vehicle mathematical model into a three-dimensional data model in a two-dimensional map, calculating and judging roads through which vehicles can pass, and planning a vehicle passing route according to an optimal path algorithm.
4. The method for detecting road trafficability based on three-dimensional map data according to claim 1, wherein the third step is specifically: a local road scene model is established through a road detection algorithm, a target detection algorithm and a scene construction algorithm, a mathematical model of a special vehicle model is brought into the local road scene model, the current road state of the vehicle is judged by combining laser radar real-time point cloud information and inertial navigation attitude information, whether obstacles in the road block the special vehicle to pass is judged, and a driver is prompted to drive cautiously.
5. The method according to claim 2, wherein the step one includes the following steps:
step 1.1, adopting a multi-sensor fusion technology, and acquiring laser radar point cloud information, inertial navigation equipment vehicle attitude information, odometer vehicle wheel speed and distance information and chassis vehicle state information according to synchronous time;
step 1.2, filtering noise points by a point cloud filtering method, adopting bilateral filtering, and analyzing and describing point cloud characteristics by characteristic values to generate an LAS file;
and step 1.3, processing the point cloud data to obtain road attribute information, judging whether obstacles exist or not by combining the point cloud data and the visual data of the camera, and marking all information on a two-dimensional map.
6. The method for detecting road trafficability based on three-dimensional map data according to claim 3, wherein step three specifically includes the following steps:
step 3.1, width judgment:
measuring the transverse width of the environment model to ensure that the width of a road at each position in an environment range is greater than the width of a rear vehicle body;
step 3.2, height judgment:
when the vehicle passes through the tunnel and the culvert, the height is judged by combining the height and the width of the vehicle body of the fleet, the height of the vehicle body of the fleet is assumed to be H, the length L of the tunnel at the position of the highest vehicle is calculated, and if the length L is greater than the width of the vehicle body, the rear vehicle can pass through the tunnel;
step 3.3, judging the trafficability of the turning radius:
the method comprises the steps of comparing the curvature radius of a curved road with the turning radius of an automobile, utilizing road environment point data output by a laser radar in real time, utilizing an algorithm curve fitting mode to calculate an expression of the inner edge and the outer edge of the road in real time, further calculating the curvature and the road width of the inner edge and the outer edge of the road, regarding the curved road as an arc line segment, calculating the curvature radius of the curved road, comparing the curve line segment with the minimum turning radius of the automobile, and regarding the curved road as a passing curve when the curvature radius of the curved road is larger than the turning radius of a rear automobile.
7. The method for detecting road trafficability based on three-dimensional map data of claim 5, wherein in step 3.1, the following two methods are used for determining the road trafficability:
in a global coordinate system, setting the x + direction as the width direction of a vehicle, the y + direction as the advancing direction of the vehicle and the z + direction as the vertical upward direction of the vehicle, directly solving the difference value of the x coordinates of the two under the condition that the y coordinates are equal, and determining that the minimum difference value is passed when the minimum difference value is larger than the vehicle body passing range;
in the road modeling part, curve fitting is carried out on the road side, namely polynomial expression of the road side is carried out, the difference is carried out on the two polynomials, the minimum value is found out through derivation, and then whether the road can pass or not is judged.
8. The method for detecting road trafficability based on three-dimensional map data according to any one of claims 1 to 7, wherein the road attribute includes width, height and curvature information of a road.
9. The map three-dimensional data-based road trafficability detection method according to any one of claims 1 to 7, wherein the special vehicle mathematical model includes a height, a width and a turning radius of the vehicle.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114463989A (en) * | 2021-12-13 | 2022-05-10 | 浙江明航智能科技有限公司 | Height limit detection system for civil aviation special vehicle |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114463989A (en) * | 2021-12-13 | 2022-05-10 | 浙江明航智能科技有限公司 | Height limit detection system for civil aviation special vehicle |
| CN114463989B (en) * | 2021-12-13 | 2023-09-08 | 浙江明航智能科技有限公司 | Civil aviation special vehicle limit for height detecting system |
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Application publication date: 20211123 |