CN113962849A - High-precision map precision detection method based on projection transformation - Google Patents
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Abstract
The invention relates to a high-precision map precision detection method based on projection transformation, which comprises the following steps: step 1: converting the format of the high-precision map constructed based on the UTM projection method and then carrying out visualization processing; step 2: the method comprises the steps that centimeter-level high-precision positioning information of a vehicle, namely real-time longitude and latitude information of the vehicle, is collected through detection equipment carried by the vehicle; and step 3: projecting the real-time longitude and latitude information of the vehicle to a plane rectangular coordinate system through a UTM (Universal time management) and converting the real-time longitude and latitude information into local coordinate information; and 4, step 4: acquiring a real-time track of the current vehicle running on a plane rectangular coordinate system based on the local coordinate information; and 5: compared with the prior art, the method has the advantages that the precision of the high-precision map can be clearly and intuitively detected, the precision of vehicle positioning can be improved, and the like.
Description
Technical Field
The invention relates to the field of high-precision map precision detection, in particular to a high-precision map precision detection method based on projection transformation.
Background
The method is a common high-precision map drawing method at present, wherein a data standard vehicle is constructed to collect point cloud and image data, and then road environment construction is reconstructed through data processing, certain errors are generated in the process of drawing a high-precision map due to factors such as shelters and road surface abrasion, so that the accuracy of the high-precision map needs to be further verified.
The other method is that the longitude and latitude or the local coordinate under a certain local coordinate system is obtained by selecting a certain characteristic point on the high-precision map, usually the building corner point or the lane indicating corner point of the area, and compared with the longitude and latitude obtained by the combined surveying and mapping of a total station and a handheld RTK in a real scene or the local coordinate under the local coordinate system which is the same as the high-precision map, the method is characterized in that a certain characteristic point on a high-precision map is selected and is usually observed by naked eyes, the characteristic point is difficult to be completely matched with the characteristic point in a real scene, namely, a group of selected contrast coordinates cannot be guaranteed to be the same point, an error exists in the selection process, the high-precision map comprises thousands of coordinate points, manual mapping and comparison cannot be achieved, and the high-precision map cannot be completely detected by selecting a plurality of characteristic points for comparison.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a high-precision map precision detection method based on projection transformation.
The purpose of the invention can be realized by the following technical scheme:
a high-precision map precision detection method based on projection transformation comprises the following steps:
step 1: converting the format of the high-precision map constructed based on the UTM projection method and then carrying out visualization processing;
step 2: the method comprises the steps that centimeter-level high-precision positioning information of a vehicle, namely real-time longitude and latitude information of the vehicle, is collected through detection equipment carried by the vehicle;
and step 3: projecting the real-time longitude and latitude information of the vehicle to a plane rectangular coordinate system through a UTM (Universal time management) and converting the real-time longitude and latitude information into local coordinate information;
and 4, step 4: acquiring a real-time track of the current vehicle running on a plane rectangular coordinate system based on the local coordinate information;
and 5: and (3) matching the visualized real-time track of the vehicle with a high-precision map to detect the precision of the high-precision map.
In the step 1, the visualization process specifically includes:
in an industrial personal computer of a vehicle, a high-precision map is exported in an xodr format, and then is converted into an osm format based on an open source conversion tool and then imported into visualization software Rviz in an ROS operating system.
In the step 2, the detection device carried by the vehicle is high-precision integrated navigation.
In the step 3, the real-time longitude and latitude of the vehicle are converted into coordinates under a plane rectangular coordinate system based on a UTM projection method, and real-time longitude and latitude information sent out by the high-precision integrated navigation is repeatedly recalled based on an ROS communication architecture so as to convert the real-time longitude and latitude information into local coordinate information.
The plane rectangular coordinate system is a local coordinate system, and the origin of the local coordinate system is a point within the range of the high-precision map to be detected or within a hundred meters outside the range of the high-precision map.
The UTM projection method is specifically a projection method for cutting an earth ellipsoid by an elliptic cylinder transverse and positive axis, the central line of the elliptic cylinder is positioned on the equatorial plane of the ellipsoid and points on the ellipsoid are projected onto the elliptic cylinder through ellipsoid body points, two secant circles generated by cutting the earth ellipsoid by the elliptic cylinder transverse and positive axis are projected by the UTM and have unchanged lengths, namely two standard meridian circles, the middle of the two secant circles is a central meridian circle, and the scale factor of the central meridian is 0.9996, so that the left and right parts of the central meridian are ensured to have the standard meridian circles without distortion.
The calculation formula of the scale factor is as follows:
where k is a scale factor that varies with the distance of the warp coil from the central meridian.
The UTM projection divides the earth surface area between 84 degrees of north latitude and 80 degrees of south latitude into north and south longitudinal bands, namely projection bands, the projection bands are divided into different projection index bands according to different numbers of dividing bands, the projection index bands comprise 3 index bands and 6 index bands, and the projection index bands are consistent with the projection index bands adopted by the constructed high-precision map.
In the step 4, the local coordinate information forms an array through continuous superposition, and then the real-time track of the vehicle running on the plane rectangular coordinate system is obtained.
In the step 5, the process of detecting the accuracy of the high-accuracy map specifically includes the following steps:
step 501: the vehicle keeps normally running in the middle of the lane in all road sections of a real scene depicted by a high-precision map to be detected;
step 502: observing the matching condition of the real-time track of the vehicle on the high-precision map through a visual interface;
step 503: and judging whether the real-time track of the vehicle is always at the middle position of the corresponding lane on the high-precision map and is matched with the driving track, if the real-time track of the vehicle is always at the middle position of the corresponding lane on the high-precision map and is matched with the driving track, the precision of the high-precision map reaches the lane level, otherwise, the precision of the high-precision map does not reach the lane level.
Compared with the prior art, the invention has the following advantages:
the method comprises the following steps that firstly, high-precision map precision detection based on UTM projection transformation can clearly and visually reflect the precision of a high-precision map;
and secondly, the precision of the high-precision map can be ensured to improve the precision of vehicle positioning, and the prior global information is provided for the intelligent networked vehicles.
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FIG. 1 is a flow chart of the present invention.
Fig. 2 is an Rviz visualization interface diagram of the high-precision map and vehicle real-time track matching condition 1.
Fig. 3 is an Rviz visualization interface diagram in the matching case 2 of the high-precision map and the real-time trajectory of the vehicle, wherein the diagram (3a) and the diagram (3b) are the Rviz visualization interface diagrams with different vehicle positions in the matching case 2 respectively.
Detailed Description
The invention is described in detail below with reference to the figures and specific embodiments. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all 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.
Examples
The invention provides a high-precision map precision detection method based on projection transformation, which is characterized in that a vehicle is provided with detection equipment such as combined inertial navigation equipment, the vehicle runs in a high-precision map building area, real-time longitude and latitude information of a current vehicle is acquired, because the longitude and latitude information cannot visually reflect distance information under a rectangular coordinate system, the same projection transformation method as that used for building a high-precision map is needed to be adopted to control variables, the projection transformation method comprises a UTM projection (universal transverse axis mercator projection method), a TM projection (transverse axis mercator projection method) and a Gaussian projection, and the high-precision map precision detection method provided by the method adopts the UTM projection method to build the high-precision map and detects the precision of the high-precision map based on the UTM projection method.
UTM coordinate projection is the ellipsoid that the ellipse circular column horizontal axis was cut, the central line of ellipse circular column is located ellipsoid equatorial plane, and pass through ellipsoid body particle, thereby with the point projection on the ellipsoid to the ellipse circular column on, two secant circles are unchangeable in length on UTM projection drawing, 2 standard meridian circles promptly, be central meridian circle in the middle of two secant circles, the length behind the central meridian projection is 0.9996 times before its projection, the scale factor k of central meridian is 0.9996 promptly, the expression of scale factor k is:
the UTM projection divides the earth surface area between 84 degrees of north latitude and 80 degrees of south latitude into north and south longitudinal belts, namely projection belts, the projection belts are divided into different projection division belts according to different numbers of division belts, the projection division belts comprise 3 division belts and 6 division belts, the projection division belts are consistent with the projection division belts adopted when a high-precision map is constructed when the projection division belts are selected, each longitudinal belt (projection belt) of the UTM system is projected onto a plane through a universal transverse shaft mercator, the covering distance in the south and north directions is long, and the small distortion amplitude is ensured.
As shown in fig. 1, the detection process of the high-precision map precision detection method based on UTM projection includes the following steps:
step 1: in an industrial personal computer of a vehicle, firstly, deriving a high-precision map constructed based on Roadrunner or other tools in an Opendrive format (xodr format), then converting the high-precision map in the xodr format into an osm format based on an open source conversion tool, and importing visualization software Rviz in an ROS operating system;
step 2: the method comprises the steps that centimeter-level high-precision positioning information of a vehicle, namely longitude and latitude information of the vehicle, is acquired and acquired through high-precision combined navigation carried by the vehicle;
and step 3: converting the real-time longitude and latitude of the vehicle to a coordinate under a plane rectangular coordinate system based on a UTM projection method, and repeatedly calling back real-time longitude and latitude information sent out by high-precision integrated navigation based on an ROS communication architecture so as to convert the real-time longitude and latitude information to local coordinate information in real time;
and 4, step 4: acquiring a real-time track of the current vehicle running under a plane rectangular coordinate system according to an array formed by continuously superposing the converted local coordinate information;
and 5: and (3) publishing a topic in a message type nav _ msgs/Path of the ROS, visualizing a vehicle track by subscribing the topic in the Rviz and matching the visualized vehicle track with an imported osm-format high-precision map so as to detect the precision of the high-precision map.
In step 1, the high-precision map for detecting the precision is generated based on Roadrunner, and the high-precision map can be generated by other tools as long as the high-precision map is guaranteed to be in an xodr form.
In step 2, the plane rectangular coordinate system is a local coordinate system, the high-precision map range to be detected or a point outside the high-precision map range is selected as the origin of the local coordinate system, and when the point outside the high-precision map range to be detected is selected, the point is within one hundred meters of the high-precision map range to be detected.
The detection process of the precision of the high-precision map specifically comprises the following steps:
step 501: the vehicle keeps normally running in the middle of the lane in all road sections of a real scene depicted by a high-precision map to be detected;
step 502: observing the matching condition of the real-time track of the vehicle on the high-precision map through a visual interface;
step 503: and judging whether the real-time track of the vehicle is always at the middle position of the corresponding lane on the high-precision map and is matched with the driving track, if the real-time track of the vehicle is always at the middle position of the corresponding lane on the high-precision map and is matched with the driving track, the precision of the high-precision map reaches the lane level, otherwise, the precision of the high-precision map does not reach the lane level.
As shown in fig. 3a, a gray bold line is an osm format high-precision map into which Rviz is introduced, an upper gray thin line is a real-time track of the vehicle, in the experiment, the vehicle always runs along a normal straight line of a center line of a lane on the right side of the map, the real-time track is a true value obtained based on high-precision combined navigation, and as can be seen from the figure, the real-time track is not in the lane of the high-precision map, so that the detected high-precision map obviously does not meet the precision requirement, and an error exists in rotation translation transformation, and therefore prior global information cannot be provided for the intelligent internet vehicle.
As shown in fig. 3b, the gray renting line is an osm format high-precision map into which Rviz is introduced, the oblique line block diagram and the gray thin line are respectively real-time tracks of the vehicle and the vehicle, in the experiment, the vehicle always runs along the center line of the right lane of the map normally and straightly, the real-time track obtained based on the high-precision combined navigation is regarded as a true value, as can be seen from the figure, the real-time track is always in the right lane of the high-precision map, the detected high-precision map meets the precision requirement, the degree of consistency with the driving behavior track of the driver is high, the precision reaches the lane level, and the prior global information can be provided for the intelligent internet vehicle.
In conclusion, the high-precision map precision detection method based on projection transformation provided by the invention can qualitatively analyze whether the high-precision map reaches the lane-level precision or not.
While the invention has been described with reference to specific embodiments, the invention is not limited thereto, and those skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope of the invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A high-precision map precision detection method based on projection transformation is characterized by comprising the following steps:
step 1: converting the format of the high-precision map constructed based on the UTM projection method and then carrying out visualization processing;
step 2: the method comprises the steps that centimeter-level high-precision positioning information of a vehicle, namely real-time longitude and latitude information of the vehicle, is collected through detection equipment carried by the vehicle;
and step 3: projecting the real-time longitude and latitude information of the vehicle to a plane rectangular coordinate system through a UTM (Universal time management) and converting the real-time longitude and latitude information into local coordinate information;
and 4, step 4: acquiring a real-time track of the current vehicle running on a plane rectangular coordinate system based on the local coordinate information;
and 5: and (4) matching the visualized real-time track of the vehicle with a high-precision map to detect the precision of the high-precision map.
2. The method for detecting the precision of the high-precision map based on the projective transformation as claimed in claim 1, wherein in the step 1, the visualization process specifically comprises:
in an industrial personal computer of a vehicle, a high-precision map is exported in an xodr format, converted into an osm format based on an open source conversion tool and then imported into visualization software Rviz in an ROS operating system.
3. The method for detecting the accuracy of a high-precision map based on projective transformation as claimed in claim 1, wherein in step 2, the detection device mounted on the vehicle is a high-precision integrated navigation.
4. The method as claimed in claim 1, wherein in step 3, the real-time longitude and latitude of the vehicle are converted into coordinates in a rectangular plane coordinate system based on a UTM projection method, and the real-time longitude and latitude information sent by the high-precision integrated navigation is repeatedly recalled based on an ROS communication architecture, so as to convert the real-time longitude and latitude information into local coordinate information.
5. The method as claimed in claim 4, wherein the planar rectangular coordinate system is a local coordinate system, and the origin of the local coordinate system is a point within the high-precision map range to be detected or within a hundred meters outside the high-precision map range.
6. The method as claimed in claim 4, wherein the UTM projection method is a projection method of an ellipsoid transverse and orthogonal axis cutting earth ellipsoid, the central line of the ellipsoid is located on the equatorial plane of the ellipsoid and passes through the ellipsoid particles, thereby realizing the projection of the point on the ellipsoid onto the ellipsoid, the two secant circles generated by the ellipsoid transverse and orthogonal axis cutting earth ellipsoid are unchanged in length after the UTM projection, namely two standard meridian circles, the middle of the two secant circles is a central meridian circle, and the scale factor of the central meridian is 0.9996, so as to ensure that the standard meridian circles are undistorted at the left and right positions of the central meridian.
7. The method according to claim 6, wherein the scaling factor is calculated by:
where k is a scale factor that varies with the distance of the warp coil from the central meridian.
8. The method as claimed in claim 7, wherein the UTM projection divides the earth surface area between 84 degrees north latitude and 80 degrees south latitude into longitudinal south-north bands, i.e. projection bands, the projection bands are divided into different projection graduation bands according to the number of division bands, and the projection graduation bands comprise 3 graduation bands and 6 graduation bands, which are consistent with the projection graduation bands adopted by the constructed high-precision map.
9. The method as claimed in claim 1, wherein in step 4, the local coordinate information is continuously superimposed to form an array, so as to obtain a real-time track of the vehicle traveling on the planar rectangular coordinate system.
10. The method for detecting the precision of the high-precision map based on the projective transformation as claimed in claim 1, wherein in the step 5, the process of detecting the precision of the high-precision map specifically includes the following steps:
step 501: the vehicle keeps normally running in the middle of a lane in all road sections of a real scene depicted by a high-precision map to be detected;
step 502: observing the matching condition of the real-time track of the vehicle on the high-precision map through a visual interface;
step 503: and judging whether the real-time track of the vehicle is always at the middle position of the corresponding lane on the high-precision map and is matched with the driving track, if the real-time track of the vehicle is always at the middle position of the corresponding lane on the high-precision map and is matched with the driving track, the precision of the high-precision map reaches the lane level, otherwise, the precision of the high-precision map does not reach the lane level.
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