Object sampling method, device and equipment in multi-pass data

Abstract

The invention discloses an object sampling method, device and equipment in multi-pass data, and the method comprises the steps: carrying out the pairwise combination of a lane object with a matching relation and a semantic object with a matching relation in segment data based on a plurality of pieces of segment data in group data divided by the multi-pass data, respectively constructing a lane object set and a semantic object set of the group data; and based on the lane object set and the semantic object set, separately sampling the lane objects and the semantic objects, so as to determine an object alignment error based on a sampling result. According to the method, the lane object and the semantic object are sampled to determine the longitudinal error and the transverse error among multiple times of data, so that the absolute error among the multiple times of data caused by sampling precision and multiple times of acquisition can be effectively corrected, and a foundation is laid for subsequently making a high-definition map based on the multiple times of data.

Description

Technical Field

[0001] The present invention relates to the field of geographic information technology, mainly to the field of high-precision map technology based on multi-source crowdsourcing data, and in particular to a method, device and equipment for sampling objects in multi-pass data. Background Art

[0002] High-precision maps can be used for autonomous driving, and the recognition and production of road signs are important components of generating high-precision maps. Road signs (semantic objects) are generally identified and extracted through road image information collected by cameras mounted on vehicles.

[0003] In the process of making high-precision maps, in order to solve the drawback of large resource investment in single-trip closed-loop collection (closed-loop single-trip collection) and optimize application scenarios, the inventor previously proposed a solution for making high-precision maps based on multi-source crowdsourcing data, that is, by using private cars as collection equipment, data collection is performed without interfering with the stable and fast driving of the vehicle. In the process of using private cars to collect data, since it is impossible to stipulate that a certain vehicle strictly follows a closed-loop trajectory, most vehicle trajectories are only one-way trajectories from the starting point to the destination, so multiple single-track data is used in the later production of high-precision maps, that is, multiple / multiple image collections are required for the same location, and then the road sign information in each collected image is extracted separately, and finally the same road sign collected in multiple trips is fused, and then a high-precision map is produced based on multi-source crowdsourcing data. Summary of the invention

[0004] In order to improve the ability of the same map data (vehicle lane objects or semantic objects, etc.) in actual roads to be successfully clustered during clustering, thereby enriching technical routes and increasing selection space, an embodiment of the present invention provides a method, device and equipment for sampling objects in multi-trip data.

[0005] In a first aspect, an embodiment of the present invention provides a method for sampling objects in multi-pass data, which may include:

[0006] Based on a plurality of segment data included in the group data divided from the multi-trip data, the vehicle line objects with matching relationships and the semantic objects with matching relationships included in the segment data are combined in pairs to respectively construct a vehicle line object set and a semantic object set of the group data;

[0007] Based on the set of car-line objects and the set of semantic objects, car-line objects and semantic objects are sampled respectively, so as to determine an object alignment error based on the sampling results.

[0008] Optionally, sampling the vehicle lane object based on the vehicle lane object set may include:

[0009] Traversing each set of two-by-two matching car line objects in the car line object set, and determining whether the matching car line objects have an intersection;

[0010] If there is no intersection, traverse the other groups of matching car line objects in the car line object set;

[0011] If there is an intersection, determine whether the matched lane object has a curve part;

[0012] If there is no curve part, a straight line sampling is performed on the lane object;

[0013] If there is a curve portion, curve sampling is performed on the curve portion of the lane object.

[0014] Optionally, performing curve sampling on the matched vehicle lane object may include:

[0015] Based on the curve portion of the vehicle lane object, determining a curve segment corresponding to a matching interval of the curve portion;

[0016] Performing ellipse fitting based on the matching interval to respectively obtain two ellipses;

[0017] Taking the characteristic points where the direction of a curve segment corresponding to a lane object changes as sampling points, and constructing a matching vector with the center of the fitted ellipse and the sampling points;

[0018] Determine a matching point on the curve segment corresponding to the other car line object by fitting the center of the ellipse and the matching vector to the curve segment corresponding to the other car line object;

[0019] The sampling point and the matching point are used as alignment sampling points of the lane object.

[0020] Optionally, determining the matching interval of the curve portion of the vehicle line object may include:

[0021] Based on the extreme value points of the curvature change of the curve portion, the curve portion is segmented to obtain curve segments;

[0022] Constructing a curve segment feature matrix based on feature points on the curve segment including feature points with changed directions;

[0023] Matching is performed based on the curve segment feature matrix, and the matching interval of the curve portion of the lane object is determined using the curvature parameters and distance parameters of the two groups of feature points.

[0024] Optionally, after determining the matching interval of the curve portion of the lane object, the following steps may also be performed:

[0025] Extending the curve segment corresponding to the matching interval equidistantly to both ends, and weighted summing the matching results by the sum of the squares of the curvature differences of the feature points on the two groups of extended curve segments and the distances between the two groups of feature points;

[0026] The weighted summation result of the extended curve segment is compared with the weighted summation result of the curve segment corresponding to the current matching interval, and the matching interval is expanded based on the comparison result.

[0027] Optionally, before segmenting the curve portion, the method may further include: intercepting the curve portion in the car line object based on the curvature of the feature points included in the car line object.

[0028] Optionally, performing straight line sampling on the vehicle lane object may include:

[0029] Use one of the matched vehicle line objects as the sampling line and the other as the matching line;

[0030] Arranging sampling points at the beginning and end points of the sampling line, and inserting sampling points on the sampling line based on a preset distance threshold;

[0031] The sampling points on the sampling line are projected onto the matching line respectively; and it is determined whether the angle between the connecting line of the sampling point and the projection point and the matching line is within a preset angle range;

[0032] If so, the sampling point and the corresponding projection point are used as the alignment sampling points of the matched lane object.

[0033] Optionally, performing straight line sampling on the vehicle lane object may include:

[0034] Based on the projections of the first and last endpoints of the plurality of lane objects on the reference line included in the group data, intercepting a target reference line segment covering all the lane objects on the reference line;

[0035] Using the target reference line segment as a sampling line and arranging sampling points;

[0036] Projecting the sampling points onto the plurality of lane objects respectively;

[0037] Based on the angle between the line connecting the sampling point and the projection point and the car line object, and the number of projection points corresponding to the same sampling point, the aligned sampling points of the car line object are screened out from the projection points corresponding to the sampling point.

[0038] Optionally, the method for determining whether the matched lane object has a curve part is as follows:

[0039] Expanding a preset radius range outwards based on the axis of the car line object to obtain a first area;

[0040] Connecting the first and last points of the car line object to obtain the first and last lines of the car line object;

[0041] Expanding a preset radius range outward based on the head and tail lines to obtain a second area;

[0042] Based on the first area and the second area, an intersection area is determined, so as to determine whether there is a curve portion in the lane object based on the intersection area and a preset ratio threshold.

[0043] Optionally, the method for determining whether the matched lane objects have an intersection is as follows:

[0044] Determine the direction vectors of the two matching car line objects based on the trajectory lines corresponding to the car line objects respectively;

[0045] Based on the direction vector of the vehicle line object and the endpoint position of the vehicle line object, it is determined whether the matched vehicle line objects have an intersection.

[0046] Optionally, sampling the semantic object based on the semantic object set may include:

[0047] Traversing each group of semantic objects that match each other in the semantic object set, and determining whether a ratio of similar areas of the matched semantic objects is greater than a first area ratio threshold;

[0048] If it is less than the first area ratio threshold, traversing other groups of semantic objects that match each other in the semantic object set;

[0049] If it is greater than the first area ratio threshold, determining whether the ratio of similar areas of the matched semantic objects is greater than a second area ratio threshold; wherein the first area ratio threshold is less than the second area ratio threshold;

[0050] If it is greater than the second area ratio threshold, the centroid points of the semantic objects are respectively used as alignment sampling points;

[0051] If it is less than the second area ratio threshold, determining whether the outer boundary of the semantic object is a rectangle;

[0052] If it is a rectangle, traverse the other groups of semantic objects that match each other in the semantic object set;

[0053] If it is not a rectangle, a local semantic object corresponding to the highest similarity region is determined in the matched semantic objects, and the centroid point of the local semantic object is used as the alignment sampling point.

[0054] Optionally, determining the highest similarity region may include:

[0055] Determine the respective graphic bounding boxes based on the matched semantic objects, and determine a target graphic bounding box covering the two graphic bounding boxes based on the length and width of the respective graphic bounding boxes;

[0056] Constructing a granularity rectangle based on the outer bounding box of the target graphic, and constructing a first semantic object feature matrix based on a first semantic object in the matched semantic objects and the granularity rectangle; wherein the matched semantic objects include a first semantic object and a second semantic object, and an area of ​​the first semantic object is greater than an area of ​​the second semantic object;

[0057] Based on the second semantic object and the granularity rectangle, determining a second semantic object feature matrix of a sliding search window and the second semantic object;

[0058] Sliding the sliding search window on the outer border of the target graphic to determine the similarity between the first semantic object feature matrix and the second semantic object feature matrix after each sliding;

[0059] The highest similarity value is determined, and the region with the highest similarity value is taken as the highest similarity region.

[0060] Optionally, the highest similarity value is compared with a preset highest similarity threshold, and if the highest similarity value is greater than the preset highest similarity threshold, the centroid point of the local semantic object corresponding to the highest similarity region is used as the alignment sampling point.

[0061] In a second aspect, an embodiment of the present invention provides an object sampling device in multi-pass data, which may include:

[0062] A construction module, configured to construct a set of vehicle line objects and a set of semantic objects of the group data based on a plurality of segment data included in the group data divided from the plurality of trips of data, by combining the vehicle line objects with matching relationships and the semantic objects with matching relationships included in the segment data in pairs, respectively;

[0063] A sampling module is used to sample the car-line objects and the semantic objects respectively based on the car-line object set and the semantic object set, so as to determine the object alignment error based on the sampling result.

[0064] In a third aspect, an embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the object sampling method in multiple passes of data as described in the first aspect.

[0065] In a fourth aspect, an embodiment of the present invention provides a computer device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the object sampling method in multiple passes of data as described in the first aspect is implemented.

[0066] The beneficial effects of the above technical solution provided by the embodiment of the present invention include at least:

[0067] The embodiments of the present invention provide a method, device and equipment for sampling objects in multi-pass data. The method samples objects in multi-pass data, determines the alignment error between objects at sampling points, and aligns objects in the multi-pass data based on the alignment error, providing a basis for subsequent clustering of the multi-pass data. In the specific implementation of the embodiments of the present invention, the group data divided by the multi-pass data is used as a processing unit, which avoids the inconsistency of the positions of the first and last points of the multi-pass data and improves the processing rate during data processing. The above method samples the vehicle line objects and semantic objects to determine the longitudinal error and lateral error between the multi-pass data, which can effectively correct the absolute errors between the multi-pass data due to sampling accuracy and multiple multi-pass acquisitions, laying the foundation for the subsequent production of high-definition maps based on multi-pass data.

[0068] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0069] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0071] Figure 1 A flowchart of a method for sampling objects in multiple passes of data provided in an embodiment of the present invention;

[0072] Figure 2 A schematic diagram of segment data provided in an embodiment of the present invention;

[0073] Figure 3 A schematic diagram of line production provided in an embodiment of the present invention;

[0074] Figure 4 A schematic diagram of multiple data groupings provided in an embodiment of the present invention;

[0075] Figure 5One of the flow charts of sampling a vehicle lane object based on a vehicle lane object set provided in an embodiment of the present invention;

[0076] Figure 6 The second flowchart of sampling the vehicle lane objects based on the vehicle lane object set provided in the embodiment of the present invention;

[0077] Figure 7 A detailed flow chart of sampling a vehicle lane object based on a vehicle lane object set provided in an embodiment of the present invention;

[0078] Figure 8 A schematic diagram of determining whether two lane objects have an intersection provided in an embodiment of the present invention;

[0079] Fig. 9 A schematic diagram of determining whether a lane object has a curved portion provided in an embodiment of the present invention;

[0080] Fig.10 This is one of the flow charts for performing curve sampling on the matched lane object in step S55;

[0081] Fig.11 This is the second flow chart of performing curve sampling on the matched lane object in step S55;

[0082] Fig.12 This is one of the flowcharts for determining the matching interval in step S101;

[0083] Fig.13 This is the second flowchart of determining the matching interval in step S101;

[0084] Fig.14 A schematic diagram of determining a matching interval in step S101;

[0085] Fig.15 A flow chart of direct sampling of a straight line object provided in an embodiment of the present invention;

[0086] Fig.16 This is one of the direct sampling processes of the straight line object provided in the embodiment of the present invention;

[0087] Fig.17 This is the second direct sampling process of the straight line object provided in the embodiment of the present invention;

[0088] Fig.18 This is the third direct sampling process of the straight line object provided in the embodiment of the present invention;

[0089] Fig.19 A flow chart of the reference line sampling of a straight line object provided in an embodiment of the present invention;

[0090] Fig. 20A schematic diagram of a sampling of reference lines of a straight line object provided in an embodiment of the present invention;

[0091] Fig.21 One of the flowcharts of semantic object sampling provided in an embodiment of the present invention;

[0092] Fig. 22 This is the second flowchart of semantic object sampling provided in an embodiment of the present invention;

[0093] Fig.23 A flowchart for determining the highest similarity region provided in an embodiment of the present invention;

[0094] Fig.24 A schematic diagram of determining a graphic outer frame provided in an embodiment of the present invention;

[0095] Fig.25 A schematic diagram of constructing a feature matrix of a semantic object provided in an embodiment of the present invention;

[0096] Fig.26 A schematic diagram of determining a sliding search window provided in an embodiment of the present invention;

[0097] Fig. 27 A schematic diagram of similarity calculation of semantic object feature matrices provided in an embodiment of the present invention;

[0098] Fig.28 It is a schematic diagram of the structure of an object sampling device in multiple passes of data provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0099] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although the exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0100] In the process of making a high-precision map based on multi-pass data, the same map data in the actual road, such as lane objects, semantic objects of the road surface, etc., must be clustered and then aggregated into a semantic object. The inventors found that due to the limited acquisition accuracy of multi-source data during acquisition, and the density-based clustering has certain requirements on the distance of the same semantic object, it is necessary to align the multi-pass data so that the multi-pass data are roughly in the same position to facilitate clustering and aggregation. In the process of aligning the multi-pass data, the inventors also found that due to the multiple acquisitions of the multi-pass visual data itself, the data accuracy is limited, and there is a lack of effective scale calibration information between each pass of data. Therefore, how to align the multi-pass data is a necessary step in the subsequent construction of a high-precision map. In view of the above problems, the present invention is proposed to provide a method, device and equipment for sampling objects in multi-pass data that overcomes the above problems or at least partially solves the above problems.

[0101] The present invention provides a method for sampling objects in multiple passes of data. Figure 1 As shown, the method may include the following steps:

[0102] Step S11, based on the multiple segment data included in the group data divided from the multi-pass data, the vehicle line objects with matching relationship and the semantic objects with matching relationship included in the segment data are combined in pairs to respectively construct a vehicle line object set and a semantic object set of the group data.

[0103] Step S12: based on the car line object set and the semantic object set, sample the car line objects and the semantic objects respectively, so as to determine the object alignment error based on the sampling results.

[0104] In an embodiment of the present invention, object sampling is performed on multiple passes of data, and the alignment error between objects is determined by sampling points. The objects in the multiple passes of data are aligned based on the alignment error, providing a basis for subsequent clustering of the multiple passes of data. In the specific implementation of the embodiment of the present invention, the group data divided by the multiple passes of data is used as a processing unit, which avoids the inconsistency of the positions of the first and last points of the multiple passes of data and improves the processing rate during data processing. The above method samples the vehicle line objects and semantic objects to determine the longitudinal error and lateral error between the multiple passes of data, which can effectively correct the absolute errors between the multiple passes of data due to sampling accuracy and multiple passes of acquisition, laying the foundation for the subsequent production of high-definition maps based on the multiple passes of data.

[0105] In order to more clearly explain that the above-mentioned multi-pass data uses group data as the minimum processing unit in the embodiment of the present invention, the embodiment of the present invention describes the group data divided by the multi-pass data through the following content. Figure 2As shown, a single trip of data consists of several connected segments, each of which contains a semantic object and a lane object. Multiple trips of segment data at the same position can be combined into a bunch of data, that is, each single trip of data is segmented based on the same processing mechanism, and the segment data at the same position are combined into a bunch of data.

[0106] Before segmentation, pre-process the single-trip data separately, and establish the position relationship in the horizontal and vertical directions based on the line information, that is, the vehicle lane object information. In the specific implementation, the vertical vehicle lane object is made into a line (i.e. the vertical relationship), and the semantic objects (marking semantic objects, such as arrows, zebra crossings, etc.) in the single-trip data are mounted on the line. Figure 3 As shown in the figure, the vertical thin line is the produced line. In the same frame of data, the absolute relationship between the above-mentioned lane object and the semantic object is known, so the position relationship of the semantic object in a single trip data can be calibrated. After the calibration is completed, due to the large amount of data and the different starting and ending points between each single trip route, it is not possible to use one trip route as a whole for sampling and alignment. At this time, the single trip data needs to be segmented. In the specific implementation, the single trip data is divided into several segments according to the positions of preset signs such as intersections, zebra crossings, stop lines, etc.

[0107] After the multi-trip data is segmented, it is grouped, that is, the multi-trip data establishes a group relationship, and multiple single-trip data representing the same section of road are divided into one group. A topological relationship is established between adjacent groups, and each group can be written with an ID based on the longitudinal extension characteristics of the line. Figure 4 As shown in the figure, the segments at the same position will form a group data. The endpoints of the group data are crosses. The cross represents the location point of the end of the bunch. Through the relationship of the cross, we can determine which bunch it belongs to, and also determine the relationship between adjacent bunches. Figure 4 As shown, after grouping, the semantic objects and car line objects at the actual same position in the group can be sampled and aligned, so that the aligned semantic objects and car line objects can meet the conditions for clustering.

[0108] In an optional embodiment, the above-mentioned sampling of the car line object based on the car line object set is performed, referring to Figure 5 to Figure 7 As shown, the following steps may be included:

[0109] Step S51, traverse each group of two-way matching car line objects in the car line object set, and determine whether the matching car line objects have an intersection; if there is no intersection, execute step S52; if there is an intersection, execute step S53.

[0110] In this step, firstly, the direction vectors of the two matching car line objects are determined based on the trajectory lines corresponding to the car line objects; then, based on the direction vectors of the car line objects and the endpoint positions of the car line objects, it is determined whether the matching car line objects have an intersection. Figure 8 As shown, to determine whether two directional lines intersect in a plane, the concept of vectors is needed. For two lines L1 and L2, assume that their direction vectors are v1 and v2 respectively. If the two lines intersect in a plane, then their intersection point P can be expressed as: P = t1*L1+t2*L2. Among them, t1 and t2 are distance parameters, indicating the distance of point P to L1 and L2. Convert the above formula into vector form and we get: P = t1*v1+t2*v2. If the two lines are parallel or coincident, t1 and t2 have only one value, which means that P has only one position. If the two lines have no intersection, t1 and t2 have no common solution, that is, point P does not exist in the plane. Therefore, we can check whether the two lines have an intersection by solving this equation.

[0111] The solution to the above equation is: 1) Calculate the cross product of L1 and L2. If the cross product is zero, then the two lines are parallel or coincident. 2) If the cross product is not zero, then use the direction of the cross product to determine the signs of the two parameters t1 and t2. If the dot product of v1 and v2 is positive, then both t1 and t2 are positive; if the dot product is negative, then both t1 and t2 are negative. 3) Divide the cross product by the magnitude of v1 and v2 to get a vector w that is perpendicular to L1 and L2. Normalize w to get a new vector w_unit. 4) Multiply the w_unit vector by the L1 and L2 vectors respectively to get the two distance parameters t1 and t2. If both distance parameters have real solutions, then the two lines intersect. Refer to Figure 8 As shown, the above two car line objects are projected along the direction perpendicular to the vector. Simply judging that there is no intersection point can determine that the above two car line objects have no intersection. Figure 8 In the middle left image, the projections of the two endpoints of the short car line object have an intersection, so there is an intersection; in the right image, the projections of the two endpoints of the short car line object have no intersection, so there is no intersection, and this car line object is no longer sampled.

[0112] Step S52: traverse other groups of matching car line objects in the car line object set.

[0113] Step S53, determine whether the matched lane object has a curved part; if no curved part exists, execute step S54; if a curved part exists, execute step S55.

[0114] In the specific implementation of the embodiment of the present invention, an expansion algorithm is used to determine whether a lane has a curved portion, for example, referring to Fig. 9As shown, first, the preset radius range is expanded outward based on the axis line of the car line object to obtain the first area; second, the first and last points of the car line object are connected to obtain the first and last lines of the car line object; third, the preset radius range is expanded outward based on the first and last lines to obtain the second area; fourth, based on the first area and the second area, the intersection area is determined to determine whether there is a curve part in the car line object based on the intersection area and a preset ratio threshold.

[0115] In step S53 of the embodiment of the present invention, the straightness of the car line object is determined based on the threshold value, the car line object is expanded with a specified radius to form a figure, and then the line connecting the first and last points of the car line object is expanded with a specified radius to form a figure, and it is determined whether the intersection area of ​​the two figures exceeds the specified threshold to determine whether the car line object includes a curved part. In a specific example, the car line object itself is used as the axis line, and the radius is one meter, and the outer extension is performed; then, a straight line is connected between the two endpoints of the line segment, and the straight line is expanded one meter to the left and right respectively; if the ratio of the intersection area of ​​the two at the endpoints is greater than 0.95, the car line object is judged to be a straight line; if it is less than or equal to 0.95, it is judged that the car line object has a curved part.

[0116] Step S54, performing straight line sampling on the car line object. In the embodiment of the present invention, when performing straight line sampling on the straight line portion of the car line object, a point-to-line projection method is used to form sampling points, which will be described in detail below.

[0117] Step S55: perform curve sampling on the curve part of the lane object.

[0118] In a specific embodiment, in the above step S55, curve sampling is performed on the matched car line object. When sampling a straight line, the sampling points are formed by point-to-point projection. However, using point-to-point projection on the curve part will produce erroneous lateral errors, so it is necessary to use unconventional sampling methods. That is, two matched car line objects are used as input, and the matching interval between the two matched car line objects is found. The two curve parts are respectively elliptical fitted according to their own matching intervals. The feature points on one line are taken to find the corresponding matching points on the other curve. The center of the ellipse obtained by fitting is used to calculate the vector from the center of the circle to the feature point. Based on this vector, the corresponding matching point is found on the other curve. In this way, the curve sampling is completed. Refer to Fig.10 and Fig.11 As shown, the following steps may be specifically included:

[0119] Step S101, based on the curve part of the lane object, determine the curve segment corresponding to the matching interval of the curve part. In this step, finding the lane line matching interval is mainly divided into four steps, namely: curve segmentation, constructing a curve feature matrix, matching interval search, and determining the best matching interval.

[0120] Step S102: performing ellipse fitting based on the matching interval to obtain two ellipses respectively.

[0121] Step S103: taking a characteristic point on a curve segment corresponding to a lane object where the direction changes as a sampling point, and constructing a matching vector with the center of the fitted ellipse and the sampling point.

[0122] Step S104: fitting the center of the ellipse and the matching vector with the curve segment corresponding to the other car line object to determine the matching point on the curve segment corresponding to the other car line object.

[0123] Step S105: Use the sampling points and the matching points as alignment sampling points of the lane object.

[0124] In the embodiment of the present invention, when ellipse fitting is performed on two matching curve segments, a feature point on a certain line is used as a reference point, and a vector from the feature point to the center of the ellipse is calculated (in a local coordinate system with the center of the ellipse as the origin, the minor axis as the x-axis, and the major axis as the y-axis). Based on this vector, the position corresponding to the feature point is restored on the center of the ellipse of another curve fitting to obtain a set of aligned sampling points.

[0125] In a specific embodiment, referring to Fig.12 , Fig.13 and Fig.14 As shown, determining the matching interval of the curve portion may specifically include the following steps:

[0126] Step S121: based on the curvature of the feature points included in the car line object, intercept a curve portion in the car line object.

[0127] It should be noted that when generating a car line object, the coordinates of the points where the direction changes are retained as feature points on the line. Connecting multiple feature points constitutes a car line object. For lines that do not change direction, only the first and last points are retained. Therefore, when constructing the feature matrix of the car line object, there is no need to perform sampling to generate points that describe the features of the car line object. Fig.14 As shown in (1) and (2), the curvature of the feature points on the car line object can be used to divide it into a straight line part and a curve part according to the curvature change. The straight line part uses straight line sampling, and the curve part uses curve sampling.

[0128] Step S122: based on the extreme value point of the curvature change of the curve part, the curve part is segmented to obtain curve segments. This step calculates the curvature of each feature point on a curve, and each feature point and the previous feature point are used to obtain the curvature change of the feature point. The curve segmentation is performed at the extreme value point of the curvature change to obtain a number of curve segments. Fig.14 As shown in (3), the curve part is divided into curve segments ① to ⑤.

[0129] Step S123: construct a curve segment feature matrix based on the feature points on the curve segment whose directions have changed.

[0130] Assume that there are n characteristic points on a curve segment T, which are There are m characteristic points on another curve segment Q, which are Establish the feature matrix M respectively T and M Q , constructed using the distance between each point on the line and all points. To establish the feature matrix M T For example:

[0131]

[0132] in

[0133] choose As a starting point, The local characteristic matrix of the curve segment is obtained as the termination point.

[0134]

[0135] New feature matrix By changing M T The rows in cyclically shift up (k-1) rows, and the columns in cyclically shift up (k-1) columns are obtained. Therefore, any feature point is selected as the starting feature point, and the corresponding feature matrix can be obtained by using cyclic shift.

[0136] Step S124: matching is performed based on the curve segment feature matrix, and the matching interval of the curve portion of the lane object is determined using the curvature parameters and distance parameters of the two groups of feature points.

[0137] If there is a complete match between curve segment T and curve segment Q, and the feature point and are located in the matching parts of T and Q respectively, then there must be a submatrix R such that f(r ij )minimum.

[0138]

[0139]

[0140] Or the calculation result S pq If it is greater than the specified threshold δ, it is considered that there is a matching relationship.

[0141]

[0142] This step is to find the best matching interval, refer to Fig.14 As shown in (3), ② and ④ can be combined with Fig.14 When matching the short car line object below it, if there are multiple matching intervals, it is necessary to filter out the best matching interval. The details are as follows:

[0143] Take n points at equal intervals in these sampling intervals, calculate the sum of the squares of the curvature differences between the two groups of points and the distance between the two groups of points, and perform weighted summation of the two values ​​to obtain the best matching interval.

[0144]

[0145] The smaller V is, the closer the curve shape is, so the best matching interval is found, as shown in Fig.14 As shown in , curve segment ② is the best matching interval.

[0146] Step S125: Extend the curve segment corresponding to the matching interval equidistantly to both ends, and perform weighted summation on the matching results using the sum of the squares of the curvature differences of the feature points on the two groups of extended curve segments and the distances between the two groups of feature points.

[0147] Step S126: compare the weighted sum of the extended curve segments with the weighted sum of the curve segments corresponding to the current matching interval, and expand the matching interval based on the comparison result.

[0148] The above steps S125 and S126 extend the curve segment to both ends at equal intervals based on the determined current best matching interval. After the extension, the interval needs to be recalculated. Value, if If the value is not greater than the existing value, the starting or ending matching point is extended to the extended boundary point, and the above process is repeated until The match ends when the value is greater than the existing value (the smaller the V value, the better the match), completing the expansion of the matching range.

[0149] In another alternative embodiment, referring to Figure 6 As shown, both the straight line portion of the lane object and the completely straight lane object require straight line sampling. The embodiment of the present invention provides two sampling methods when sampling the (straight line) lane object, as follows:

[0150] <Method 1> (Straight Line) Direct Sampling of Car Line Objects

[0151] Reference Fig.15 As shown, for example, sampling points are arranged at the positions of 1 / 10 of the length of the first and last 1 / 10 of the length of the straight line object, which specifically includes the following steps:

[0152] Step S151: use one of the matched vehicle lane objects as a sampling line and the other as a matching line.

[0153] Step S152: Arrange sampling points at the beginning and end points of the sampling line, and insert sampling points on the sampling line based on a preset distance threshold.

[0154] Step S153: Project the sampling points on the sampling line onto the matching line respectively.

[0155] Step S154, determine whether the angle between the line connecting the sampling point and the projection point and the matching line is within a preset angle range; if so, execute step S155.

[0156] Step S155: Use the sampling point and the corresponding projection point as the alignment sampling point of the matched lane object.

[0157] Reference Figure 16 to Figure 18 As shown, in the embodiment of the present invention, different segments in the same bunch have a matching relationship. Two mutually matching segments 1 and 2 are confirmed, and the car line object in one segment 1 is selected as the line where the sampling point is located, and the car line object in the other segment 2 is the matching line. The sampling point on each sampling line is projected onto the matching line, and the angle between the line connecting the sampling point and the projection point and the matching line is calculated to be greater than 85° and less than 95°. If it is, it means that the projection point meets the requirements and is retained. Points that do not meet the requirements are discarded. A set of matching sampling points can be generated by the above method.

[0158] <Method 2> (Straight Line) Car Line Object Reference Line Sampling

[0159] This method is based on the reference line for sampling. Fig.19 and Fig. 20 As shown, the following steps may be included:

[0160] Step S191: based on the projections of the first and last endpoints of the plurality of lane objects on the reference line included in the group data, intercept a target reference line segment covering all the lane objects on the reference line.

[0161] Step S192: Use the target reference line segment as a sampling line and arrange sampling points.

[0162] Step S193: project the sampling points onto the multiple lane objects respectively.

[0163] Step S194: based on the angle between the line connecting the sampling point and the projection point and the car line object, and the number of projection points corresponding to the same sampling point, select the aligned sampling points of the car line object from the projection points corresponding to the sampling point.

[0164] In the embodiment of the present invention, there is a reference line in each group data (bunch) (which can be directly obtained from the map data and exists throughout the bunch), and there are multiple matching lane objects in each bunch. For example, Fig. 20 As can be seen from the left, a set of matching car line objects includes three car line objects. Each endpoint of the car line object is projected onto the reference line, and the two farthest points are obtained. The intercepted target reference line segment is obtained on the reference line, so that the length of the intercepted target reference line segment can completely cover the three car line objects. If the length of the intercepted target reference line segment exceeds the preset distance threshold, sampling is performed according to the reference line length divided by the first preset distance value. If sampling is performed according to the second preset distance value, the interval is metered. Fig. 20 The sampling points on the target reference line segment are projected onto the matching car line object. The retained sampling points need to meet two conditions at the same time: ① The angle between the projection point and the sampling point and the matching car line object is between 95 and 85 (as shown in the right figure below, there is an angle of 100°, and the corresponding sampling point is discarded); ② There are more than two projection points on the matching car line object (such as Fig. 20 Right one, after projection, the upper right endpoint has only one projection point, so the corresponding sampling point is discarded), and then the sampling points that meet the requirements are formed into a group of sampling points.

[0165] In another optional embodiment, the embodiment of the present invention samples semantic objects based on a semantic object set, referring to Fig.21 and Fig. 22 As shown, the following steps may be specifically included:

[0166] Step S221, traverse each group of semantic objects that match each other in the semantic object set, and determine whether the ratio of the similar areas of the matched semantic objects is greater than a first area ratio threshold; if it is less than the first area ratio threshold, execute step S222; if it is greater than the first area ratio threshold, execute step S223.

[0167] Step S222: traverse other groups of semantic objects that match each other in the semantic object set.

[0168] Step S223, determine whether the ratio of the similar areas of the matched semantic objects is greater than a second area ratio threshold; wherein the first area ratio threshold is less than the second area ratio threshold; if it is greater than the second area ratio threshold, execute step S224; if it is less than the second area ratio threshold, execute step S225. In the embodiment of the present invention, the first area ratio threshold may be 0.5, and the second area ratio threshold may be 0.9.

[0169] Step S224: Use the centroid points of the semantic objects as alignment sampling points respectively.

[0170] Step S225, determine whether the outer boundary of the semantic object is a rectangle; if it is a rectangle, execute step S222; if it is not a rectangle, execute step S226. In the embodiment of the present invention, since the rectangular pattern lacks alignment features, that is, when matching, it is not possible to accurately identify which part can be matched successfully, the rectangular semantic object is no longer sampled.

[0171] Step S226: Determine the local semantic object corresponding to the highest similarity region in the matched semantic objects, and use the centroid point of the local semantic object as the alignment sampling point.

[0172] In the embodiment of the present invention, when sampling semantic objects, it is first determined whether two semantic objects are similar, and only if they are similar, are they sampled as a pair of semantic objects. In the specific sampling process, if the similarity area of ​​the two semantic objects is large when they match, the centroid point can be directly used as a pair of matching sampling points; if only a partial match is achieved, the two semantic objects can be partially matched and then sampled locally.

[0173] In another alternative embodiment, referring to Fig.23 As shown, determining the highest similarity region may specifically include the following steps:

[0174] Step S231 : determining respective graphic bounding boxes based on the matched semantic objects, and determining a target graphic bounding box covering the two graphic bounding boxes based on the length and width of the respective graphic bounding boxes.

[0175] For the two matching semantic objects, determine their respective graphic bounding boxes, which can completely cover the semantic objects. Fig.24 As shown, the two semantic objects, one on the left and one on the right, are matched, and the graphic outer frame one and the graphic outer frame two are determined according to the first semantic object and the second semantic object, respectively. Then, according to the length and width of the graphic outer frame one and the graphic outer frame two, the target outer frame is determined, which can completely cover the graphic outer frame one and the graphic outer frame two. When calculating the matching graphic outer frame, when calculating the similarity between the feature matrices of a group of matching semantic objects, it is necessary to use the feature matrix of the semantic object with a smaller area (the second semantic object) to slide the window on the feature matrix of the semantic object with a larger area (the first semantic object). Then, it is necessary to ensure that the two dimensions (horizontal and vertical) of the feature matrix of the semantic object with a larger area must be greater than the two dimensions of the feature matrix of the semantic object with a smaller area (the second semantic object). Therefore, before constructing the semantic object feature matrix, it is necessary to determine the target outer frame of the matching graphic, and generate the feature matrix according to the target outer frame.

[0176] Step S232: construct a granularity rectangle based on the outer border of the target graphic, and construct a first semantic object feature matrix based on the first semantic object and the granularity rectangle in the matched semantic objects; wherein the matched semantic objects include the first semantic object and the second semantic object, and the area of ​​the first semantic object is greater than the area of ​​the second semantic object.

[0177] In this step, a granular rectangle is generated in the target bounding box of the matching semantic object. For example, the size of the granular rectangle used is 10cm*10cm. The granular rectangle is moved from left to right and from top to bottom in the target bounding rectangle, and the intersection area between the granular rectangle and the semantic object after each movement is calculated (refer to Fig.25 The numbers marked in the medium-granularity rectangles represent the intersection area ratio between each granularity rectangle and the semantic object, and the feature matrix of the semantic object is constructed using the intersection area.

[0178] Step S233: Determine a second semantic object feature matrix of the sliding search window and the second semantic object based on the second semantic object and the granularity rectangle.

[0179] This step determines the sliding search window (small rectangular non-zero grid interval search) based on the semantic object with a smaller area, calculates the non-zero interval size of the semantic object with a smaller area (that is, the value of each granularity rectangle is not zero), and uses this non-zero interval dimension to generate a sliding search window (refer to Fig.26 The right side shown is the determined sliding search window).

[0180] Step S234: slide the sliding search window on the outer border of the target graphic to determine the similarity between the first semantic object feature matrix and the second semantic object feature matrix after each slide.

[0181] This step calculates the similarity of the object matrix. The sliding search window is used to move on the feature matrix of the larger semantic object. The similarity s between the matrix in each sliding search window (e.g., a) and the feature matrix of the smaller semantic object (e.g., b) is calculated. The sliding matching process can be referred to Fig. 27 The calculation formula is as follows:

[0182]

[0183] Step S235, determine the highest similarity value, and use the region with the highest similarity value as the highest similarity region. In specific implementation, if the similarity is higher than 0.9, use the centroid of the semantic object in the region with the highest similarity and the centroid of the matching semantic object as a set of matching sampling points.

[0184] Step S236, compare the highest similarity value with the preset highest similarity threshold, if the highest similarity value is greater than the preset highest similarity threshold, the centroid point of the local semantic object corresponding to the highest similarity region is used as the alignment sampling point. This step further screens, so that better alignment sampling points can be screened out.

[0185] The semantic object sampling method provided by the embodiment of the present invention can determine the longitudinal error and the lateral error between the semantic objects included in each segment data in the group data, so as to lay a foundation for the multi-pass data alignment in the subsequent steps.

[0186] Based on the same inventive concept, an object sampling device in multiple passes of data is also provided in an embodiment of the present invention. Fig.28 As shown, the device may include: a construction module 281 and a sampling module 282, and its working principle is as follows:

[0187] The construction module 281 is used to construct a set of vehicle line objects and a set of semantic objects of the group data based on the plurality of segment data included in the group data divided by the multi-pass data, by combining the vehicle line objects with matching relationships and the semantic objects with matching relationships included in the segment data in pairs.

[0188] The sampling module 282 is used to sample the vehicle line objects and the semantic objects respectively based on the vehicle line object set and the semantic object set, so as to determine the object alignment error based on the sampling results.

[0189] In an optional embodiment, the sampling module 282 is specifically used for:

[0190] Traversing each set of two-by-two matching car line objects in the car line object set, and determining whether the matching car line objects have an intersection;

[0191] If there is no intersection, traverse the other groups of matching car line objects in the car line object set;

[0192] If there is an intersection, determine whether the matched lane object has a curve part;

[0193] If there is no curve part, a straight line sampling is performed on the lane object;

[0194] If there is a curve portion, curve sampling is performed on the curve portion of the lane object.

[0195] In another optional embodiment, the sampling module 282 is further configured to:

[0196] Based on the curve portion of the vehicle lane object, determining a curve segment corresponding to a matching interval of the curve portion;

[0197] Performing ellipse fitting based on the matching interval to respectively obtain two ellipses;

[0198] Taking the characteristic points where the direction of a curve segment corresponding to a lane object changes as sampling points, and constructing a matching vector with the center of the fitted ellipse and the sampling points;

[0199] Determine a matching point on the curve segment corresponding to the other car line object by fitting the center of the ellipse and the matching vector to the curve segment corresponding to the other car line object;

[0200] The sampling point and the matching point are used as alignment sampling points of the lane object.

[0201] In another optional embodiment, the sampling module 282 is further configured to:

[0202] Based on the extreme value points of the curvature change of the curve portion, the curve portion is segmented to obtain curve segments;

[0203] Constructing a curve segment feature matrix based on feature points on the curve segment including feature points with changed directions;

[0204] Matching is performed based on the curve segment feature matrix, and the matching interval of the curve portion of the lane object is determined using the curvature parameters and distance parameters of the two groups of feature points.

[0205] In another optional embodiment, the sampling module 282 is further used to: extend the curve segment corresponding to the matching interval to both ends equidistantly, and perform weighted summation on the matching results by taking the sum of the squares of the curvature differences of the feature points on the two groups of extended curve segments and the distances between the two groups of feature points;

[0206] The weighted summation result of the extended curve segment is compared with the weighted summation result of the curve segment corresponding to the current matching interval, and the matching interval is expanded based on the comparison result.

[0207] In another optional embodiment, the sampling module 282 is further specifically configured to: intercept a curve portion in the vehicle line object based on a curvature of a feature point included in the vehicle line object.

[0208] In another optional embodiment, the sampling module 282 is further configured to: use one of the matched vehicle lane objects as a sampling line and another one as a matching line;

[0209] Arranging sampling points at the beginning and end points of the sampling line, and inserting sampling points on the sampling line based on a preset distance threshold;

[0210] The sampling points on the sampling line are projected onto the matching line respectively; and it is determined whether the angle between the connecting line of the sampling point and the projection point and the matching line is within a preset angle range;

[0211] If so, the sampling point and the corresponding projection point are used as the alignment sampling points of the matched lane object.

[0212] In another optional embodiment, the sampling module 282 is further configured to: based on the projections of the first and last endpoints of the plurality of lane objects on the reference line included in the group data, intercept a target reference line segment covering all the lane objects on the reference line;

[0213] Using the target reference line segment as a sampling line and arranging sampling points;

[0214] Projecting the sampling points onto the plurality of lane objects respectively;

[0215] Based on the angle between the line connecting the sampling point and the projection point and the car line object, and the number of projection points corresponding to the same sampling point, the aligned sampling points of the car line object are screened out from the projection points corresponding to the sampling point.

[0216] In another optional embodiment, the sampling module 282 is further configured to: expand a preset radius range outward based on the axis of the lane object to obtain a first area;

[0217] Connecting the first and last points of the car line object to obtain the first and last lines of the car line object;

[0218] Expanding a preset radius range outward based on the head and tail lines to obtain a second area;

[0219] Based on the first area and the second area, an intersection area is determined, so as to determine whether there is a curve portion in the lane object based on the intersection area and a preset ratio threshold.

[0220] In another optional embodiment, the sampling module 282 is further configured to: traverse each set of semantic objects that are matched in pairs in the semantic object set, and determine whether a ratio of similar areas of the matched semantic objects is greater than a first area ratio threshold;

[0221] If it is less than the first area ratio threshold, traversing other groups of semantic objects that match each other in the semantic object set;

[0222] If it is greater than the first area ratio threshold, determining whether the ratio of similar areas of the matched semantic objects is greater than a second area ratio threshold; wherein the first area ratio threshold is less than the second area ratio threshold;

[0223] If it is greater than the second area ratio threshold, the centroid points of the semantic objects are respectively used as alignment sampling points;

[0224] If it is less than the second area ratio threshold, determining whether the outer boundary of the semantic object is a rectangle;

[0225] If it is a rectangle, traverse the other groups of semantic objects that match each other in the semantic object set;

[0226] If it is not a rectangle, a local semantic object corresponding to the highest similarity region is determined in the matched semantic objects, and the centroid point of the local semantic object is used as the alignment sampling point.

[0227] In another optional embodiment, the sampling module 282 is further specifically used to: determine the respective graphic bounding boxes based on the matched semantic objects, and determine the target graphic bounding box covering the two graphic bounding boxes based on the length and width of the respective graphic bounding boxes;

[0228] Constructing a granularity rectangle based on the outer bounding box of the target graphic, and constructing a first semantic object feature matrix based on a first semantic object in the matched semantic objects and the granularity rectangle; wherein the matched semantic objects include a first semantic object and a second semantic object, and an area of ​​the first semantic object is greater than an area of ​​the second semantic object;

[0229] Based on the second semantic object and the granularity rectangle, determining a second semantic object feature matrix of a sliding search window and the second semantic object;

[0230] Sliding the sliding search window on the outer border of the target graphic to determine the similarity between the first semantic object feature matrix and the second semantic object feature matrix after each sliding;

[0231] The highest similarity value is determined, and the region with the highest similarity value is taken as the highest similarity region.

[0232] In another optional embodiment, the sampling module 282 is further specifically used to: compare the highest similarity value with a preset highest similarity threshold; if the highest similarity value is greater than the preset highest similarity threshold, the centroid point of the local semantic object corresponding to the highest similarity area is used as the alignment sampling point.

[0233] Based on the same inventive concept, an embodiment of the present invention further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the object sampling method in the multi-pass data is implemented.

[0234] Based on the same inventive concept, a computer device is also provided in an embodiment of the present invention, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the above-mentioned object sampling method in multiple passes of data when executing the program.

[0235] The principles of the problems solved by the above-mentioned devices, media, and related equipment in the embodiments of the present invention are similar to those of the above-mentioned methods, so their implementation can refer to the implementation of the above-mentioned methods, and the repeated parts will not be repeated.

[0236] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) containing computer-usable program code.

[0237] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0238] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0239] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0240] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for sampling objects in multi-pass data, characterized in that: include: Based on a plurality of segment data included in the group data divided from the multi-trip data, the vehicle line objects with matching relationships and the semantic objects with matching relationships included in the segment data are combined in pairs to respectively construct a vehicle line object set and a semantic object set of the group data; Based on the set of car-line objects and the set of semantic objects, car-line objects and semantic objects are sampled respectively, so as to determine an object alignment error based on the sampling results.

2. The method according to claim 1, characterized in that Sampling the vehicle line object based on the vehicle line object set includes: Traversing each set of two-by-two matching car line objects in the car line object set, and determining whether the matching car line objects have an intersection; If there is no intersection, traverse the other groups of matching car line objects in the car line object set; If there is an intersection, determine whether the matched lane object has a curve part; If there is no curve part, a straight line sampling is performed on the lane object; If there is a curve portion, curve sampling is performed on the curve portion of the lane object.

3. The method according to claim 2, characterized in that The curve sampling of the matched lane object includes: Based on the curve portion of the vehicle line object, determining a curve segment corresponding to a matching interval of the curve portion; Performing ellipse fitting based on the matching interval to respectively obtain two ellipses; Taking the characteristic points where the direction of a curve segment corresponding to a lane object changes as sampling points, and constructing a matching vector with the center of the fitted ellipse and the sampling points; Determine a matching point on the curve segment corresponding to the other car line object by fitting the center of the ellipse and the matching vector to the curve segment corresponding to the other car line object; The sampling point and the matching point are used as alignment sampling points of the lane object.

4. The method according to claim 3, characterized in that The matching intervals for determining the curve portion of the lane object include: Based on the extreme value points of the curvature change of the curve portion, the curve portion is segmented to obtain curve segments; Constructing a curve segment feature matrix based on feature points on the curve segment including feature points with changed directions; Matching is performed based on the curve segment feature matrix, and the matching interval of the curve portion of the lane object is determined using the curvature parameters and distance parameters of the two groups of feature points.

5. The method according to claim 4, characterized in that After determining the matching interval of the curve part of the car line object, it also includes: Extending the curve segment corresponding to the matching interval equidistantly to both ends, and weighted summing the matching results by the sum of the squares of the curvature differences of the feature points on the two groups of extended curve segments and the distances between the two groups of feature points; The weighted summation result of the extended curve segment is compared with the weighted summation result of the curve segment corresponding to the current matching interval, and the matching interval is expanded based on the comparison result.

6. The method according to claim 4, characterized in that Before segmenting the curve part, the method further includes: intercepting the curve part in the car line object based on the curvature of the feature points included in the car line object.

7. The method according to claim 2, characterized in that The straight line sampling of the lane object comprises: Use one of the matched vehicle line objects as the sampling line and the other as the matching line; Arranging sampling points at the beginning and end points of the sampling line, and inserting sampling points on the sampling line based on a preset distance threshold; The sampling points on the sampling line are projected onto the matching line respectively; and it is determined whether the angle between the connecting line of the sampling point and the projection point and the matching line is within a preset angle range; If so, the sampling point and the corresponding projection point are used as the alignment sampling points of the matched lane object.

8. The method according to claim 2, characterized in that: The straight line sampling of the lane object comprises: Based on the projections of the first and last endpoints of the plurality of lane objects on the reference line included in the group data, intercepting a target reference line segment covering all the lane objects on the reference line; Using the target reference line segment as a sampling line and arranging sampling points; Projecting the sampling points onto the plurality of lane objects respectively; Based on the angle between the line connecting the sampling point and the projection point and the car line object, and the number of projection points corresponding to the same sampling point, the aligned sampling points of the car line object are screened out from the projection points corresponding to the sampling point.

9. The method according to any one of claims 2 to 8, characterized in that: The method for determining whether the matched car line object has a curve part is as follows: Expanding a preset radius range outwards based on the axis of the car line object to obtain a first area; Connecting the first and last points of the car line object to obtain the first and last lines of the car line object; Expanding a preset radius range outward based on the head and tail lines to obtain a second area; Based on the first area and the second area, an intersection area is determined, so as to determine whether there is a curve portion in the lane object based on the intersection area and a preset ratio threshold.

10. The method according to claim 1, characterized in that Sampling the semantic object based on the semantic object set includes: Traversing each group of semantic objects that match each other in the semantic object set, and determining whether a ratio of similar areas of the matched semantic objects is greater than a first area ratio threshold; If it is less than the first area ratio threshold, traversing other groups of semantic objects that match each other in the semantic object set; If it is greater than the first area ratio threshold, determining whether the ratio of similar areas of the matched semantic objects is greater than a second area ratio threshold; wherein the first area ratio threshold is less than the second area ratio threshold; If it is greater than the second area ratio threshold, the centroid points of the semantic objects are respectively used as alignment sampling points; If it is less than the second area ratio threshold, determining whether the outer boundary of the semantic object is a rectangle; If it is a rectangle, traverse the other groups of semantic objects that match each other in the semantic object set; If it is not a rectangle, a local semantic object corresponding to the highest similarity region is determined in the matched semantic objects, and the centroid point of the local semantic object is used as the alignment sampling point.

11. The method according to claim 10, characterized in that The highest similarity regions are determined to include: Determine the respective graphic bounding boxes based on the matched semantic objects, and determine a target graphic bounding box covering the two graphic bounding boxes based on the length and width of the respective graphic bounding boxes; Constructing a granularity rectangle based on the outer bounding box of the target graphic, and constructing a first semantic object feature matrix based on a first semantic object in the matched semantic objects and the granularity rectangle; wherein the matched semantic objects include a first semantic object and a second semantic object, and an area of ​​the first semantic object is greater than an area of ​​the second semantic object; Based on the second semantic object and the granularity rectangle, determining a second semantic object feature matrix of a sliding search window and the second semantic object; Sliding the sliding search window on the outer border of the target graphic to determine the similarity between the first semantic object feature matrix and the second semantic object feature matrix after each sliding; The highest similarity value is determined, and the region with the highest similarity value is taken as the highest similarity region.

12. The method according to claim 11, characterized in that The highest similarity value is compared with a preset highest similarity threshold, and if the highest similarity value is greater than the preset highest similarity threshold, the centroid point of the local semantic object corresponding to the highest similarity region is used as the alignment sampling point.

13. An object sampling device in multi-pass data, characterized in that: include: A construction module, configured to construct a set of vehicle line objects and a set of semantic objects of the group data based on a plurality of segment data included in the group data divided from the plurality of trips of data, by combining the vehicle line objects with matching relationships and the semantic objects with matching relationships included in the segment data in pairs, respectively; A sampling module is used to sample the car-line objects and the semantic objects respectively based on the car-line object set and the semantic object set, so as to determine the object alignment error based on the sampling result.

14. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the object sampling method in multi-pass data according to any one of claims 1 to 12 is implemented.

15. A computer device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the program, the object sampling method in multi-pass data according to any one of claims 1 to 12 is implemented.

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