CN114877838B - Road geometric feature detection method based on vehicle-mounted laser scanning system - Google Patents
Road geometric feature detection method based on vehicle-mounted laser scanning system Download PDFInfo
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Abstract
The invention discloses a road geometric feature detection method based on a vehicle-mounted laser scanning system, which comprises the following steps of: selecting and coupling inertial navigation and GNSS satellite positioning data to obtain high-precision pose data, and fusing laser radar point cloud data to construct an outdoor scene map; extracting ground points based on the height histogram ordering and super-voxel cluster recognition; identifying road edge points based on the average elevation features, the smoothness features, and the adjacent distance features; after a RANSAC method is used for fitting a road boundary, a Kalman filter is input to obtain smooth road boundary information; dividing the empty point cloud object in the road area by using an ultra-voxel clustering method, and interpolating the empty point cloud object to a road plane to calculate the clearance; projecting longitude and latitude information of a vehicle track to an ink-card support coordinate system, fitting a road flat curve by using a quadratic function RANSAC method, and calculating a radius value; calculating a longitudinal slope value within a fixed travel distance interval by utilizing vehicle elevation information; and filtering and processing acceleration data based on inertial navigation equipment and obtaining the root mean square to represent the road flatness.
Description
Technical Field
The invention relates to the field of road rapid detection, in particular to a road geometric feature detection method based on a vehicle-mounted laser scanning system.
Background
In some specific occasions, such as a specific military transfer process, a small guide vehicle is needed to survey road conditions on a transfer route in real time, so as to provide route decision and auxiliary driving basis for the trafficability of large vehicles of a rear large army, and the following conditions are needed to be ensured for the road surfaces transported by the large vehicles such as military vehicles: 1. the road surface has no road surface damage (such as ruts, pits, bags, sinkers and protruding well covers) which obviously influence the running safety of the vehicle; 2. the pavement has better anti-skid capability; 3. the road cross section and the vertical upper part have no obstacle influencing the driving safety; 4. the road width and curve radius are suitable for military vehicles to pass through (e.g. flat curves and road width indexes).
There is a need in the art for a rapid detection means for rapidly detecting road surface breakage (more significant breakage), road surface construction depth, road lateral and longitudinal clear space (including road banner width), and road planform under normal vehicle speed conditions. At present, detection means aiming at road geometric features and technical conditions are scattered and complex, and comprehensive detection means with wide universality are lacked.
Disclosure of Invention
The invention mainly aims to solve the problems of insufficient road index detection comprehensiveness and slower speed in the prior art and provides a road geometric feature detection method based on a vehicle-mounted laser scanning system.
A road geometric feature detection method based on a vehicle-mounted laser scanning system comprises the following steps:
the first step: constructing an outdoor scene map;
and a second step of: acquiring ground points, road edge points and road boundary information, and calculating the clearance height;
and a third step of: fitting a road flat curve and calculating a radius value;
fourth step: calculating a longitudinal slope value within a fixed driving distance interval;
fifth step: and calculating the road flatness to obtain the geometric feature index of the detected road section.
Further, the first step is performed as follows:
(1) Installing a multi-line laser radar, inertial navigation equipment, GNSS satellite positioning equipment and differential equipment on the top of an autonomous vehicle, building a detection platform and calibrating;
(2) And obtaining high-precision pose data by using inertial navigation and GNSS satellite positioning data, and fusing laser radar point cloud data to construct an outdoor scene map.
Further, pose translation matrixes and rotation matrixes of the geometric center of the laser radar and the geometric center of the inertial navigation device are externally measured, so that calibration of the detection platform is realized.
Furthermore, the output frequencies of the multi-line laser radar and the inertial navigation device are set to be consistent, the same time stamp is used as a standard, an external calibration result is incorporated into calculation, and an inertial navigation device gesture matrix and a position matrix are matched for each frame of point cloud data.
Further, the second step is performed as follows:
(1) Extracting ground points based on the point cloud data by using the height histogram ordering and super voxel cluster recognition;
(2) Identifying road edge points based on the average elevation features, the smoothness features, and the adjacent distance features;
(3) After a RANSAC method is used for fitting a road boundary, a Kalman filter is input to obtain smooth road boundary information;
(4) And (5) segmenting the space point cloud object above the road area by using an ultra-voxel clustering method, interpolating the object to a road plane, and calculating the clearance height.
Further, in the step (1), the point cloud data is divided into columnar units of 0.5m by 0.2m, a frequency histogram along the elevation direction is established, the frequency is ordered, and the area with the highest frequency is the area to which the ground point belongs; and carrying out three-dimensional voxel segmentation on the point cloud data in the area of the ground point, and splicing the point cloud data to obtain a final road surface super-voxel clustering result to obtain complete road surface point cloud data.
Further, the step (2) is to detect the position where the average elevation feature, the smoothness feature and the adjacent distance feature of the road point change significantly as the road edge point.
Further, in the step (3), the RANSAC method is fitted to the road edge points to obtain a boundary curve, the boundary curve is smoothed through Kalman filtering, and the road width detection result is stabilized.
And (3) in the step (4), different objects are segmented and clustered in the area above the detected road section by using a super voxel clustering method, and the distance from the lowest point in the objects to the road plane is calculated to be the clearance height.
Further, the third step is performed as follows:
(1) Projecting longitude and latitude information of a vehicle track to a mercator plane coordinate system, fitting a road flat curve by using a quadratic function RANSAC method, and calculating a radius value of a road round curve to obtain a turning radius index;
(2) Calculating longitudinal slope values between starting points and finishing points at intervals of certain data points, and extracting road longitudinal section indexes in sections;
(3) And filtering the longitudinal acceleration data of the inertial navigation equipment, and solving the root mean square to represent the road flatness index.
The invention has the beneficial effects that: the invention relates to a road geometric feature and technical index condition rapid detection method based on a vehicle-mounted laser scanning system, which comprises the steps of constructing a vehicle-mounted laser detection platform, calibrating external parameters, constructing an outdoor scene map by fusion point cloud data and pose data, extracting road geometric information comprising road boundaries, clearance height, turning radius and longitudinal section gradient from the point cloud data and vehicle track points by using a quadratic function RANSAC method and road boundary feature definition, and obtaining flatness index of a root mean square representation road by using vehicle longitudinal acceleration to comprehensively obtain rapid detection results of the road geometric feature and the technical condition.
Drawings
Fig. 1 is a flow chart of a method for rapidly detecting road geometric features and technical condition indexes according to the present invention.
Detailed Description
The technical scheme of the invention is further described below with reference to specific embodiments and drawings.
As shown in fig. 1, a road geometric feature and technical index condition rapid detection method based on a vehicle-mounted laser scanning system comprises the following steps:
the first step: selecting multi-line laser radar, inertial navigation equipment, GNSS satellite positioning equipment and differential equipment, installing the multi-line laser radar, the inertial navigation equipment, the GNSS satellite positioning equipment and the differential equipment on an autonomous vehicle, constructing a detection platform and calibrating the detection platform; acquiring high-precision pose data by using inertial navigation and GNSS satellite positioning data, and fusing laser radar point cloud data to construct an outdoor scene map;
and a second step of: extracting ground points based on the point cloud data by using the height histogram ordering and super voxel cluster recognition; identifying road edge points based on the average elevation features, the smoothness features, and the adjacent distance features; after a RANSAC method is used for fitting a road boundary, a Kalman filter is input to obtain smooth road boundary information; and (5) segmenting the empty point cloud object above the road area by using a super voxel clustering method and interpolating the empty point cloud object to a road plane to calculate the clearance.
And a third step of: projecting longitude and latitude information of a vehicle track to an ink-card support coordinate system, fitting a road flat curve by using a quadratic function RANSAC method, and calculating a radius value; calculating a longitudinal slope value within a fixed travel distance interval by utilizing vehicle elevation information; filtering and processing acceleration data based on inertial navigation equipment and obtaining the root mean square to represent road flatness; through the steps, the geometric characteristic index and the technical condition index of the detected road section are obtained in a summarizing mode.
The example selects a mechanical 16-line laser radar, NPOS220S integrated navigation equipment and MD-649DTU differential equipment to be installed on an autonomous vehicle, a detection platform is built and calibrated, and a displacement matrix and a rotation matrix between phase centers of the two are measured externally.
And (3) acquiring INS (inertial navigation system) calculation data after integrated navigation and inertial navigation original output triaxial acceleration and attitude angle data by using an integrated navigation built-in tight coupling algorithm, converting an inertial navigation equipment coordinate system into a geocentric fixed coordinate system, matching a laser radar coordinate system into the same coordinate system through a displacement matrix and a rotation matrix, and fusing laser radar point cloud data to construct an outdoor scene map.
The step of identifying segmented ground points from the point cloud data includes,
(1) Determining the ground approximate height according to the mounting ground clearance of the laser radar, dividing the point cloud into 0.5m x 0.2m columnar units along the coordinate axis, establishing a frequency histogram along the Z (elevation) direction, and simultaneously establishing an empty cell array phi;
(2) The frequency ordering is carried out on the elevation in each unit, and the elevation range of the unit with the highest frequency is the approximate road surface position elevation;
(3) And distinguishing the pavement part from other discrete plane points by using a super-voxel clustering method, and obtaining complete pavement point cloud data.
The specific steps of extracting the road edge feature points from the ground points comprise:
(1) The average Gao Chenghui within the grid containing the road edges is higher than the average elevation of the road surface area,setting a proper threshold value to identify a grid where the road edge area is located; wherein z is i Represents the elevation of each point, μ represents the average elevation within the grid, n heigh Representing the number of data points in the grid, T heigh Then the selected threshold indicator;
(2) The road plane has obvious smoothness difference relative to the edge with elevation mutation, and the position with great smoothness coefficient change is searched for to find the characteristic point and smoothness indexWherein S is an arbitrary point p i Is a neighborhood of, p j Is the division of p in S i Points other than, |S| is the cardinality of the point set S, |P i And II is the sum of the distances between the points of the point set S.
(3) The laser points on the same scanning line are uniformly distributed on the road plane, and the distance between the points at the edge of the road Greater than a set threshold. Wherein H is s Is point p i Absolute value of height, θ l Is point p i Vertical angle of scan line, θ a Is the horizontal angular resolution of the lidar.
After the RANSAC method and Kalman filtering smoothing processing are used, road boundaries are fitted, error influence is eliminated, and a RANSAC model adopts a quadratic function model to fit boundary curves from edge feature point sets and calculate road widths.
The longitude and latitude coordinates of the vehicle track under the WGS-84 coordinate system output by the inertial navigation device are projected to the ink-card-bracket plane coordinate system, and two are utilizedFitting road flat curve by using subfunction RANSAC method and calculating radius value of road circular curve Obtaining a road turning radius index; wherein R represents the radius of the circular curve, K represents the curvature of the corresponding position of the circular curve, and y '/y' represents the first derivative and the second derivative of the quadratic function respectively.
Calculating longitudinal slope value between two points at 100 track points eachExtracting longitudinal slope indexes of a longitudinal section of a road in a segmented manner; wherein (x) i ,y i ,z i )，(x i+1 ,y i+1 ,z i+1 ) Respectively representing coordinates of two adjacent points in a space rectangular coordinate system.
Reading inertial navigation longitudinal acceleration data, inputting the data into a Kalman filter to obtain a filtered acceleration time domain diagram, and calculating the mean square error of road surface accelerationTo characterize road surface irregularities. Wherein S is as (f) A function representing the longitudinal acceleration profile over time, f representing a time variable.
Through the above processes, the geometric characteristic value and the technical condition index of the measured road section are obtained in a summarizing way.
It should be noted that the above embodiments are only for aiding in understanding the method of the present application and its core idea, and that it will be obvious to those skilled in the art that several improvements and modifications can be made to the present application without departing from the principle of the present application, and these improvements and modifications are also within the scope of the claims of the present application.
Claims (8)
1. The road geometric feature detection method based on the vehicle-mounted laser scanning system is characterized by comprising the following steps of:
the first step: constructing an outdoor scene map;
and a second step of: acquiring ground points, road edge points and road boundary information, and calculating the clearance height;
and a third step of: fitting a road flat curve and calculating a radius value;
fourth step: calculating a longitudinal slope value within a fixed driving distance interval;
fifth step: calculating the road flatness to obtain geometric feature indexes of the detected road section;
the second step is executed as follows:
(1) Extracting ground points based on the point cloud data by using the height histogram ordering and super voxel cluster recognition;
(2) Identifying road edge points based on the average elevation features, the smoothness features, and the adjacent distance features;
(3) After a RANSAC method is used for fitting a road boundary, a Kalman filter is input to obtain smooth road boundary information;
(4) Dividing the empty point cloud object on the road area by using an ultra-voxel clustering method, interpolating the empty point cloud object to a road plane, and calculating the clearance height;
in the step (1), the point cloud data are divided into columnar units of 0.5 x 0.2m, a frequency histogram along the elevation direction is established, the frequency is ordered, and the area with the highest frequency is the area to which the ground point belongs; and carrying out three-dimensional voxel segmentation on the point cloud data in the area of the ground point, and splicing the point cloud data to obtain a final road surface super-voxel clustering result to obtain complete road surface point cloud data.
2. The method for detecting road geometric features based on a vehicle-mounted laser scanning system according to claim 1, wherein the first step is performed as follows:
(1) Installing a multi-line laser radar, inertial navigation equipment, GNSS satellite positioning equipment and differential equipment on the top of an autonomous vehicle, building a detection platform and calibrating;
(2) And obtaining high-precision pose data by using inertial navigation and GNSS satellite positioning data, and fusing laser radar point cloud data to construct an outdoor scene map.
3. The road geometric feature detection method based on the vehicle-mounted laser scanning system according to claim 2, wherein the pose translation matrix and the rotation matrix of the geometric center of the external measurement laser radar and the geometric center of the inertial navigation device are used for achieving calibration of the detection platform.
4. The road geometric feature detection method based on the vehicle-mounted laser scanning system according to claim 2, wherein output frequencies of the multi-line laser radar and the inertial navigation device are set to be consistent, an external calibration result is taken into calculation by taking the same time stamp as a standard, and an inertial navigation device posture matrix and a position matrix are matched for each frame of point cloud data.
5. The method for detecting road geometric features based on a vehicle-mounted laser scanning system according to claim 1, wherein the position where the average elevation feature, the smoothness feature and the adjacent distance feature of the road point change significantly is detected as the road edge point in the step (2).
6. The method for detecting road geometric features based on a vehicle-mounted laser scanning system according to claim 1, wherein in the step (3), a RANSAC method is fitted to road edge points to obtain a boundary curve, and the boundary curve is smoothed by a kalman filter to stabilize a road width detection result.
7. The method for detecting the geometric features of the road based on the vehicle-mounted laser scanning system according to claim 1, wherein in the step (4), different objects are clustered by segmentation in an area above a detected road section by using a super voxel clustering method, and the distance from the lowest point in the objects to the road plane is calculated as the clearance height.
8. The road geometric feature detection method based on the vehicle-mounted laser scanning system according to claim 1, wherein the third step is performed as follows:
(1) Projecting longitude and latitude information of a vehicle track to a mercator plane coordinate system, fitting a road flat curve by using a quadratic function RANSAC method, and calculating a radius value of a road round curve to obtain a turning radius index;
(2) Calculating longitudinal slope values between starting points and finishing points at intervals of certain data points, and extracting road longitudinal section indexes in sections;
(3) And filtering the longitudinal acceleration data of the inertial navigation equipment, and solving the root mean square to represent the road flatness index.
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