CN111596309B - Vehicle queuing measurement method based on laser radar - Google Patents
Vehicle queuing measurement method based on laser radar Download PDFInfo
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- CN111596309B CN111596309B CN202010299678.XA CN202010299678A CN111596309B CN 111596309 B CN111596309 B CN 111596309B CN 202010299678 A CN202010299678 A CN 202010299678A CN 111596309 B CN111596309 B CN 111596309B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/065—Traffic control systems for road vehicles by counting the vehicles in a section of the road or in a parking area, i.e. comparing incoming count with outgoing count
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
- Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
- Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation
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Abstract
The invention relates to the technical field of laser radar measurement, in particular to a vehicle queuing measurement method based on a laser radar. The invention has the beneficial effects that: the method can rapidly filter the ground point cloud, eliminate uncorrelated noise interference, and has the advantages of high reliability, high precision, wide application range, accurate vehicle queuing length calculation and small error.
Description
Technical Field
The invention relates to the technical field of laser radar measurement, in particular to a vehicle queuing measurement method based on a laser radar.
Background
Lidar is a generic term for laser active detection sensor devices. For the measurement imaging laser radar, the main working principle is to realize three-dimensional scanning measurement (imaging) of the target contour through high-frequency ranging and scanning angle measurement. The measuring imaging laser radar mainly comprises two major types of measuring LiDAR and navigation LiDAR. The measuring LiDAR is mainly used for high-precision mapping; the navigation LiDAR is mainly used for navigation obstacle avoidance of intelligent vehicles, robots and flying equipment. The measurement principle and the technical basis of the two products are similar, and the main difference is the difference of the operation mode, the detection distance and the measurement precision. The navigation LiDAR product trades for reduced equipment size and weight and increased laser scan line count at the expense of detection distance, ranging/angular measurement accuracy, and detection of imaging gray scale. In addition, the navigation LiDAR realizes the measurement of the three-dimensional contour of the target through a plurality of scanning lines. Compared with auxiliary driving sensors such as cameras, ultrasonic radars, millimeter wave radars and the like, the laser radars are 'eyes of intelligent vehicles' truly provided with space three-dimensional resolution capability.
The traditional laser radar filtering ground algorithm mainly adopts a PCL-based RANSAC algorithm, and the RANSAC is an abbreviation of 'Random Sample Consensus (random sampling consensus'). It can estimate the parameters of the mathematical model in an iterative manner from a set of observation data sets containing "outliers". It is an uncertain algorithm-it has a certain probability to get a reasonable result; the number of iterations must be increased in order to increase the probability.
The basic assumption of RANSAC is:
(1) The data consists of "intra-office points", such as: the distribution of the data may be interpreted with some model parameters;
(2) An "outlier" is data that cannot fit the model;
(3) The data in addition belongs to noise.
The reasons for the generation of the off-site points are: extreme values of noise; an erroneous measurement method; false assumptions about data. RANSAC also makes the following assumptions: given a set of (usually small) intra-office points, there is a process by which model parameters can be estimated; and the model can be interpreted or applied to the local points. A certain distance threshold is set through a RANSAC filtering algorithm of PCL, no matter the threshold is too large or too small, if the threshold is too small, the ground cannot be well filtered, and if the threshold is too large, the point clouds of other objects can be filtered out.
Disclosure of Invention
The invention aims to overcome the problems in the prior art and provides a vehicle queuing measurement method based on a laser radar, which can solve the problems in the prior art to at least a certain extent.
In order to achieve the technical purpose and the technical effect, the invention is realized by the following technical scheme:
a lidar-based vehicle queuing measurement method, comprising:
1) Filtering a point cloud near the ground, comprising the steps of:
filtering the ground point cloud by utilizing a RANSAC algorithm and laser radar height included angle information in the process of filtering the ground by utilizing a multithread laser radar, and then obtaining point cloud information of an upper layer of a road;
2) The vehicle length is calculated based on the K-means algorithm, and the method comprises the following steps of:
2.1 Selecting classes/groups and randomly initializing their respective center points;
2.2 Calculating the distance from each data point to the center point, and dividing the data points into which type from which center point the data points are nearest;
2.3 Calculating the center point in each class as a new center point;
2.4 Repeating the steps until each class of center does not change much after each iteration;
3) And determining the position of the automobile based on a clustering algorithm and the reflection intensity of the laser radar, wherein the data point cloud format output by the multi-thread laser radar is (x, y, r), x and y are coordinate values of scanning points, and r is the reflection intensity.
As a further optimization of the solution described above, in the step 1), the point cloud is filtered by the linear distance of each thread in the multi-threaded lidar to the ground.
As a further optimization of the above technical solution, in the step 3), the reflection intensity of the multi-thread laser radar is (0, 255).
The invention has the beneficial effects that: the method can rapidly filter the ground point cloud, eliminate uncorrelated noise interference, and has the advantages of high reliability, high precision, wide application range, accurate vehicle queuing length calculation and small error.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a diagram showing steps for calculating a vehicle length based on a K-means algorithm in the present invention;
FIG. 2 is an illustration of the step of determining the position of an automobile based on a clustering algorithm and laser radar reflection intensity in the present invention.
Description of the embodiments
In order to make the technical means, the creation features, the achievement of the purposes and the effects of the present invention easy to understand, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention, and it is obvious that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
A lidar-based vehicle queuing measurement method, comprising:
1) Filtering a point cloud near the ground, comprising the steps of:
the method comprises the steps that in the process of filtering the ground by utilizing a multithread laser radar, a RANSAC algorithm is used for filtering the ground point cloud together with the laser radar height included angle information, then point cloud information of the upper layer of the road is obtained, the straight line distance between each thread and the ground is known, and the point cloud close to the ground can be filtered according to the information;
referring to fig. 1 and 2), calculating a vehicle length based on a K-means algorithm, mainly by performing an algorithm operation on a point cloud remaining after removing the ground, including the steps of:
2.1 First we select some classes/groups and randomly initialize their respective center points. The center point is the same location as the vector length of each data point. This requires that we predict the number of classes (i.e. the number of center points) in advance;
2.2 Calculating the distance from each data point to the center point, and dividing the data points into which type from which center point the data points are nearest;
2.3 Calculating the center point in each class as a new center point;
2.4 Repeating the above steps until each class center does not change much after each iteration. The center point can be initialized randomly for a plurality of times, and then one with the best operation result is selected;
referring to fig. 2 and 3), the position of the automobile is determined based on a clustering algorithm and the reflection intensity of the laser radar, the data point cloud format output by the multi-thread laser radar is (x, y, r), x and y are coordinate values of scanning points, r is reflection intensity, the reflection intensity of the multi-thread laser radar is (0, 255) generally, the reflection intensity of the object which is more bright and opaque is stronger, so that the reflection intensity of the automobile is far greater than that of other objects such as roads and green belts, the point cloud swept to the automobile by the laser radar is usually greater than 200, the automobile can be well identified according to the information and the clustering algorithm, and the queuing information of the automobile can be measured.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The preferred embodiments of the invention disclosed above are intended only to assist in the explanation of the invention. The preferred embodiments are not exhaustive or to limit the invention to the precise form disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand and utilize the invention. The invention is limited only by the claims and the full scope and equivalents thereof.
Claims (3)
1. A laser radar-based vehicle queuing measurement method, comprising:
1) Filtering a point cloud near the ground, comprising the steps of:
the multi-thread laser radar receives the ground point cloud information and the laser radar height included angle information, filters the point cloud information by utilizing a RANSAC algorithm, and then acquires the point cloud information of the upper layer of the road;
2) The vehicle length is calculated based on the K-means algorithm, and the method comprises the following steps of:
2.1 Selecting classes/groups in which a value is randomly determined as the center point of such group or groups within the data taken in such group or groups;
2.2 Calculating the distance from each data point of the class/group to the center point, and dividing the data point into the class as to which center point is nearest to;
2.3 Taking the calculated new central point of each class as the central point of the next iterative calculation;
2.4 Repeating the steps until the central point of each class does not change more than a certain range after each iteration;
3) And determining the position of the automobile based on a clustering algorithm and the reflection intensity of the laser radar, wherein the data point cloud format output by the multi-thread laser radar is (x, y, r), x and y are coordinate values of scanning points, and r is the reflection intensity.
2. The lidar-based vehicle queuing measurement method of claim 1, wherein in step 1), the point cloud is filtered by the linear distance of each thread in the multi-threaded lidar to the ground.
3. The method for lidar-based vehicle queuing measurement according to claim 1, wherein in step 3), the reflection intensity of the multi-threaded lidar takes a value of (0,255).
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