CN109752701A - A kind of road edge detection method based on laser point cloud - Google Patents
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Abstract
The road edge detection method based on laser point cloud that the invention discloses a kind of, for obtaining the curb information of road, to provide related traffic information for automatic Pilot decision.Include: to be pre-processed to laser point cloud data, the noise spot on road surface is higher than in removal point cloud；Using the angle feature between laser point cloud data, single frames point cloud data or so curb point is obtained；It is excessively sparse for single frames curb point, it is merged using multiframe curb point；Curb point on laser radar coordinate system is transformed under vehicle axis system；It carries out curve fitting to qualified curb point, to obtain based on the curb detection line under vehicle axis system.The present invention can quickly detect curb information using velodyne16 line laser radar on real road, and this method is not influenced by Changes in weather, illumination variation etc., and robustness and accuracy are good.
Description
Technical Field
The invention relates to the technical field of sensor perception, in particular to a road edge detection method based on laser point cloud.
Background
In recent years, with the great increase of the number of automobiles, many cities face increasingly serious road traffic problems, and unmanned driving receives more and more attention. The unmanned vehicle mainly comprises three parts of environment perception, path planning and decision control, wherein the environment perception is the basis of the path planning and the decision control, and the road environment detection is an important link in an environment perception system, so the road environment detection plays a crucial role in the overall performance of the unmanned vehicle. Generally, a sensor with an environment sensing function, such as a camera, a radar, a GPS and the like, can be carried and installed on the unmanned vehicle. The laser radar is not influenced by factors such as weather and illumination, the scanning frequency is high during measurement, the data volume is rich, and the returned scanning data is convenient to process in real time. Road edge detection can divide a road in front of a vehicle into a drivable area and a non-drivable area so as to assist an automatic driving system in path planning.
The existing road edge detection technology at present mainly has two types: firstly, a camera is adopted to process acquired images, but the images are easily limited by multiple factors such as environment, weather and illumination, so that the requirements of the unmanned vehicle on the real-time performance cannot be met, and the processing effect is unstable; secondly, by adopting the Hough transform method, the Hough transform is used for detecting points on an approximate straight line, however, the points scanned on the obstacle on the laser radar data are very concentrated, and the points scanned on the road edge are very sparse, so the probability of detecting the road edge by the Hough transform is very small.
Disclosure of Invention
The invention aims to solve the problems that the existing road edge detection method is easily influenced by weather and illumination and road edge points are sparse, and provides a road edge detection method based on multi-frame laser point cloud fusion.
In order to achieve the technical purpose, the invention adopts the technical scheme that the road edge detection method based on the laser point cloud comprises the following steps:
firstly, preprocessing laser point cloud data, and removing noise points higher than a road surface in the point cloud;
acquiring left and right road edge points of single-frame point cloud data by utilizing the characteristic of an included angle between the laser point cloud data;
step three, aiming at the fact that single-frame road edge points are too sparse, multiple frames of road edge points are adopted for fusion;
step four, converting the road edge points on the laser radar coordinate system to a vehicle coordinate system;
and step five, performing curve fitting on the road edge points meeting the conditions to obtain a road edge detection line based on a vehicle coordinate system.
The road edge detection method based on the laser point cloud comprises the following steps:
the method comprises the steps of filtering scanned laser point cloud data according to the spatial position of the scanned laser point cloud data, removing point cloud data higher than a set threshold (determined by the height of a laser radar from the ground, generally set to be about 0.3 m higher than the road surface, namely the value of the position of the laser radar in a laser radar coordinate system 0.3 m higher than the road surface, for example, when the origin of the laser radar is 1 m higher than the ground, the laser radar selects-0.7 as the set threshold), setting a rectangular area (the rectangular area only needs to be in a section of range in front of a vehicle, the reference range is 20 m in front of the vehicle, and the reference range is 10 m in front of the vehicle and the left and right sides of the vehicle), and.
The second step of the road edge detection method based on the laser point cloud comprises the following steps:
for single frame laser point cloud data, firstly traversing each circle of point cloud in sequence, searching a central point each time, wherein the central point is the intersection point of a straight line of a vehicle central line under a laser radar coordinate system and each circle of point cloud, searching a left road edge point along the central point leftwards, selecting three points in sequence, wherein the interval of each point is step _ size, connecting lines by taking a second point as a center, calculating an angle value corresponding to the vertex, if the cosine value of the angle value is greater than a set threshold value (generally-0.85), marking as the left road edge point, otherwise not being the road edge point, searching a right road edge point along the central point rightwards, and marking.
The road edge detection method based on the laser point cloud comprises the following steps:
step 1, feature point extraction:
firstly, traversing all points in the laser point cloud, when the square of the distance between a certain point and the subsequent point is greater than a threshold value sigma and the included angle of two vectors is less than a threshold value c (occlusion may exist if the included angle is small), setting adjacent points (for example, 6 points can be taken) on one side of the certain point as points which cannot be marked as feature points, if the distance from the certain point to the two points before and after the certain point is greater than α times the depth of the point, judging the certain point as the point which cannot be marked as the feature point, dividing the points into different categories according to the curvature c of the point, wherein the point with the curvature greater than the threshold value is an edge point, and the point with the curvature less than the threshold value is a plane point:
wherein, S is a set of continuous points around, and is the coordinate of the ith point scanned at the kth time under the laser coordinate system; | S | is the number of points around S, and is generally 10.
By comparing the curvatures, edge points with a large curvature and plane points with a small curvature are selected. In order to prevent feature point aggregation, the point cloud of each scan is divided into four parts, and two points with the largest curvature are selected from each part as edge points and four points with the smallest curvature are selected as plane points.
Step 2, finding the correspondence of the feature points:
and performing registration by using the point cloud data of two adjacent frames to complete the association of the point cloud data at the time t and the time t + 1. And (j, l) is called as the correspondence of the point i in the point cloud at the time t, and a straight line closest to the edge point at the current time is determined as the registration correspondence of the point. And similarly, firstly finding the closest point j, finding l around j, finding m around j, and referring to (j, l, m) as the correspondence of the point i in the point cloud at the time t, and determining a plane closest to the plane point at the current time as the registration correspondence of the plane point. Next, the distances from the edge points to the line and the plane points to the plane are required, the whole registration process is changed into an optimization process, the iteration is continuously carried out to ensure that the sum of the distances is minimum, and the distance from the point to the line segment is calculated, wherein the formula is as follows:
the point-to-plane distance is calculated as follows:
step 3, motion estimation
And calculating the sum of the distances from all corresponding points to the straight line to the surface to be the shortest, and then carrying out iterative calculation according to a Levenberg-Marquardt algorithm to obtain the transformation between the two frames.
The road edge detection method based on the laser point cloud comprises the following four steps:
step 1, calibrating the laser radar to obtain the distances (delta x, delta y and delta z) from the coordinate origin of the laser radar to the coordinate origin of a vehicle body coordinate system in all directions, the horizontal deflection angle and the laser placement height.
Step 2, when the laser radar inclines downwards, the laser radar coordinate system X which deflects vertically is used for determining the position of the laser radar coordinate system XrYrZrConversion to horizontal lidar coordinate system XlYlZlThe coordinate transformation formula is:
β is the angle of downward inclination of the lidar, after the transformation, the lidar coordinate system remains horizontal.
And 3, converting the horizontal laser radar coordinate system into a vehicle body coordinate system (rotation conversion and translation conversion). The coordinate conversion formula of the step is as follows:
and Δ x, Δ y and Δ z are offset of the laser radar relative to the origin of the coordinate system of the vehicle body. Theta is the horizontal lidar coordinate system YlThe angle between the axis and the Y-axis of the vehicle coordinate system.
The road edge detection method based on the laser point cloud comprises the following fifth step:
obtaining multi-frame road edge points of vehicle coordinates according to the four-coordinate conversion in the step, and adopting a least square methodFitting left and right road edges respectively, and using cubic curve function y ═ a0+a1x+a2x2+a3x3Describing, the road edge points are fitted by ensuring that the square sum of the road edge points to the curve distance is minimum to obtain the optimal curve.
The invention has the beneficial effects that:
(1) the method has the advantages that the laser radar is used for data acquisition, the image is prevented from being easily affected by multiple factors such as environment, weather and illumination, the road edge points can be quickly and accurately extracted from the laser radar data, and the left and right road edge points of the single-frame laser data are obtained through the characteristic that whether the included angle of the continuous three-point cloud points is larger than a set angle, so that effective road edge points are provided for curve fitting, and the robustness of road edge detection is improved;
(2) the method and the device fuse multi-frame laser point cloud data, solve the problem that points scanned to a road edge by a single frame are very sparse, provide a reasonable number of road edge points for curve fitting, and increase the accuracy of road edge detection.
Drawings
FIG. 1 is a block diagram of a road edge detection system based on laser point cloud;
FIG. 2 is a horizontal spacing of the laser radar scan area from the laser beam;
FIG. 3 shows left and right road edge points marked by laser point cloud data;
fig. 4 is a vehicle coordinate system and a horizontal lidar coordinate system.
Detailed Description
Embodiments of the present invention will be further described with reference to the accompanying drawings.
The invention provides a road edge detection method based on multi-frame fusion of laser point clouds, a block diagram of the whole system is shown as an attached figure 1, and the method comprises the following steps:
s1, preprocessing laser point cloud data: and removing noise points higher than the road surface from the scanned laser point cloud data.
According to the invention, the point cloud data higher than the set threshold value is removed by setting the height threshold value and the rectangular area, and the point cloud data in the specified range is reserved. For each frame of point cloud data, it is typically represented as an array of at least three columns and N rows, each row corresponding to a single point and having its spatial location point (X, Y, Z), with the lidar scanning area horizontally spaced from the laser beam as shown in fig. 2.
S2, extracting left and right road edge points of single-frame point cloud data: and extracting right and left road edge points which meet the conditions by using the characteristics of the included angle between the laser point cloud data.
For a single frame of laser point cloud data, the left and right road edge points extracted are shown in fig. 3. Firstly, traversing each circle of point cloud in sequence, searching a central point each time, wherein the central point is the intersection point of a straight line of a vehicle central line under a laser radar coordinate system and each circle of point cloud, searching a left road edge point along the central point leftwards, selecting three points in sequence, wherein the interval of each point is step _ size, connecting lines by taking a second point as a center, calculating an angle value corresponding to the vertex, if the cosine value of the angle value is greater than a set threshold value, marking the point as the left road edge point, otherwise, not the road edge point, searching the right road edge point along the central point rightwards, and marking.
S3, multi-frame point cloud data road edge point fusion: and (3) carrying out registration of feature points (mainly edge/surface features) on the two frames of point cloud data before and after, and estimating the motion attitude of laser motion, namely, rotational translation transformation, so as to transfer the two frames of point cloud data to the same coordinate system.
(1) Extracting characteristic points:
firstly, traversing all points in the laser point cloud, and when the square of the distance between a certain point and the subsequent point is larger than a threshold value sigma and the distance is bidirectionalIncluded angle of magnitude less than thresholdIf the distance from a certain point to the front point and the rear point of the certain point is greater than α times of the depth of the point, the point is judged as the point which cannot mark the characteristic point, the points are divided into different categories according to the curvature c of the point, the point with the curvature greater than the threshold value is an edge point, the point with the curvature less than the threshold value is a plane point, and the formula is as follows:
wherein S is a set of continuous points around the S,is the coordinate of the ith point of the kth scanning under the laser coordinate system.
By comparing the curvatures, edge points with a large curvature and plane points with a small curvature are selected. In order to prevent feature point aggregation, the point cloud of each scan is divided into four parts, and two points with the largest curvature are selected from each part as edge points and four points with the smallest curvature are selected as plane points.
(2) Finding correspondences of feature points:
and performing registration by using the point cloud data of two adjacent frames to complete the association of the point cloud data at the time t and the time t + 1. And (j, l) is called as the correspondence of the point i in the point cloud at the time t, and a straight line closest to the edge point at the current time is determined as the registration correspondence of the point. And similarly, firstly finding the closest point j, finding l around j, finding m around j, and referring to (j, l, m) as the correspondence of the point i in the point cloud at the time t, and determining a plane closest to the plane point at the current time as the registration correspondence of the plane point. Next, the distances from the edge points to the line and the plane points to the plane are required, the whole registration process is changed into an optimization process, the iteration is continuously carried out to ensure that the sum of the distances is minimum, and the distance from the point to the line segment is calculated, wherein the formula is as follows:
the point-to-plane distance is calculated as follows:
(3) motion estimation
And calculating the sum of the distances from all corresponding points to the straight line to the surface to be the shortest, and then carrying out iterative calculation according to a Levenberg-Marquardt algorithm to obtain the transformation between the two frames.
S4, coordinate conversion: converting road edge points on a laser radar coordinate system into a vehicle coordinate system
Measuring distances (delta x, delta y and delta z) from a laser radar coordinate origin to the coordinate origin of a vehicle body coordinate system in all directions by a measuring tape, wherein the three parameters are three parameter values required by translation transformation, measuring a horizontal deflection angle by a protractor, calculating a vertical deflection angle by laser height and a distance from a point where the last line of laser is swept on the ground to the laser origin, and then carrying out coordinate transformation.
(1) And calibrating the laser radar to obtain the distances (delta x, delta y and delta z) from the coordinate origin of the laser radar to the coordinate origin of the vehicle body coordinate system in all directions, the horizontal deflection angle and the laser placement height.
(2) The vertical deflection of the lidar coordinate system X when the lidar is tilted downwardrYrZrConversion to horizontal lidar coordinate system XlYlZlThe coordinate transformation formula is:
β is the angle of downward inclination of the lidar, after the transformation, the lidar coordinate system remains horizontal.
(3) Transforming the horizontal laser radar coordinate system to the vehicle body coordinate system (rotation transformation + translation transformation): the coordinate conversion formula of the step is as follows:
and Δ x, Δ y and Δ z are offset of the laser radar relative to the origin of the coordinate system of the vehicle body. Theta is the horizontal lidar coordinate system YlThe angle between the axis and the Y-axis of the vehicle coordinate system.
S5, curve fitting: and performing curve fitting on the road edge points meeting the conditions to obtain a road edge detection line based on a vehicle coordinate system.
Obtaining multi-frame road edge points of the vehicle coordinates according to the four-coordinate conversion, respectively fitting the left road edge and the right road edge by adopting a least square method, and using a cubic curve function y as a0+a1x+a2x2+a3x3Describing, the optimal curve is obtained by ensuring the least square sum of the distances from the road edge points to the curve, and the main steps are as follows:
(1) the sum of the distances from all the road edge points to the curve is obtained, namely the square sum of the deviations is as follows:
(2) to find the qualified a value, the right side a of equation (6) is comparedjPartial derivatives are calculated, so we have:
(3) the following equation (8) can then be obtained by simplifying equation (7) to the left:
(4) by expressing equation (8) in the form of a matrix, the following matrix (9) can be obtained:
by solving the coefficient matrix, a fitting curve can be obtained, so that the road edge points are fitted.
Claims (8)
1. A road edge detection method based on laser point cloud is characterized by comprising the following steps:
firstly, preprocessing laser point cloud data, and removing noise points higher than a road surface in the laser point cloud data;
step two, acquiring left and right road edge points of single-frame point cloud data by utilizing the characteristic of an included angle between laser point cloud data after noise points are removed;
step three, aiming at sparseness of single-frame road edge points, fusing multiple frames of road edge points to obtain a certain number of road edge points;
step four, converting the road edge points obtained by the fusion of the frames in the step three into a vehicle coordinate system to obtain road edge points based on the vehicle body;
and fifthly, performing curve fitting on the road edge points based on the vehicle body to obtain a road edge detection line based on a vehicle coordinate system.
2. The method for detecting the road edge based on the laser point cloud of claim 1, wherein the step one specific implementation process comprises the following steps:
and filtering the laser point cloud data obtained by scanning according to the spatial position of the laser point cloud data, eliminating the point cloud data higher than a set threshold value, setting a rectangular area in front of the vehicle, and retaining the laser point cloud data in a specified range.
3. The method for detecting the road edge based on the laser point cloud as claimed in claim 1, wherein the second step comprises the following concrete implementation processes:
for single frame laser point cloud data, firstly traversing each circle of laser point cloud data in sequence, searching a central point each time, wherein the central point is an intersection point of a straight line of a vehicle central line under a laser radar coordinate system and each circle of laser point cloud data, selecting three points along the central point leftwards in sequence, the interval of each point is step _ size, taking a second point as a central connecting line, calculating an angle value corresponding to the second point, if the cosine value of the angle value is greater than a set threshold value, marking the point as a left road edge point, otherwise, not a road edge point, searching a right road edge point along the central point rightwards in a similar manner, and marking the right road edge point.
4. The method for detecting the road edge based on the laser point cloud as claimed in claim 1, wherein the concrete implementation process of the third step comprises:
1) traversing all points in the laser point cloud data, and when the square of the distance between a certain point and the subsequent point is greater than a threshold value sigma, and the included angle of two vectors formed by the certain point, the subsequent point and the original point is less than the threshold valueIf the distance between a certain point and the two points in front of and behind the certain point is larger than α times the depth of the point, the point is judged as the point which can not mark the characteristic point, the points are divided into different categories according to the curvature c of the point, the point with the curvature larger than the threshold value is an edge point, the point with the curvature smaller than the threshold value is a plane point, and the formula is as follows:
wherein S is a set of continuous points around the S; the | S | is the number of S containing points;the coordinate of the ith point scanned for the kth time under the laser radar coordinate system;the coordinate of the jth point scanned for the kth time under the laser radar coordinate system; the | S | is the number of points around S;
2) registering by using the laser point cloud data of two adjacent frames to complete the association of the laser point cloud data at the time t and the time t + 1; finding a closest point j in the laser point cloud data of the point i at the time t by using a KD tree, finding a next closest point l around the j, referring the (j, l) as the correspondence of the point i in the point cloud at the time t, and determining a straight line closest to the edge point at the current time as the registration correspondence of the point; similarly, firstly finding the closest point j, finding l around j, finding m around j, and referring the (j, l, m) as the corresponding of the point i in the point cloud at the time t, and determining a plane closest to the plane point at the current time as the registration corresponding of the plane point; calculating the distance between the edge point and the line and the distance between the plane point and the plane, changing the whole registration process into an optimization process, continuously iterating to ensure that the sum of the distances is minimum, wherein the calculation formula of the distance between the edge point and the line is as follows:
wherein ,respectively representing i, j, l point three-dimensional coordinates scanned under a laser radar coordinate system;
the plane point-to-plane distance calculation formula is as follows:
wherein ,the three-dimensional coordinates of m points scanned under a laser radar coordinate system are obtained;
3) and constructing a constraint equation by minimizing the distance from all edge points of the current frame to the nearest edge of the previous frame and the distance from all plane points of the current frame to the nearest plane of the previous frame, and calculating according to a Levenberg-Marquardt method to obtain the transformation between the two frames.
5. The method of claim 4, wherein the laser point cloud data of each scan is divided into four parts, and two points with the largest curvature are selected from each part as edge points and four points with the smallest curvature are selected as plane points.
6. The method for detecting the road edge based on the laser point cloud as claimed in claim 1, wherein the step four is realized by the following steps:
step 1, calibrating a laser radar to obtain the distance (delta x, delta y, delta z) from the coordinate origin of the laser radar to the coordinate origin of a vehicle body coordinate system in each direction, a horizontal deflection angle and the height for placing the laser radar;
step 2, when the laser radar is inclined downwards, the laser radar which is deflected vertically is inclinedUp to coordinate system XrYrZrConversion to horizontal lidar coordinate system XlYlZlThe coordinate transformation formula is:
wherein ,xr,yr,zrβ is the angle of downward tilt of the lidar for the spatial position of each laser point;
and 3, transforming the horizontal laser radar coordinate system to a vehicle body coordinate system:
wherein, the delta x, the delta y and the delta z are offset of the laser radar relative to the origin of the coordinate system of the vehicle body; theta is the horizontal lidar coordinate system YlThe angle between the axis and the Y-axis of the vehicle coordinate system.
7. The method for detecting the road edge based on the laser point cloud as claimed in claim 1, wherein the step five is realized by the following steps: and fourthly, obtaining multi-frame road edge points of the vehicle coordinates, respectively fitting a left road edge and a right road edge by adopting a least square method, describing the left road edge and the right road edge by using a cubic curve function, ensuring that the square sum of the distances from the road edge points to the curve is minimum, obtaining an optimal curve, and fitting the road edge points.
8. The method of claim 7, wherein the road edge point is fitted by solving the following coefficient matrix to obtain an optimal curve:
wherein n is the number of the road edge points, xi、yiThe values of the road edge point i in the x-axis and y-axis directions respectively，a0、a1、a2、a3Are coefficients of a cubic curve function.
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