CN114779273A - Method and device for detecting remaining cargo volume - Google Patents
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Abstract
Embodiments of the present disclosure disclose methods and apparatus for detecting a remaining cargo volume. The specific implementation mode of the method comprises the following steps: acquiring point cloud data in a storage box; selecting a reference point from the point cloud data, and executing the following point cloud triangularization steps: searching a predetermined number of candidate points closest to the reference point; projecting a predetermined number of candidate points on a projection plane of the reference point to obtain a projection point set; connecting the projection points in the projection point set to construct a non-overlapping triangle set; selecting points near the reference point from the points without the reference point as the reference point, and continuously executing the point cloud triangularization step; finally, for each triangle in the triangle set, constructing a tetrahedron according to the coordinates of the triangle before the three points are projected and the position of the laser radar, and calculating the volume of the tetrahedron; the volume of each tetrahedron is summed as the remaining cargo volume. This embodiment has realized that the quick, accurate storage tank volume of carrying cargo that detects.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of computers, in particular to a method and a device for detecting a remaining cargo volume.
Background
With the rapid development of science and technology, the industrial production process develops towards automation, intellectualization and rapidness. The port plays a very important role in connecting inland economy abdomens and promoting the development of marine transportation, and the intelligent degree of the port is directly related to the economic development of the whole region. The key of the automatic production between ports is to carry out high-precision detection on the bulk cargo loaded train carriage entering and exiting the ports. The laser radar detection technology is widely concerned as a high-precision detection means, a new idea is provided for obtaining surface information of a measured object to realize high-precision detection through development of the technology in the field of surveying and mapping measurement, surface form information of the measured object can be obtained through obtaining and processing point cloud data of the measured object, and then a series of operations such as corresponding geometric parameter extraction, volume operation processing and the like are completed according to actual engineering requirements.
Disclosure of Invention
Embodiments of the present disclosure provide methods and apparatus for detecting a remaining cargo volume.
In a first aspect, an embodiment of the present disclosure provides a method for detecting a remaining cargo volume, wherein a laser radar is installed at a tail portion of a storage box, and a point cloud inside the whole storage box is scanned completely towards the inside of the storage box, and the method includes: acquiring point cloud data in a storage box; selecting a reference point from the point cloud data, and executing the following point cloud triangularization steps: searching a predetermined number of candidate points closest to the reference point; projecting the candidate points with the preset number on a projection plane of the reference point to obtain a projection point set; connecting the projection points in the projection point set to construct a non-overlapping triangle set; selecting points near the reference point from points without reference points as the reference point, and repeatedly executing the point cloud triangularization step until all the points are traversed, wherein the projection direction is the direction in which the position of the laser radar points to the reference point, and the projection plane is a plane which passes through the reference point and is perpendicular to the projection direction; for each triangle in the triangle set, constructing a tetrahedron according to the coordinates of the triangle before the three points are projected and the position of the laser radar, and calculating the volume of the tetrahedron; the volume of each tetrahedron is summed as the remaining cargo volume.
In some embodiments, obtaining point cloud data within a storage bin comprises: acquiring initial point cloud data in a storage box; dividing grids for the initial point cloud data according to the angular resolution of the laser radar; calculating the coordinate of the central point of the grid according to the average value of the coordinates of the points in each grid; point cloud data is generated based on the center point of each mesh.
In some embodiments, connecting the proxels in the set of proxels to construct a non-overlapping set of triangles includes: connecting the projection points in the projection point set and performing visibility inspection, and reserving a visual point; and triangulating the visible points to construct a non-overlapping triangle set.
In some embodiments, before connecting the proxels in the set of proxels to construct a non-overlapping set of triangles, the method further comprises: and filtering out the projection points of which the distances from the reference point in the projection point set exceed a preset threshold, wherein in the projection point set, the distance obtained by multiplying the distance from the closest point to the reference point by a preset parameter is used as a preset threshold, and the preset threshold is greater than a preset side length threshold.
In some embodiments, the method further comprises: establishing a coordinate system on a projection plane of the reference point by taking the reference point as an origin, and obtaining the coordinate of each projection point; and calculating an included angle between a line segment formed by each projection point and the origin and the transverse axis according to the coordinates of each projection point, and calculating the angle of each angle in the triangle according to the included angle.
In some embodiments, the connecting the proxels in the set of proxels to construct a non-overlapping set of triangles includes: based on a greedy projection triangularization algorithm, connecting the projection points in the projection point set to construct non-overlapping candidate triangles; and if the candidate triangle meets the angle limitation condition and the length limitation condition, adding the candidate triangle into the triangle set.
In some embodiments, the angle limitation includes an angle of any one angle being less than a predetermined maximum angle and greater than a predetermined minimum angle, and the length limitation includes: the length of any side is smaller than a preset side length threshold value, wherein the preset side length threshold value is positively correlated with the distance from the reference point to the laser radar and the angle resolution of the laser radar.
In a second aspect, embodiments of the present disclosure provide a device for detecting a remaining cargo volume, wherein a laser radar is installed at a rear portion of a storage box, and scans a point cloud inside the whole storage box towards an inside of the storage box, the device including: an acquisition unit configured to acquire point cloud data within a storage bin; a triangularization unit configured to select a reference point from the point cloud data, perform the following point cloud triangularization steps: searching a predetermined number of candidate points closest to the reference point; projecting the candidate points with the preset number on a projection plane of the reference point to obtain a projection point set; connecting the projection points in the projection point set to construct a non-overlapping triangle set; selecting points near the reference point from points without reference points as the reference point, and repeatedly executing the point cloud triangularization step until all the points are traversed, wherein the projection direction is the direction in which the position of the laser radar points to the reference point, and the projection plane is a plane which passes through the reference point and is perpendicular to the projection direction; a construction unit configured to construct a tetrahedron for each triangle in the set of triangles, based on coordinates before projection of three points of the triangle and a position of the lidar, and calculate a volume of the tetrahedron; a computing unit configured to sum the volumes of each tetrahedron as a remaining cargo volume.
In some embodiments, the obtaining unit is further configured to: acquiring initial point cloud data in a storage box; dividing grids for the initial point cloud data according to the angular resolution of the laser radar; calculating the coordinate of the central point of the grid according to the average value of the coordinates of the points in each grid; point cloud data is generated based on the center point of each mesh.
In some embodiments, the triangularization unit is further configured to: connecting the projection points in the projection point set and performing visibility inspection, and reserving a visual point; and triangulating the visual points to construct a non-overlapping triangle set.
In some embodiments, the triangularization unit is further configured to: filtering out the projection points of which the distances from the reference point in the projection point set exceed a preset threshold before connecting the projection points in the projection point set to construct a non-overlapping triangle set, wherein in the projection point set, the distance obtained by multiplying the distance from the nearest point of the reference point by a preset parameter is used as a preset threshold, and the preset threshold is greater than a preset side length threshold.
In some embodiments, the triangularization unit is further configured to: establishing a coordinate system on a projection plane of the reference point by taking the reference point as an origin, and obtaining the coordinate of each projection point; and calculating an included angle between a line segment formed by each projection point and the origin and the transverse axis according to the coordinates of each projection point, and calculating the angle of each angle in the triangle according to the included angle.
In some embodiments, the triangularization unit is further configured to: based on a greedy projection triangularization algorithm, connecting the projection points in the projection point set to construct non-overlapping candidate triangles; and if the candidate triangle meets the angle limitation condition and the length limitation condition, adding the candidate triangle into the triangle set.
In some embodiments, the angle limitation includes an angle of any one angle being less than a predetermined maximum angle and greater than a predetermined minimum angle, and the length limitation includes: the length of any side is smaller than a preset side length threshold value, wherein the preset side length threshold value is positively correlated with the distance from the reference point to the laser radar and the angle resolution of the laser radar.
In a third aspect, embodiments of the present disclosure provide an electronic device for detecting a remaining cargo volume, comprising: one or more processors; storage means having one or more computer programs stored thereon, which when executed by the one or more processors, cause the one or more processors to implement the method of any one of the first aspects.
In a fourth aspect, embodiments of the present disclosure provide a computer-readable medium, on which a computer program is stored, wherein the computer program, when executed by a processor, implements the method according to any one of the first aspects.
According to the method and the device for detecting the residual cargo volume, the laser radar equipment is arranged at the tail of the storage box, the cloud point image of the storage box after the cargo is filled can be collected, the occupied volume of the cargo in the current storage box can be calculated by utilizing the cloud point image, and the cargo loading and unloading of workers can not be influenced. The triangulation effect is not good due to the sparseness of the remote point cloud. According to the principle of laser radar imaging, the laser emission direction is selected as the normal direction of the point cloud, the point cloud near the reference point cloud is projected to the plane perpendicular to the normal vector, and triangulation is performed on the plane, so that the problem that the point cloud at the side of the deep part of the storage box cannot be better triangular due to sparsity caused by the angle resolution, and finally the volume calculation has errors can be solved.
Drawings
Other features, objects and advantages of the disclosure will become more apparent upon reading of the following detailed description of non-limiting embodiments thereof, made with reference to the accompanying drawings in which:
FIG. 1 is an exemplary system architecture diagram in which one embodiment of the present disclosure may be applied;
FIG. 2 is a flow diagram of one embodiment of a method of detecting a remaining cargo volume according to the present disclosure;
FIGS. 3a, 3b are schematic diagrams of point cloud data for a method of detecting a remaining cargo volume according to the present disclosure;
FIG. 4 is a flow diagram of yet another embodiment of a method of detecting a remaining cargo volume according to the present disclosure;
FIG. 5 is a schematic diagram of an embodiment of an apparatus for detecting a remaining cargo volume according to the present disclosure;
FIG. 6 is a schematic block diagram of a computer system suitable for use with an electronic device implementing embodiments of the present disclosure.
Detailed Description
The present disclosure is described in further detail below with reference to the figures and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not to be construed as limiting the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that, in the present disclosure, the embodiments and features of the embodiments may be combined with each other without conflict. The present disclosure will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Fig. 1 illustrates an exemplary system architecture to which embodiments of the method of detecting a remaining cargo volume or the apparatus for detecting a remaining cargo volume of the present disclosure may be applied.
As shown in fig. 1, the system architecture includes a lidar, a processing chip, and a server.
As an example, in the implementation process of the scheme, the laser radar can adopt 128-line laser radar OS0-128 of the company of Ouster, and the laser radar is used for collecting point cloud data; the processing chip can be, for example, an i.mx8m chip of NXP corporation, and is used for uploading point clouds collected by the OSs 0-128 in a cloud and processing data in the cloud. The operation flow of the whole scheme is as follows:
1. the OS0-128 is installed at the tail of the storage box, the installation direction of the radar faces to the inside of the storage box, and due to the fact that the OS0-128 has a vertical angle of view of 90 degrees and a horizontal angle of view of 360 degrees, the point cloud in the whole storage box can be scanned completely. In the operation process, the radar continuously scans the point cloud condition in the storage box.
2. The I.MX8M embedded board is connected with the OS0-128 through a network port, point cloud data generated by the OS0-128 are obtained, and the point cloud data are packaged and uploaded to the server.
3. The server processes the data, namely, a triangularization method is used for carrying out model reconstruction on the point cloud, and the volume of the current point cloud is calculated after reconstruction.
The method comprises the steps of measuring and calculating the volume inside the storage box by using a laser radar, triangulating the collected point cloud to form a grid surface, measuring and calculating the volume inside the storage box, forming tetrahedrons with the laser radar installation position after the point cloud is triangulated, calculating tetrahedrons formed by each triangle on the surface of the triangulated model and the points of the laser radar installation position, and summing each tetrahedron to obtain the volume of the residual space in the current storage box.
The server may be hardware or software. When the server is hardware, it may be implemented as a distributed server cluster composed of multiple servers, or may be implemented as a single server. When the server is software, it may be implemented as multiple pieces of software or software modules (e.g., multiple pieces of software or software modules used to provide distributed services), or as a single piece of software or software module. And is not particularly limited herein. The server may also be a server of a distributed system, or a server incorporating a blockchain. The server can also be a cloud server, or an intelligent cloud computing server or an intelligent cloud host with artificial intelligence technology.
It should be noted that the method for detecting the remaining cargo volume provided by the embodiments of the present disclosure is generally performed by a server, and accordingly, the device for detecting the remaining cargo volume is generally disposed in the server.
It should be understood that the numbers of lidar, i.mx8m chips, and servers in fig. 1 are illustrative only. There may be any number of lidar, i.mx8m chips, and servers, as desired for the implementation. Further, the models of the laser radar and the chip are not limited to those exemplified in the embodiment of the present application.
With continued reference to FIG. 2, a flow 200 of one embodiment of a method of detecting a remaining cargo volume in accordance with the present disclosure is shown. The method for detecting the remaining cargo volume comprises the following steps:
In this embodiment, an executing entity (for example, a server shown in fig. 1) of the method for detecting the remaining cargo volume may acquire point cloud data in the storage box from a laser radar with which the user performs detection of the remaining cargo volume through a wired connection or a wireless connection. The application can directly use the original point cloud data.
In some optional implementations of the present embodiment, mean filtered point cloud data may also be used. The mean filtering process is as follows: acquiring initial point cloud data in a storage box; dividing grids for the initial point cloud data according to the angular resolution of the laser radar; calculating the coordinate of the central point of the grid according to the average value of the coordinates of the points in each grid; point cloud data is generated based on the center point of each mesh. And dividing the initial point cloud into grids according to the angle resolution of the laser radar, dividing the grids into transverse and longitudinal partitions, and solving the gravity center of the current point cloud according to the point cloud coordinates in each angle grid so as to perform mean value filtering. The method has the advantages that the input point cloud is subjected to mean filtering according to the angular resolution of the laser radar, the problem that the laser radar scans a plurality of point clouds at the same angle and the connection is complex due to the fact that the projection of the local point clouds behind is triangulated can be solved, accordingly, the calculation process is simplified, and the detection time is shortened.
In the present embodiment, an arbitrary initial point is selected as the reference point R.
In the present embodiment, the distance here is a distance in a three-dimensional space calculated from actual coordinates of the point cloud. The predetermined number k may be an integer of 100 or the like. And taking a predetermined number of points closest to the reference point in the point cloud data as candidate points.
In this embodiment, k nearest points meeting the distance requirement on the R point are projected in a direction in which the position of the laser radar points to the R point, and all the points are projected onto a plane perpendicular to this direction, so as to obtain the plane coordinates of each nearest point, as shown in fig. 3 a. According to the installation mode shown in fig. 1, in the practical application process, the triangulation effect is not good due to the sparse remote point cloud. According to the principle of laser radar formation of image, select the normal direction of laser emission direction conduct cloud, with the reference point near the cloud point on the plane of this normal vector of perpendicular to, carry out greedy projection triangulation on this plane, can solve the problem that the cloud of the storage tank depths side leads to greedy point cloud triangulation can't better form triangle-shaped by the sparsity that angular resolution caused from this.
The direction of using present reference point and initial point is carried out the projection as projection plane perpendicular direction, because the fixed shooting of laser radar and mechanical radar are according to the characteristic that angular resolution scanned, select this projection plane can not need normal vector, and the point cloud surface density after the projection is even, can be better carry out the triangulation. After the projection surface is selected, the direction of each local point cloud on the projection surface and the radar origin is very close to the direction from the reference point to the origin, namely, the projected plane is equivalent to that the projected plane points only carry out depth compression on the current point cloud in the projection direction, and the connection errors after point cloud projection can not be caused.
In some optional implementations of this embodiment, the method further includes: a coordinate system can be established on a projection plane of the reference point by taking the reference point as an origin, and the coordinate of each projection point is obtained; and calculating an included angle between a line segment formed by each projection point and the origin and the horizontal axis according to the coordinates of each projection point, and taking the included angle as the angle of each projection point. And calculating the angle of each projection point for judging the rear angle limit. Thus, the angles of the two projection points can be directly subtracted when calculating the angle of the triangle. Therefore, the calculation speed is improved, and the detection time is shortened.
In this embodiment, the projective points in the set of projective points can be connected by the existing three-dimensional point reconstruction technique to construct a non-overlapping triangle set, as shown in fig. 3 b. The specific steps are as follows:
initializing a triangular patch:
1. calculating the barycenter of all the points;
2. finding a point closest to the center of gravity, and setting the point as ptn 0;
3. in the field of ptn0, the farthest distance was calculated, and a point from it 1/3len (but still greater than the minimum distance) was found as ptn 1;
4. finding a point ptn2 in the field such that the radius of the circumscribed circle they make is minimized;
a triangular patch is constructed from these three points and labeled as boundary points.
(II) constructing Mesh:
1. firstly, finding all boundary half edges from the Mesh;
2. for each of the boundary halves: if the endpoint is also extensible, then the following is done, otherwise:
1) firstly, expanding from the upper and lower adjacent edges of the edge, and if the expansion is successful, performing triangular reconstruction on the newly added edge; and the new half of the reconstruction is added to the half set.
2) If the expansion cannot be carried out on the upper edge and the lower edge, finding out all unused adjacent points of the two end points;
3) for each of the above neighboring points, the third endpoint of the triangle is formed, but several requirements are met:
A. the length of the two newly added edges is less than a threshold value;
B. the internal angles of the triangle are all within a certain range: (20 to 100 degree)
C. The dihedral angle formed is greater than a threshold value.
D. The radius of the circumscribed circle of the formed triangular patch is minimum.
E. Forming new surface without boundary points or points which can not be expanded
4) And after finding out the points which accord with the reconstruction, reconstructing a triangular patch, reconstructing two newly added edges (reconstructing only in the front edge and the rear edge), and finally reconstructing the two newly added edges and putting the two newly added edges into the half-edge queue.
5) If both points of a half cannot be reconstructed, the points are deleted until there is no edge in the half queue.
And (III) deleting the triangular plates with overlapped reconstruction:
1. firstly, finding boundary points but not isolated points;
2. finding the boundary surface of its domain for each such point; if the number of the boundary surfaces is less than 2, no overlap exists; an overlap is indicated if there is an angle <30 deg. from face to face.
3. If overlap occurs, these faces are deleted.
(IV) updating the use condition of each point
Traversing each point in the Mesh, and assigning a corresponding zone bit to the corresponding point: boundary points: PT _ BOUND isolated points: PT _ NOT _ USED IN-FACE POINT PT _ IN _ MESH AND RETARDING POINT PT _ TRI _ USED
Fifthly, filling holes
1. Finding each hole:
A. finding all boundary edges to place in a queue;
B. starting from one half of the edges, finding the half of the edge taking the starting point of the edge as the end point, and sequentially finding the half of the edge until finding the edge taking the end point of the first edge as the starting point; one hole is found, the rest are similar;
C. judging the effectiveness of the holes: 1. if there are only three sides, it may be an isolated surface
2. If 4 sides are possible, two isolated faces.
3. If the edge is between 2 and 8, there is a common vertex and no row
The above-mentioned 3 cases are all met by removing the faces.
4. Filling holes:
1) if the number of edges is less than 20, delete its surrounding faces; if the holes are common holes, filling is carried out, otherwise, the holes are not filled.
2) After the above operations, the holes are extracted twice. And (5) carrying out filling work.
5. The use information of the point is updated again, as above
In this embodiment, the points near the reference point are added to the sequence and a point is randomly chosen as the reference point for the next cycle, and the above steps are repeated until all the points are completed.
And step 207, constructing a tetrahedron according to the coordinates of the three points of the triangle before projection and the position of the laser radar for each triangle in the triangle set, and calculating the volume of the tetrahedron.
In this embodiment, after the point cloud is triangulated, points on the two-dimensional projection plane are associated with points in the three-dimensional space before projection, and a tetrahedron is constructed using a triangle formed by the points in the three-dimensional space and the position of the laser radar. The tetrahedral volume calculation formula is prior art and thus will not be described in detail.
The volume of each tetrahedron is summed 208 to provide the remaining cargo volume.
In this embodiment, the remaining space of the storage compartment is calculated as the remaining cargo volume by summing the volumes of each tetrahedron. When the goods are placed, the goods are placed closely according to the sequence from the head of the storage box to the tail of the storage box. Alternatively, if the lidar is mounted at the head of the vehicle, the lidar may be arranged closely in the order from the rear of the storage box to the head of the storage box. This allows not only the remaining cargo volume to be calculated, but also the already-loaded volume to be calculated from the total cargo volume.
With further reference to FIG. 4, a flow 400 of yet another embodiment of a method of detecting a remaining cargo volume is shown. The process 400 of the method for detecting a remaining cargo volume includes the steps of:
In step 403, a predetermined number of candidate points closest to the reference point are searched.
Steps 401-404 are substantially the same as steps 201-204 and thus are not described again.
In this embodiment, among the k closest points, the points beyond the distance among the k points are limited by the distance obtained by multiplying the distance from the closest point to the R point by the parameter u, and the points beyond the distance do not participate in the triangulation. The processing here is to limit points where the point cloud is sparse to too far distances in order to adapt to the density of the point cloud near R. The predetermined threshold is greater than the predetermined side length threshold.
And 406, connecting the projection points in the projection point set and performing visibility inspection.
In the embodiment, the reference point and the closest point are connected and subjected to the visibility check, and firstly, whether the side to be formed into the triangle in the projection plane is visually checked is carried out, namely, when the formed side of the triangle intersects with the side to be formed into the triangle, the formation of the triangle is abandoned. The points connected to the dotted line of point R in fig. 3b are all occluded, i.e. not visible, by the sides forming the triangle.
And step 407, performing angle threshold and side length limitation judgment on the triangle to be formed.
In the present embodiment, the angle limitation condition includes that the angle of any one angle is smaller than the predetermined maximum angle and larger than the predetermined minimum angle, so that the case where the angle is very small or very large does not occur, and it is easier to construct a tetrahedron whose volume is easily calculated. And carrying out triangle connection on the points subjected to the visibility verification, judging that the angle of the formed triangle is between the maximum angle and the minimum angle, and meeting the side length limitation of the self-adaptive triangle, and considering that the triangle can form a triangularization network. In the practical application process, the point cloud densities at different distances are different, the side length limit threshold of the triangle needs to be subjected to self-adaptive processing, the problem that the same triangle side length limit cannot adapt to different point cloud densities in different areas, and the triangulation effect is poor is solved, the distance of the closest point of the point cloud on the projection surface in the last step can be estimated through the distance judgment of the point cloud and the angle resolution characteristic of the laser radar, and therefore the side length threshold of the triangle formed by self-adaptive shaping can be obtained.
The length limiting conditions include: the length of any side is smaller than a preset side length threshold value, wherein the preset side length threshold value is positively correlated with the distance from the reference point to the laser radar and the angular resolution of the laser radar. For example,
L＝b*μ*tan(θ/2)
wherein L is a preset side length threshold, mu is the distance from a reference point to the laser radar, and theta is the angular resolution of the laser radar. The hyperparameter b is 2 or more and may be set to 5 or more.
In the greedy projection triangulation process, the side length of a triangle during triangulation needs to be limited to prevent a reference point and a far point from forming triangulation of other points in a larger triangle influence area, and the density scanned by a point radar in different areas is different, in the original greedy projection triangulation method, only the side length of the triangle with a fixed threshold is limited, and the method cannot be self-adaptive to point clouds with different densities, and based on the scanning characteristic of a laser radar, when the radar is installed at a fixed position, the point clouds at far distances are sparse, and under an ideal condition, if the point clouds scanned by the laser radar are limited by angular resolution, the angle intervals of the point clouds between two points are uniform, the distance between the point clouds with different distances and the nearest point cloud on a projection plane can be calculated, the angular resolution during radar scanning is set to be theta, and the distance between the current point cloud and the origin of the radar is set to be mu, the distance L between the current point and its closest point is as follows:
L＝2*μ*tan(θ/2)
therefore, the nearest distance L of surrounding points on the projection surface can be estimated at points in different areas through the calculation mode of the formula, the side length threshold of the limiting triangle is set to be omega L, wherein omega is a fixed parameter, namely the threshold for limiting the side length of the triangle is limited by taking the multiple of L under the projection surface, and therefore the self-adaptive side length threshold is achieved.
The side length limit threshold of the triangle is adaptively adjusted according to the depth of the point cloud, so that the side length of the triangle can be better limited under different point cloud densities. Because the shooting position of the laser radar is fixed, and because of the scanning characteristic of the mechanical laser radar, the point cloud density of an area close to the radar is larger, the density of an area far away from the radar is sparser, and under the ideal condition, the distances among the point clouds at different distances can be calculated, so that the side length threshold of the triangle can be adapted.
And step 409, constructing a tetrahedron for each triangle in the triangle set according to the coordinates of the three points of the triangle before projection and the position of the laser radar, and calculating the volume of the tetrahedron.
The volume of each tetrahedron is summed 410 to provide the remaining cargo volume.
The steps 408 and 410 are substantially the same as the steps 206 and 208, and therefore will not be described in detail.
The quality of triangularization of the point cloud is a key factor influencing the volume calculation of the final storage box. The commonly used point cloud triangulation algorithm is greedy projection triangulation, which firstly calculates a normal vector of each point cloud, projects each point in a local area of each point based on a tangent plane perpendicular to the normal vector, and completes triangulation connection after projection. If inside installing the storage tank with laser radar, can influence the loading and unloading of workman to the goods, and install laser radar equipment to the storage tank afterbody rigid, can shoot the inside goods volume of storage tank by the full flow, and do not influence the normal goods handling of storage tank. Since the scanning mode of the mechanical lidar is an angular division mode, different devices have different angular resolutions. When the mechanical lidar is mounted at the rear of the storage box and the farthest position of the storage box is scanned, the point cloud finally generated on the side of the storage box due to the angular resolution characteristic of the radar can form the situation as shown in fig. 1. The right side in figure 1 is laser radar mounted position, and when the circle position was scanned, the radar point cloud that appears was shown in figure 1, and when using greedy projection triangulation algorithm to do the triangulation of point cloud under this condition, can be because threshold value judgement problem, can't form the triangulation net very well.
In greedy projection triangulation, the reason that partial point clouds are projected onto a tangent plane to form triangulation is that intersection of different triangles cannot occur after point clouds are triangulated, however, errors exist in calculation of normal vectors of unordered point clouds, and even if the partial normal vectors are found correctly, normal triangulation is difficult to achieve due to irregularity of the point clouds when the partial normal vectors are subsequently triangulated on a projection plane. Therefore, based on the shooting characteristic of the laser radar, a projection plane with the direction from the local point cloud to the original point (namely the installation position) of the laser radar as a normal vector is selected for a certain local point cloud. And because the angular resolution of the laser radar, the local point cloud projected on the plane is a point with uniform distribution, so when the point cloud is triangulated on the projection surface, the problem that the point cloud is disordered and cannot be triangulated better can not be caused. The effect is shown in fig. 3a, and the point clouds are uniformly distributed under the projection plane.
The method based on greedy triangularization in special scenes is applied to storage box volume calculation, and better triangularization models and volume calculation accuracy are achieved.
With further reference to fig. 5, as an implementation of the methods illustrated in the above figures, the present disclosure provides one embodiment of an apparatus for detecting a remaining cargo volume, which corresponds to the method embodiment illustrated in fig. 2, and which may be particularly applicable in various electronic devices.
As shown in fig. 5, the apparatus 500 for detecting the remaining cargo volume of the present embodiment, wherein the laser radar is installed at the tail of the storage box, and scans the entire point cloud inside the storage box towards the inside of the storage box, comprises: an acquisition unit 501, a triangularization unit 502, a construction unit 503 and a calculation unit 504. The acquisition unit 501 is configured to acquire point cloud data in the storage box; a triangularization unit 502 configured to select a reference point from the point cloud data, perform the following point cloud triangularization steps: searching a predetermined number of candidate points closest to the reference point; projecting the candidate points with the preset number on a projection plane of the reference point to obtain a projection point set; connecting the projection points in the projection point set to construct a non-overlapping triangle set; selecting points near the reference point from points without reference points as the reference points, and repeatedly executing the point cloud triangularization step until all the points are traversed, wherein the projection direction is the direction in which the position of the laser radar points to the reference points, and the projection plane is a plane which passes through the reference points and is perpendicular to the projection direction; a constructing unit 503 configured to construct, for each triangle in the set of triangles, a tetrahedron from the coordinates of the triangle before projection of the three points and the position of the lidar, and calculate a volume of the tetrahedron; a calculation unit 504 configured to sum the volume of each tetrahedron as the remaining cargo volume.
In the present embodiment, the specific processes of the obtaining unit 501, the triangularization unit 502, the construction unit 503 and the calculation unit 504 of the apparatus 500 for detecting a remaining cargo volume may refer to step 201 and step 208 in the corresponding embodiment of fig. 2.
In some optional implementations of this embodiment, the obtaining unit 501 is further configured to: acquiring initial point cloud data in a storage box; dividing grids for the initial point cloud data according to the angular resolution of the laser radar; calculating the coordinate of the central point of the grid according to the average value of the coordinates of the points in each grid; point cloud data is generated based on the center point of each mesh.
In some optional implementations of the present embodiment, the triangularization unit 502 is further configured to: connecting the projection points in the projection point set, carrying out visibility inspection, and reserving a visual point; and triangulating the visible points to construct a non-overlapping triangle set.
In some optional implementations of the present embodiment, the triangularization unit 502 is further configured to: filtering out the projection points of which the distances from the projection point set to the reference point exceed a preset threshold before connecting the projection points in the projection point set to construct a non-overlapping triangle set, wherein in the projection point set, the distance obtained by multiplying the distance from the nearest point of the reference point to a preset parameter is used as a preset threshold, and the preset threshold is greater than a preset side length threshold.
In some optional implementations of the present embodiment, the triangularization unit 502 is further configured to: establishing a coordinate system on a projection plane of the reference point by taking the reference point as an origin, and obtaining the coordinate of each projection point; and calculating an included angle between a line segment formed by each projection point and the origin and the transverse axis according to the coordinates of each projection point, and calculating the angle of each angle in the triangle according to the included angle.
In some optional implementations of the present embodiment, the triangularization unit 502 is further configured to: connecting the projection points in the projection point set based on a greedy projection triangulation algorithm to construct non-overlapping candidate triangles; and if the candidate triangle meets the angle limitation condition and the length limitation condition, adding the candidate triangle into the triangle set.
In some optional implementations of this embodiment, the angle limitation condition includes that an angle of any one angle is smaller than a predetermined maximum angle and larger than a predetermined minimum angle, and the length limitation condition includes: the length of any side is smaller than a preset side length threshold value, wherein the preset side length threshold value is positively correlated with the distance from the reference point to the laser radar and the angle resolution of the laser radar.
According to an embodiment of the present disclosure, the present disclosure also provides an electronic device and a readable storage medium.
An electronic device for detecting a remaining cargo volume, comprising: one or more processors; a storage device having one or more computer programs stored thereon that, when executed by the one or more processors, cause the one or more processors to implement the method of flows 200 or 400.
A computer-readable medium, on which a computer program is stored, wherein the computer program, when executed by a processor, performs the method of flows 200 or 400.
FIG. 6 illustrates a schematic block diagram of an example electronic device 600 that can be used to implement embodiments of the present disclosure. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of the disclosure described and/or claimed herein.
As shown in fig. 6, the apparatus 600 includes a computing unit 601, which can perform various appropriate actions and processes according to a computer program stored in a Read Only Memory (ROM)602 or a computer program loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data necessary for the operation of the device 600 can also be stored. The calculation unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network such as the internet and/or various telecommunication networks.
The computing unit 601 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various dedicated Artificial Intelligence (AI) computing chips, various computing units running machine learning model algorithms, a Digital Signal Processor (DSP), and any suitable processor, controller, microcontroller, and so forth. The calculation unit 601 performs the respective methods and processes described above, for example the method of detecting the remaining cargo volume. For example, in some embodiments, the method of detecting the remaining cargo volume may be implemented as a computer software program tangibly embodied in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the method of detecting a remaining cargo volume described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform the method of detecting the remaining cargo volume by any other suitable means (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuitry, Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), system on a chip (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
Program code for implementing the methods of the present disclosure may be written in any combination of one or more programming languages. These program code may be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the program code, when executed by the processor or controller, causes the functions/acts specified in the flowchart and/or block diagram to be performed. The program code may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which a user may provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the Internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server may be a server of a distributed system or a server incorporating a blockchain. The server can also be a cloud server, or an intelligent cloud computing server or an intelligent cloud host with artificial intelligence technology. The server may be a server of a distributed system or a server incorporating a blockchain. The server can also be a cloud server, or an intelligent cloud computing server or an intelligent cloud host with artificial intelligence technology.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present disclosure may be executed in parallel, sequentially, or in different orders, and are not limited herein as long as the desired results of the technical solutions disclosed in the present disclosure can be achieved.
The above detailed description should not be construed as limiting the scope of the disclosure. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present disclosure should be included in the scope of protection of the present disclosure.
Claims (10)
1. A method of detecting a remaining cargo volume, wherein a lidar is mounted at a rear portion of a storage bin to scan a point cloud entirely within the storage bin toward an interior of the storage bin, the method comprising:
acquiring point cloud data in a storage box;
selecting a reference point from the point cloud data, and executing the following point cloud triangularization steps: searching a predetermined number of candidate points closest to the reference point; projecting the candidate points with the preset number on a projection plane of the reference point to obtain a projection point set; connecting the projection points in the projection point set to construct a non-overlapping triangle set; selecting points near the reference point from points without reference points as the reference point, and repeatedly executing the point cloud triangularization step until all the points are traversed, wherein the projection direction is the direction in which the position of the laser radar points to the reference point, and the projection plane is a plane which passes through the reference point and is perpendicular to the projection direction;
for each triangle in the triangle set, constructing a tetrahedron according to the coordinates of the triangle before the three points are projected and the position of the laser radar, and calculating the volume of the tetrahedron;
the volumes of each tetrahedron are summed to serve as the remaining cargo volume.
2. The method of claim 1, wherein the obtaining point cloud data within a storage bin comprises:
acquiring initial point cloud data in a storage box;
dividing grids for the initial point cloud data according to the angular resolution of the laser radar;
calculating the coordinate of the central point of the grid according to the average value of the coordinates of the points in each grid;
point cloud data is generated based on the center point of each mesh.
3. The method of claim 1, wherein the connecting the proxels in the set of proxels constructs a non-overlapping set of triangles including:
connecting the projection points in the projection point set and performing visibility inspection, and reserving a visual point;
and triangulating the visible points to construct a non-overlapping triangle set.
4. The method of claim 1, wherein, prior to connecting the proxels in the set of proxels to construct a non-overlapping set of triangles, the method further comprises:
and filtering out the projection points of which the distances from the reference point in the projection point set exceed a preset threshold, wherein in the projection point set, the distance obtained by multiplying the distance from the closest point to the reference point by a preset parameter is used as a preset threshold, and the preset threshold is greater than a preset side length threshold.
5. The method of claim 1, wherein the method further comprises:
establishing a coordinate system on a projection plane of the reference point by taking the reference point as an origin, and obtaining the coordinate of each projection point;
and calculating an included angle between a line segment formed by each projection point and the original point and the transverse axis according to the coordinates of each projection point, and calculating the angle of each angle in the triangle according to the included angle.
6. The method of claim 1, wherein the connecting the proxels in the set of proxels constructs a non-overlapping set of triangles including:
connecting the projection points in the projection point set based on a greedy projection triangulation algorithm to construct non-overlapping candidate triangles;
and if the candidate triangle meets the angle limitation condition and the length limitation condition, adding the candidate triangle into the triangle set.
7. The method of claim 6, wherein the angle constraints include that the angle of any one angle is less than a predetermined maximum angle and greater than a predetermined minimum angle, the length constraints including: the length of any side is smaller than a preset side length threshold value, wherein the preset side length threshold value is positively correlated with the distance from the reference point to the laser radar and the angular resolution of the laser radar.
8. A device for detecting a remaining cargo volume, wherein a lidar is mounted at the rear of a storage bin and scans the entire point cloud inside the storage bin towards the interior of the storage bin, the device comprising:
an acquisition unit configured to acquire point cloud data within a storage bin;
a triangularization unit configured to select a reference point from the point cloud data, perform the following point cloud triangularization steps: searching a predetermined number of candidate points closest to the reference point; projecting the candidate points with the preset number on a projection plane of the reference point to obtain a projection point set; connecting the projection points in the projection point set to construct a non-overlapping triangle set; selecting points near the reference point from points without reference points as the reference point, and repeatedly executing the point cloud triangularization step until all the points are traversed, wherein the projection direction is the direction in which the position of the laser radar points to the reference point, and the projection plane is a plane which passes through the reference point and is perpendicular to the projection direction;
a construction unit configured to construct a tetrahedron for each triangle in the set of triangles, based on coordinates before projection of three points of the triangle and a position of the lidar, and calculate a volume of the tetrahedron;
a computing unit configured to sum the volumes of each tetrahedron as a remaining cargo volume.
9. An electronic device for detecting a remaining cargo volume, comprising:
one or more processors;
a storage device having one or more computer programs stored thereon,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, wherein the computer program, when being executed by a processor, carries out the method according to any one of claims 1-7.
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| CN117268498B (en) * | 2023-11-20 | 2024-01-23 | 中国航空工业集团公司金城南京机电液压工程研究中心 | Oil mass measurement method and system |
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