CN116626702A - Elevator hoistway real-time modeling monitoring method based on multiple area array laser radars - Google Patents
Elevator hoistway real-time modeling monitoring method based on multiple area array laser radars Download PDFInfo
- Publication number
- CN116626702A CN116626702A CN202310749209.7A CN202310749209A CN116626702A CN 116626702 A CN116626702 A CN 116626702A CN 202310749209 A CN202310749209 A CN 202310749209A CN 116626702 A CN116626702 A CN 116626702A
- Authority
- CN
- China
- Prior art keywords
- point cloud
- cloud data
- elevator
- plane
- pose
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 39
- 238000012544 monitoring process Methods 0.000 title claims abstract description 18
- 230000008859 change Effects 0.000 claims abstract description 61
- 238000013519 translation Methods 0.000 claims description 18
- 238000012806 monitoring device Methods 0.000 claims description 7
- 238000004364 calculation method Methods 0.000 claims description 5
- 230000000694 effects Effects 0.000 description 13
- 230000006870 function Effects 0.000 description 7
- 238000013507 mapping Methods 0.000 description 4
- 238000000605 extraction Methods 0.000 description 3
- 238000005259 measurement Methods 0.000 description 3
- 230000007246 mechanism Effects 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000006073 displacement reaction Methods 0.000 description 2
- 230000010354 integration Effects 0.000 description 2
- 230000001788 irregular Effects 0.000 description 2
- 230000000007 visual effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000008447 perception Effects 0.000 description 1
- 230000008569 process Effects 0.000 description 1
Classifications
-
- 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
-
- 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
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/497—Means for monitoring or calibrating
Abstract
The application provides a real-time modeling and monitoring method for an elevator shaft based on a plurality of area array laser radars, which comprises the following steps: the method comprises the steps of calibrating pose relations by a plurality of laser radars facing each surface of the inner wall of an elevator shaft on an elevator car, and establishing current laser radar point cloud data; splicing the laser radar point cloud data at adjacent moments to obtain pose change initial values; dividing the space in an elevator hoistway into a plurality of grids, calculating a PCA result of each grid, and extracting all planes in laser radar point cloud data according to characteristic values of the PCA result to serve as first plane parameters; matching is carried out according to the pose change initial value and the first plane parameter, and matching cost is calculated through a function, so that the pose change initial value with the minimum matching cost is obtained; and merging the pose change initial values with the minimum matching cost at different moments to obtain a scanning modeling result. The application solves the problems of complex operation, low timeliness and poor precision of the traditional method for scanning and modeling the elevator shaft by the unmanned aerial vehicle.
Description
Technical Field
The application relates to the technical field of elevators, in particular to a real-time modeling and monitoring method for an elevator shaft based on a plurality of area array laser radars.
Background
In the safety operation monitoring of elevators, the perception of the real-time change situation of the hoistway is of great importance, in particular the geometrical parameters of the hoistway and the like. Current 3D modeling and metrology methods of elevator shafts have begun to gradually use lidar as a sensor. The laser radar is moved in the well through a high-precision guide rail or other methods with precise control sensors, and 3D modeling and measurement of the well are realized through splicing of laser point clouds.
The method based on the guide rail needs a very complicated layout process, and has high requirement on manual operation and lower safety coefficient. Other methods include hoistway scanning reconstruction by carrying a 360 degree angle of view mechanical rotary lidar under the unmanned aerial vehicle. And carrying a high-precision inertial navigation device and a range finder on the unmanned aerial vehicle, recording the flying gesture and position in real time, and finally splicing laser scanning data according to the information to finish well modeling and measurement. The unmanned aerial vehicle-based method is high in automation degree, but high-performance auxiliary sensors are required to be configured, and operation is complex. In addition, in the operation of the elevator, the unmanned aerial vehicle cannot finish measurement in real time, and the timeliness is low. And the number of lines of the rotary multi-line laser radar is generally small, and the angular resolution is low. The accuracy of hoistway reconstruction is difficult to ensure. And the high-precision laser scanner of the mapping stage is too expensive to be applied on a large scale.
Disclosure of Invention
The application solves the problems that the existing method for scanning and modeling the elevator shaft by the unmanned aerial vehicle has complex operation, low timeliness and poor precision.
In order to solve the problems, the application provides a real-time modeling and monitoring method for an elevator shaft based on a plurality of area array laser radars, which comprises the following steps: the method comprises the steps of calibrating pose relations by a plurality of laser radars facing each surface of the inner wall of an elevator shaft on an elevator car, and establishing current laser radar point cloud data; splicing the laser radar point cloud data at adjacent moments to obtain pose change initial values; dividing the space in an elevator hoistway into a plurality of grids, calculating a PCA result of each grid, and extracting all planes in the laser radar point cloud data according to the characteristic value of the PCA result to serve as first plane parameters; matching is carried out according to the pose change initial value and the first plane parameter, and matching cost is calculated through a function, so that the pose change initial value with the minimum matching cost is obtained; and merging the pose change initial values with the minimum matching cost at different moments to obtain a scanning modeling result.
The technical effect achieved after the technical scheme is adopted is as follows: the laser radar can obtain better resolution and higher point cloud density, and meanwhile, compared with a mapping-level laser scanner, the cost is lower; the automatic matching method of the point clouds at adjacent moments is adopted to realize full-automatic point cloud splicing and modeling, no additional precision mechanisms such as a range finder and a timer are needed to cooperate, the operation is simple, and the timeliness is high; aiming at the special environment of the elevator well, the plane extraction is adopted as a primitive of point cloud matching, the reliability of Gao Dian cloud full-automatic matching and splicing can be greatly improved, and the precision is improved.
Further, a plurality of lidar calibration pose relationships on the elevator car towards each face of the inner wall of the elevator shaft, comprising: and a plurality of laser radars are circumferentially arranged on the elevator car, a calibration component is arranged in a scanning overlapping area of the adjacent laser radars, and an image of the calibration component is calibrated according to the adjacent laser radars to obtain a calibration initial value.
The technical effect achieved after the technical scheme is adopted is as follows: the combination of the laser radars of the multiple area arrays, the adjacent laser radars are provided with scanning overlapping areas, so that 360-degree visual angle coverage can be realized, and the circumferential image of the elevator car can be detected more comprehensively; the adjacent laser radars can acquire images in the scanning overlapping area, and the position relationship and the posture relationship of the adjacent laser radars can be calibrated conveniently according to the images acquired by the adjacent laser radars, so that the point cloud data of the laser radars can be spliced conveniently.
Further, a plurality of laser radar calibration pose relations towards each face of elevator well inner wall on the elevator car still includes: and according to the initial calibration value, acquiring an accurate calibration value by adopting an iterative nearest point algorithm.
The technical effect achieved after the technical scheme is adopted is as follows: the images of the adjacent laser radars can be accurately spliced through the algorithm of the closest point of iteration, and the accuracy of the point cloud data of the laser radars is improved.
Further, the splicing the laser radar point cloud data at adjacent moments to obtain the pose change initial value includes: judging the pose of the point cloud at the adjacent moment according to the running speed of the elevator to obtain the initial value of the pose change; the up-down translation change amount during elevator operation is calculated by multiplying the elevator operation speed by the operation time, and the translation and rotation change amounts in other directions during elevator operation are set to be 0.
The technical effect achieved after the technical scheme is adopted is as follows: the vertical opposite translation mainly occurs in the elevator operation, the translation and rotation in other directions are less, and the pose change can be quickly obtained according to the vertical translation change amount and the operation time when the elevator operates.
Further, the calculating the PCA result of each grid, extracting all planes in the laser radar point cloud data according to the feature value of the PCA result, and using the planes as the first plane parameters includes: calculating the PCA result of each grid to obtain three characteristic values of the PCA result, wherein the maximum value of the three characteristic values is a, the middle value is b, and the minimum value is c, if any one of a-b is less than or equal to k1 and a/b is less than or equal to k2 and any one of b-c is more than or equal to k3 and b/c is more than or equal to k4 is met, judging that the laser radar point cloud data in the grid is plane-like point cloud data, and otherwise, judging that the laser radar point cloud data is non-plane point cloud data; where k1, k2, k3, k4 are constants.
The technical effect achieved after the technical scheme is adopted is as follows: if any one of a-b.ltoreq.k1 and a/b.ltoreq.k2 is satisfied and any one of b-c.ltoreq.k3 and b/c.ltoreq.k4 is satisfied, that is, one of three eigenvalues of the PCA result is far smaller than the other two, and the magnitudes of the other two eigenvalues are close, the grid is reflected to be close to a plane at the moment, and the grid reflected under other conditions is an irregular surface.
Further, the calculating the PCA result of each grid, extracting all planes in the laser radar point cloud data according to the feature value of the PCA result, and using the planes as the first plane parameters, further includes: searching grids around the grids corresponding to the plane-like point cloud data, calculating PCA results again, guiding to complete calculation of all grids, merging all plane-like point cloud data, and ignoring all non-plane-like point cloud data to obtain first plane parameters.
The technical effect achieved after the technical scheme is adopted is as follows: the grids around the planes are detected, the rest planes can be rapidly identified, integration of adjacent planes is facilitated, and accurate first plane parameters are obtained.
Further, the matching is performed according to the pose change initial value and the first plane parameter, and a matching cost is calculated through a function, so as to obtain the pose change initial value with the minimum matching cost, including: calculating Euclidean distance from a point P1 in the first plane parameter at the moment T1 to a surface A2 closest to the P1 in the first plane parameter at the moment T2, and taking the sum of Euclidean distances from all the points P1 to the surface A2 as a matching cost; and setting a search range based on the pose change initial value, and searching the pose change initial value with the minimum matching cost in the search range.
The technical effect achieved after the technical scheme is adopted is as follows: based on the cost function from point to plane on the basis of the given pose change initial value, searching a possible pose range, solving the splicing parameters, and accurately obtaining the best matched pose change initial value.
Further, the setting the search range based on the initial pose change value includes: setting a search range within an X-direction + -L1 range, a Y-direction + -L2 range, a Z-direction + -L3 range, and a rotation + -alpha angle range along an X-axis, a rotation + -beta angle range along a Y-axis, and a rotation + -gamma angle range along a Z-axis of the initial pose change value; wherein L1, L2, L3, alpha, beta, gamma are constants.
The technical effect achieved after the technical scheme is adopted is as follows: the initial pose change value and the first plane parameters have certain translation or rotation errors, translation search is performed based on the X axis, the Y axis and the Z axis, rotation search is performed based on the X axis, the Y axis and the Z axis, and all possible first plane parameters can be included and matched, so that matching cost is accurately calculated, the obtained optimal deflection angle and displacement estimation is achieved, and plane-like point cloud data are spliced into a whole.
Further, the elevator hoistway real-time modeling monitoring method further comprises the following steps: setting a D1 time period, and eliminating the scanning modeling result before the D1 time.
The technical effect achieved after the technical scheme is adopted is as follows: continuous updating of the elevator shaft scanning modeling result is achieved.
The application also provides a real-time modeling and monitoring device for the elevator shaft based on the multiple area array laser radars, which is used for realizing the real-time modeling and monitoring method for the elevator shaft provided by any embodiment.
The technical effect achieved after the technical scheme is adopted is as follows: any one or more of the technical effects of the above embodiments can be achieved.
In summary, each of the above technical solutions of the present application may have one or more of the following advantages or beneficial effects: i) The laser radar can obtain better resolution and higher point cloud density, and meanwhile, compared with a mapping-level laser scanner, the cost is lower; ii) the automatic matching method of the point clouds at adjacent moments is adopted to realize full-automatic point cloud splicing and modeling, no additional precision mechanisms such as a range finder and a timer are needed to cooperate, the operation is simple, and the timeliness is high; iii) Aiming at the special environment of the elevator well, the plane extraction is adopted as a primitive of point cloud matching, the reliability of Gao Dian cloud full-automatic matching and splicing can be greatly improved, and the precision is improved.
Drawings
Fig. 1 is a flowchart of an elevator hoistway real-time modeling and monitoring method based on a plurality of area array lidars according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an elevator hoistway real-time modeling and monitoring device based on a plurality of area array lidars according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of another view of fig. 2.
Reference numerals illustrate:
100-real-time modeling and monitoring device of elevator well; 110-lidar; 200-elevator car; 300-elevator hoistway.
Detailed Description
The application aims to provide a real-time modeling and monitoring method for an elevator shaft based on a plurality of area array laser radars, which is used for realizing better resolution and higher point cloud density and improving the effects of timeliness and precision.
In order that the above objects, features and advantages of the application will be readily understood, a more particular description of the application will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings.
Referring to fig. 1-3, an embodiment of the present application provides a method for real-time modeling and monitoring of an elevator hoistway based on a plurality of area array lidars, the method for real-time modeling and monitoring of an elevator hoistway includes: the method comprises the steps of calibrating pose relations by a plurality of laser radars facing each surface of the inner wall of an elevator shaft on an elevator car, and establishing current laser radar point cloud data; splicing the laser radar point cloud data at adjacent moments to obtain pose change initial values; dividing the space in an elevator hoistway into a plurality of grids, calculating a PCA result of each grid, and extracting all planes in laser radar point cloud data according to characteristic values of the PCA result to serve as first plane parameters; matching is carried out according to the pose change initial value and the first plane parameter, and matching cost is calculated through a function, so that the pose change initial value with the minimum matching cost is obtained; and merging the pose change initial values with the minimum matching cost at different moments to obtain a scanning modeling result.
In the embodiment, better resolution and higher point cloud density can be obtained by adopting the laser radar, and meanwhile, compared with a mapping-level laser scanner, the cost is lower; the automatic matching method of the point clouds at adjacent moments is adopted to realize full-automatic point cloud splicing and modeling, no additional precision mechanisms such as a range finder and a timer are needed to cooperate, the operation is simple, and the timeliness is high; aiming at the special environment of the elevator well, the plane extraction is adopted as a primitive of point cloud matching, the reliability of Gao Dian cloud full-automatic matching and splicing can be greatly improved, and the precision is improved.
In one particular embodiment, a plurality of lidar calibration pose relationships on an elevator car toward inner wall surfaces of an elevator hoistway, comprising: a plurality of laser radars are circumferentially arranged on an elevator car, a calibration part is arranged in a scanning overlapping area of adjacent laser radars, and an image of the calibration part is calibrated according to the adjacent laser radars to obtain a calibration initial value.
The combination of the laser radars of the multiple area arrays, the adjacent laser radars are provided with scanning overlapping areas, so that 360-degree visual angle coverage can be realized, and the circumferential image of the elevator car can be detected more comprehensively; the adjacent laser radars can acquire images in the scanning overlapping area, and the position relationship and the posture relationship of the adjacent laser radars can be calibrated conveniently according to the images acquired by the adjacent laser radars, so that the point cloud data of the laser radars can be spliced conveniently.
Preferably, the number of lidars is e.g. 4, spaced 90 ° apart and oriented in four directions of the elevator hoistway, wherein the horizontal angle of view of each lidar is 120 ° and the vertical angle of view is 22.5 °.
Preferably, the alignment part is, for example, a three-dimensional part with a concave-convex grid, the three-dimensional part having a rectangular plane which abuts against the inner wall of the elevator hoistway. Wherein adjacent lidars are manually aligned with the concave grid and the convex grid of the three-dimensional part to obtain a calibration initial value.
In a specific embodiment, the plurality of lidar calibration pose relationships on the elevator car toward the inner wall surfaces of the elevator hoistway further comprises: and according to the initial calibration value, acquiring an accurate calibration value by adopting an iterative nearest point algorithm.
It should be noted that, the algorithm of the closest point can be iterated to accurately splice the images of the adjacent lidars, so as to improve the accuracy of the point cloud data of the lidars.
In a specific embodiment, the splicing of the laser radar point cloud data at adjacent moments to obtain the pose change initial value includes: judging the pose of the point cloud at the adjacent moment according to the running speed of the elevator to obtain a pose change initial value; the up-down translation change amount during elevator operation is calculated by multiplying the elevator operation speed by the operation time, and the translation and rotation change amounts in other directions during elevator operation are set to be 0.
It should be noted that, mainly, the vertical opposite translation occurs in the elevator operation, the translation and rotation in other directions are less, and the change of the pose can be obtained rapidly according to the vertical translation change amount and the operation time when the elevator operates.
In a specific embodiment, calculating a PCA result of each grid, extracting all planes in the laser radar point cloud data according to a feature value of the PCA result, and using the planes as a first plane parameter, including: calculating the PCA result of each grid to obtain three characteristic values of the PCA result, wherein the maximum value of the three characteristic values is a, the middle value is b, and the minimum value is c, if any one of a-b is less than or equal to k1 and a/b is less than or equal to k2 and any one of b-c is more than or equal to k3 and b/c is more than or equal to k4 is met, the laser radar point cloud data in the grid is judged to be plane-like point cloud data, and otherwise, the laser radar point cloud data is non-plane point cloud data; where k1, k2, k3, k4 are constants.
If any one of a-b.ltoreq.k1 and a/b.ltoreq.k2 is satisfied and any one of b-c.ltoreq.k3 and b/c.ltoreq.k4 is satisfied, that is, one of three eigenvalues of the PCA result is far smaller than the other two, and the other two eigenvalues are close in size, the reflecting grid is close to a plane at this time, and the reflecting grid is an irregular plane under other conditions.
In a specific embodiment, a PCA result of each grid is calculated, and all planes in the laser radar point cloud data are extracted according to a feature value of the PCA result, and the method further includes: searching grids around the grids corresponding to the plane-like point cloud data, calculating the PCA result again, guiding to complete calculation of all grids, combining all plane-like point cloud data, and ignoring all non-plane-like point cloud data to obtain a first plane parameter.
It should be noted that, detecting the grid around the plane can quickly identify the rest of the planes, and facilitate the integration of the adjacent planes to obtain the accurate first plane parameters.
In a specific embodiment, matching is performed according to the pose change initial value and the first plane parameter, and matching cost is calculated through a function to obtain the pose change initial value with the minimum matching cost, including: calculating the Euclidean distance from a point P1 in the first plane parameter at the moment T1 to a surface A2 closest to the P1 in the first plane parameter at the moment T2, and taking the sum of the Euclidean distances from all the points P1 to the surface A2 as a matching cost; and setting a search range based on the pose change initial value, and searching the pose change initial value with the minimum matching cost in the search range.
It should be noted that, based on the given initial value of pose change, a possible pose range is searched based on the cost function from point to plane, and the splicing parameters are solved, so that the best matched initial value of pose change can be accurately obtained.
In a specific embodiment, setting the search range based on the initial pose change value includes: the search range is set within an X-direction + -L1 range, a Y-direction + -L2 range, a Z-direction + -L3 range, and a rotation + -alpha angle range along the X-axis, a rotation + -beta angle range along the Y-axis, and a rotation + -gamma angle range along the Z-axis of the initial value of the pose change.
It should be noted that, there is a certain translation or rotation error between the pose change initial value and the first plane parameter, translation search is performed based on three directions of the X axis, the Y axis and the Z axis, and rotation search is performed based on the X axis, the Y axis and the Z axis, so that all possible first plane parameters can be involved and matched, and thus the matching cost is accurately calculated, the obtained optimal deflection angle and displacement amount estimation is achieved, and plane-like point cloud data are spliced into a whole.
Preferably, L1, L2, L3, α, β, γ are constant, for example, L1, L2, L3 are each 10cm, and α, β, γ are each 5 °.
Preferably, when performing the translation search, each time the translation search is spaced apart by, for example, 1cm, and when performing the rotation search, each time the angle search is spaced apart by, for example, 0.05 ° to obtain accurate search results, which is not limited herein.
In a specific embodiment, the elevator hoistway real-time modeling monitoring method further comprises: setting a D1 time period, eliminating the scanning modeling result before the D1 time, and realizing continuous updating of the elevator shaft scanning modeling result. For example, D1 is, for example, 10 minutes.
Referring to fig. 2-3, the embodiment of the present application further provides a real-time modeling and monitoring device 100 for an elevator hoistway 300 based on a plurality of area array lidars 110, which is configured to implement the real-time modeling and monitoring method for an elevator hoistway 300 provided in any of the foregoing embodiments. Any one or more of the technical effects of the above embodiments can be achieved.
Preferably, the elevator hoistway 300 real-time modeling monitoring device 100 based on the plurality of area array lidars 110 comprises 4 lidars 110 positioned on the elevator car 200 towards each surface of the inner wall of the elevator hoistway 300, and the included angle between adjacent lidars 110 is 90 degrees, so as to establish the current point cloud data of the lidars 110. The elevator hoistway 300 real-time modeling monitoring device 100 further comprises a calculation module, wherein the calculation module is used for splicing laser radar 110 point cloud data at adjacent moments to obtain pose change initial values, dividing the space in the elevator hoistway 300 into a plurality of grids, calculating PCA results of each grid, extracting all planes in the laser radar 110 point cloud data according to characteristic values of the PCA results, matching according to the pose change initial values and the first plane parameters as first plane parameters, calculating matching cost through functions to obtain the pose change initial values with minimum matching cost, and combining the pose change initial values with the minimum matching cost at different moments to obtain a scanning modeling result.
Although the present application is disclosed above, the present application is not limited thereto. Various changes and modifications may be made by one skilled in the art without departing from the spirit and scope of the application, and the scope of the application should be assessed accordingly to that of the appended claims.
Claims (10)
1. The elevator hoistway real-time modeling and monitoring method based on the multiple area array laser radars is characterized by comprising the following steps of:
the method comprises the steps of calibrating pose relations by a plurality of laser radars facing each surface of the inner wall of an elevator shaft on an elevator car, and establishing current laser radar point cloud data;
splicing the laser radar point cloud data at adjacent moments to obtain pose change initial values;
dividing the space in an elevator hoistway into a plurality of grids, calculating a PCA result of each grid, and extracting all planes in the laser radar point cloud data according to the characteristic value of the PCA result to serve as first plane parameters;
matching is carried out according to the pose change initial value and the first plane parameter, and matching cost is calculated through a function, so that the pose change initial value with the minimum matching cost is obtained;
and merging the pose change initial values with the minimum matching cost at different moments to obtain a scanning modeling result.
2. The method of claim 1, wherein the plurality of lidar calibration pose relationships on the elevator car toward each side of the inner wall of the elevator hoistway comprises:
and a plurality of laser radars are circumferentially arranged on the elevator car, a calibration component is arranged in a scanning overlapping area of the adjacent laser radars, and an image of the calibration component is calibrated according to the adjacent laser radars to obtain a calibration initial value.
3. The method of claim 2, wherein the plurality of lidar calibration pose relationships on the elevator car toward each side of the inner wall of the elevator hoistway, further comprises:
and according to the initial calibration value, acquiring an accurate calibration value by adopting an iterative nearest point algorithm.
4. The method for real-time modeling and monitoring of an elevator hoistway according to claim 1, wherein the splicing the laser radar point cloud data at adjacent moments to obtain the initial value of the pose change comprises:
judging the pose of the point cloud at the adjacent moment according to the running speed of the elevator to obtain the initial value of the pose change; the up-down translation change amount during elevator operation is calculated by multiplying the elevator operation speed by the operation time, and the translation and rotation change amounts in other directions during elevator operation are set to be 0.
5. The method according to claim 4, wherein the calculating the PCA result of each grid, extracting all planes in the lidar point cloud data according to the feature value of the PCA result, as the first plane parameters, includes:
calculating the PCA result of each grid to obtain three characteristic values of the PCA result, wherein the maximum value of the three characteristic values is a, the middle value is b, and the minimum value is c, if any one of a-b is less than or equal to k1 and a/b is less than or equal to k2 and any one of b-c is more than or equal to k3 and b/c is more than or equal to k4 is met, judging that the laser radar point cloud data in the grid is plane-like point cloud data, and otherwise, judging that the laser radar point cloud data is non-plane point cloud data;
where k1, k2, k3, k4 are constants.
6. The method according to claim 5, wherein the calculating the PCA result of each grid, extracting all planes in the lidar point cloud data according to the feature value of the PCA result, and using the extracted planes as the first plane parameters, further includes:
searching grids around the grids corresponding to the plane-like point cloud data, calculating PCA results again, guiding to complete calculation of all grids, merging all plane-like point cloud data, and ignoring all non-plane-like point cloud data to obtain first plane parameters.
7. The method according to claim 1, wherein the matching is performed according to the initial pose change value and the first plane parameter, and the matching cost is calculated by a function, so as to obtain the initial pose change value with the minimum matching cost, including:
calculating Euclidean distance from a point P1 in the first plane parameter at the moment T1 to a surface A2 closest to the P1 in the first plane parameter at the moment T2, and taking the sum of Euclidean distances from all the points P1 to the surface A2 as a matching cost;
and setting a search range based on the pose change initial value, and searching the pose change initial value with the minimum matching cost in the search range.
8. The method of claim 7, wherein the setting the search range based on the initial value of the pose change comprises:
setting a search range within an X-direction + -L1 range, a Y-direction + -L2 range, a Z-direction + -L3 range, and a rotation + -alpha angle range along an X-axis, a rotation + -beta angle range along a Y-axis, and a rotation + -gamma angle range along a Z-axis of the initial pose change value;
wherein L1, L2, L3, alpha, beta, gamma are constants.
9. The method of real-time modeling and monitoring of an elevator hoistway of claim 1, further comprising:
setting a D1 time period, and eliminating the scanning modeling result before the D1 time.
10. Elevator hoistway real-time modeling and monitoring device based on a plurality of area array laser radars, which is used for realizing the elevator hoistway real-time modeling and monitoring method according to any one of claims 1-9.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310749209.7A CN116626702A (en) | 2023-06-25 | 2023-06-25 | Elevator hoistway real-time modeling monitoring method based on multiple area array laser radars |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310749209.7A CN116626702A (en) | 2023-06-25 | 2023-06-25 | Elevator hoistway real-time modeling monitoring method based on multiple area array laser radars |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116626702A true CN116626702A (en) | 2023-08-22 |
Family
ID=87641911
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310749209.7A Pending CN116626702A (en) | 2023-06-25 | 2023-06-25 | Elevator hoistway real-time modeling monitoring method based on multiple area array laser radars |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116626702A (en) |
-
2023
- 2023-06-25 CN CN202310749209.7A patent/CN116626702A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102460065B (en) | Information processing apparatus and information processing method | |
| AU664393B2 (en) | Method and system for point by point measurement of spatial coordinates | |
| US5621807A (en) | Intelligent range image camera for object measurement | |
| CN107339935B (en) | Target space intersection measuring method for full-view scanning measuring system | |
| Luna et al. | Calibration of line-scan cameras | |
| CN103559711A (en) | Motion estimation method based on image features and three-dimensional information of three-dimensional visual system | |
| CN110208771B (en) | Point cloud intensity correction method of mobile two-dimensional laser radar | |
| US10527416B2 (en) | System and method for measuring a displacement of a mobile platform | |
| CN111913169B (en) | Laser radar internal reference and point cloud data correction method, device and storage medium | |
| CN112017248B (en) | 2D laser radar camera multi-frame single-step calibration method based on dotted line characteristics | |
| Aldao et al. | Metrological comparison of LiDAR and photogrammetric systems for deformation monitoring of aerospace parts | |
| García-Moreno et al. | Error propagation and uncertainty analysis between 3D laser scanner and camera | |
| CN108257184B (en) | Camera attitude measurement method based on square lattice cooperative target | |
| CN112525106B (en) | Three-phase machine cooperative laser-based 3D detection method and device | |
| Zhang et al. | Precise on-line non-target pose measurement for cylindrical components based on laser scanning | |
| WO2023142608A1 (en) | System and method for obtaining aircraft profile | |
| CN112508933A (en) | Flexible mechanical arm movement obstacle avoidance method based on complex space obstacle positioning | |
| CN109990734A (en) | Depth information camera module precision automatic checkout system and its accuracy checking method | |
| CN114078163A (en) | Precise calibration method for laser radar and visible light camera | |
| CN116626702A (en) | Elevator hoistway real-time modeling monitoring method based on multiple area array laser radars | |
| US20230280451A1 (en) | Apparatus and method for calibrating three-dimensional scanner and refining point cloud data | |
| Iovenitti et al. | Three-dimensional measurement using a single image | |
| CN113888693A (en) | High-precision point cloud data reconstruction method | |
| Di Leo et al. | Uncertainty of line camera image based measurements | |
| CN115797185B (en) | Coordinate conversion method based on image processing and complex sphere |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |