CN113281777A - Dynamic measuring method and device for cargo volume - Google Patents
Dynamic measuring method and device for cargo volume Download PDFInfo
- Publication number
- CN113281777A CN113281777A CN202110370457.1A CN202110370457A CN113281777A CN 113281777 A CN113281777 A CN 113281777A CN 202110370457 A CN202110370457 A CN 202110370457A CN 113281777 A CN113281777 A CN 113281777A
- Authority
- CN
- China
- Prior art keywords
- cargo
- point cloud
- data
- goods
- laser radars
- 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.)
- Granted
Links
Images
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
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
- G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
-
- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Landscapes
- Physics & Mathematics (AREA)
- Engineering & Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Computer Networks & Wireless Communication (AREA)
- Electromagnetism (AREA)
- Radar, Positioning & Navigation (AREA)
- Remote Sensing (AREA)
- Geometry (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Theoretical Computer Science (AREA)
- Length Measuring Devices By Optical Means (AREA)
Abstract
The embodiment of the invention discloses a dynamic cargo volume measuring method, which comprises the steps of starting three laser radars and calibrating according to mutual postures of the three laser radars, so that data collected by the three laser radars are unified to a same coordinate system; judging whether goods exist on the forklift or not, if so, measuring the goods by the three laser radars to obtain measurement data; performing three-dimensional reconstruction according to the measurement data to obtain a cargo point cloud model; and measuring the cargo volume according to the cargo point cloud model. Compared with the prior art, the method and the device have the advantages of being fast, simple, convenient, efficient, large in measurement range and capable of accurately measuring the cargo volume. The embodiment of the invention also provides a dynamic cargo volume measuring device which also has the effects.
Description
Technical Field
The invention relates to the technical field of surveying and mapping, in particular to a dynamic cargo volume measuring method and a measuring device thereof.
Background
In the logistics industry, sometimes charging is needed according to the volume of an object, so that the volume of collected goods needs to be measured; or when the goods are stored in a warehouse and transported and loaded, the volume of the goods needs to be measured if the next batch of goods can be loaded in the warehouse or the carriage.
Disclosure of Invention
In view of the above technical problems, embodiments of the present invention provide a method and a device for dynamically measuring a cargo volume.
A first aspect of an embodiment of the present invention provides a method for dynamically measuring a cargo volume, including the following steps:
starting the three laser radars and calibrating according to mutual postures of the three laser radars, so that data collected by the three laser radars are unified to a same coordinate system;
judging whether goods exist on the forklift or not, if so, measuring the goods by the three laser radars to obtain measurement data;
performing three-dimensional reconstruction according to the measurement data to obtain a cargo point cloud model;
and measuring the cargo volume according to the cargo point cloud model.
Optionally, the dynamic cargo volume measuring method further includes:
starting a depth camera and the laser radar to acquire data;
each of the lidar is converted to the same coordinate by the depth camera and the collected data.
Optionally, the step of converting each of the lidar data into the same coordinate by the depth camera and the collected data includes:
fixing the depth camera to calculate a transformation matrix from a single laser radar coordinate system to a depth camera coordinate system;
and sequentially calibrating to obtain transformation matrixes from the two laser radars to the depth camera, and further unifying the data collected by the three laser radars to the same coordinate system.
Optionally, the transformation matrix is
Where the top left corner of the matrix is a 3 x 3 rotation matrix, the top right corner is a 3 x 1 displacement vector, and the bottom left corner is a 1 x 3 scaling vector.
Optionally, the step of determining whether the forklift has a cargo includes:
through laser radar detects the light curtain and whether is sheltered from, if the light curtain is sheltered from then judges there is the goods.
Optionally, the distance from the cargo to the farthest of the three lidar is less than 10 meters, which is valid data.
Optionally, the step of performing three-dimensional reconstruction according to the measurement data to obtain a cargo point cloud model includes:
two-dimensional coordinates x and y are established by data detected by the two laser radars;
and the other laser radar acquires the position and the speed of the goods in real time to establish a z axis and form three-dimensional point cloud data.
Optionally, after the step of performing three-dimensional reconstruction according to the calibration data to obtain a cargo point cloud model, the method further includes:
and after the point cloud of the boundary part of the cargo point cloud model is projected to a plane, the size of the bottom surface of the cargo is obtained, and the cargo range point cloud in the plane is filled up, so that the point cloud is closed to obtain a complete cargo point cloud model.
Optionally, the step of measuring the cargo volume according to the cargo point cloud model comprises:
triangulating the cargo point cloud model using a vtkTriangleFilter class of vtk library;
the volume of the cargo point cloud model is obtained using the GetVolume method provided by the vtkMassProperties class.
The second aspect of the embodiment of the invention provides a dynamic cargo volume measuring device, which comprises a detection frame, a portal frame and a light curtain arranged in a detection area, wherein two laser radars with acute detection ray angles are arranged on the detection frame, and the portal frame is provided with another laser radar and a depth camera; wherein three of the lidar, the depth camera and the light curtain cooperate with each other to perform the cargo volume dynamic measurement method as described above.
The embodiment of the invention provides a dynamic cargo volume measuring method which comprises the steps of starting three laser radars and calibrating according to mutual postures of the three laser radars, so that data collected by the three laser radars are unified to a same coordinate system; judging whether goods exist on the forklift or not, if so, measuring the goods by the three laser radars to obtain measurement data; performing three-dimensional reconstruction according to the measurement data to obtain a cargo point cloud model; and measuring the cargo volume according to the cargo point cloud model. Compared with the prior art, the method and the device have the advantages of being fast, simple, convenient, efficient, large in measurement range and capable of accurately measuring the cargo volume. The embodiment of the invention also provides a dynamic cargo volume measuring device which also has the effects.
Drawings
Fig. 1 is a schematic structural diagram of a dynamic cargo volume measuring device according to an embodiment of the present invention;
FIG. 2 is a schematic flow chart of a method for dynamically measuring cargo volume according to an embodiment of the present invention;
FIG. 3 is a schematic flow chart of a dynamic cargo volume measurement method according to another embodiment of the present invention;
fig. 4 is a schematic flow chart of a dynamic cargo volume measuring method according to another embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a device for dynamically measuring cargo volume according to an embodiment of the present invention is disclosed. The dynamic cargo volume measuring device 1 comprises a detection frame 11, a portal frame 12 and a light curtain 13 arranged in a detection area, wherein two laser radars 111 and 112 with acute detection light angles are arranged on the detection frame 11, and the portal frame 12 is provided with another laser radar 113 and a depth camera 121; three of the laser radars 111, 112, 113, the depth camera 121 and the light curtain 13 cooperate with each other to perform the above dynamic cargo volume measurement method. Wherein, the depth camera 121 is arranged in the middle of the gantry 12, and the laser radar 113 is arranged close to the depth camera 121. The three lidar sources 111, 112, 113 and the depth camera 121 are arranged at substantially the same horizontal position.
The embodiment of the invention provides a dynamic cargo volume measuring method and a dynamic cargo volume measuring device.
Specifically, the dynamic cargo volume measuring device 1 includes a detection frame 11, a portal frame 12 and a light curtain 13 disposed in a detection area. Two laser radars 111 and 112 with acute detection light angles are arranged on the detection frame 11, and the two laser radars collect the distances from different positions of the goods to the laser radars from different angles. Another laser radar 113 and a depth camera 121 are arranged on the gantry 12, and the laser radar on the gantry acquires data from other positions of the goods to the laser radar from another angle. Three of the laser radars 111, 112, 113, the depth camera 121 and the light curtain 13 cooperate with each other to perform the above dynamic cargo volume measurement method. Wherein, the depth camera 121 is arranged in the middle of the gantry 12, and the laser radar 113 is arranged close to the depth camera 121. Specifically, in the process of receiving detection by the forklift, the forklift firstly passes below the gantry 12 and then passes below the detection frame 11, and in the process of driving the forklift, the three laser radars and the depth camera finish measuring the volume of goods on the forklift, so that the dynamic measurement method for the volume of the goods is realized.
Referring to fig. 1 and 2, the cargo volume dynamic measurement method includes the following steps:
s100: and starting the three laser radars and calibrating according to the mutual postures of the three laser radars, so that the data collected by the three laser radars are unified to the same coordinate system.
In particular, the three lidar is a single line lidar. And calibrating according to the mutual postures of the three laser radars, and preparing for measuring the goods by the three laser radars subsequently.
Understandably, the three laser radars are in different positions and postures relative to the cargo, so that the relevant data of the cargo can be collected from three different angles. However, the data acquired by the three laser radars are not in the same coordinate system, so calibration is needed, and the data acquired by the three laser radars are converted into the same coordinate system to prepare for the subsequent steps.
S110: and judging whether goods exist on the forklift, if so, measuring the goods by the three laser radars to obtain measurement data.
It can be understood that when the forklift passes below the portal frame, whether goods exist on the forklift is judged firstly, if so, the subsequent steps are carried out, and if not, the detection result can be directly given, namely the accommodating volume of the compartment is the volume capable of accommodating the goods again.
Specifically, a light-emitting device is arranged at the lower part of the dynamic cargo volume measuring device, light emitted by the light-emitting device forms a light curtain, and the light curtain irradiates on the goods in the process that the forklift passes through the portal frame and the detection frame.
Further, the step of judging whether have the goods on the fork truck includes through laser radar detection light curtain whether sheltered from, if the light curtain is sheltered from then judge there is the goods, if the light curtain does not sheltered from then say there is not the goods, need not to carry out the detection at the back again.
Furthermore, in one embodiment, the farthest distance from the cargo to the three laser radars is less than 10 meters, which is used as valid data to eliminate some interference data, so as to ensure the data to be true and valid. It will be appreciated that the furthest distance of the cargo from the three lidar units is less than 10 meters, for example any natural number of 1 meter, 2 meters, 3 meters, 5 meters, 7 meters, 8 meters, 9 meters, etc. A lidar placed in front of the cargo can calculate the position of the cargo. When goods pass through the laser radar measuring area, the laser radar can acquire the data of the set measuring area. To increase the stability of the system, it is known whether goods have passed the inspection area using a light curtain installed on the ground. When the light emitted by the light curtain is blocked, it is proved that something is present in the area of the light curtain, and therefore it is known that goods are present in the measuring area. For example, the set distance data collected is valid data within 10m from the laser radar, so that the valid data of the goods can be collected only when the goods are less than 10m from the laser radar, and the goods in the measurement area can be known.
S120: and performing three-dimensional reconstruction according to the measurement data to obtain a cargo point cloud model.
As can be understood, the three laser radars acquire the related data of the goods from different angles, so that the data of different dimensions of the goods can be obtained, and a three-dimensional point cloud model of the goods is constructed.
Specifically, in an embodiment, the step of performing three-dimensional reconstruction according to the measurement data to obtain a cargo point cloud model includes: wherein the data detected by the two lidar sets up two-dimensional coordinates x, y. According to the method, when the forklift walks, the three laser radars simultaneously acquire data, and the other laser radar acquires the position and the speed of the goods in real time to establish a z axis to form three-dimensional point cloud data.
Further, the laser radar 113 disposed in front of the cargo can calculate the position of the cargo. For example, the laser radar 113 may obtain the position and velocity of the cargo in real time as the forklift moves with the cargo. Two laser radars 111, 112 that set up on testing stand 11 can be according to the goods positional information that laser radar 113 provided in the front and the data of self collection, convert the two-dimensional information of original single line laser radar into the three-dimensional point cloud of goods, accomplish the three-dimensional process of rebuilding of whole goods. Two laser radars 111, 112 arranged above the detection frame 11 at this time give the x, y axis coordinates of the coordinate system of the point cloud itself. In addition, the cargo position information given by the laser radar 113 arranged in front of the cargo is taken as the z-axis coordinate, so that 3-dimensional point cloud under the respective coordinate systems of the two laser radars can be obtained. In the calibration step, transformation matrices from each lidar to each other in a coordinate system can be obtained, and at this time, the corresponding transformation matrix needs to be multiplied by point cloud data obtained by one of the lidar, so that the point cloud data can be unified to the same coordinate system through a point cloud transformation formula. The point cloud transformation formula is as follows:
s130: and measuring the cargo volume according to the cargo point cloud model.
Specifically, after the cargo point cloud model is established, the cargo volume can be obtained by using a corresponding algorithm according to the cargo point cloud model. The volume rate of the carriage can be obtained after the volume of the goods is accumulated, and the reloading capacity of the carriage can be further judged. Further, the step of measuring the cargo volume from the cargo point cloud model comprises triangulating the cargo point cloud model using a vtkttrianglefilter class of vtk library; the volume of the cargo point cloud model is obtained by using a GetVolume method provided by a vtkMassProperties class.
Referring to fig. 3, a method for dynamically measuring cargo volume according to an embodiment of the present invention is disclosed. The dynamic cargo volume measuring method further comprises the following steps:
s200: and starting the depth camera and the laser radar to acquire data.
Specifically, a depth camera and a laser radar are started for data acquisition. The depth camera and the laser radar can be simultaneously started for data acquisition when the mutual postures among the three laser radars are calibrated. The depth camera may also be started to perform data acquisition after calibrating the mutual attitude among the three laser radars, which is not particularly limited herein.
S210: each lidar is converted to the same coordinates by the depth camera and the acquired data.
Specifically, the step of converting each lidar to the same coordinate through the depth camera and the collected data comprises fixing the depth camera to a transformation matrix which calculates a single lidar coordinate system to a depth camera coordinate system; and calibrating in sequence to obtain transformation matrixes from the two laser radars to the depth camera, and further unifying data collected by the three laser radars to the same coordinate system.
Transform the matrix into
Wherein the top left corner of the transformation matrix is a 3 × 3 rotation matrix, the top right corner is a 3 × 1 displacement vector, and the bottom left corner is a 1 × 3 scaling vector. Where the bottom left corner is a 1 × 3 scaling vector, which is all zero since the transformation of the coordinate system is a rigid transformation and there is no scaling.
And repeating the steps, and calibrating the three laser radars and the depth camera respectively to obtain a transformation matrix from the three laser radar coordinate systems to the depth camera coordinate system. And calculating to obtain the transformation matrix between the laser radars according to the transformation matrix from each laser radar to the camera obtained by calibration. The data of the three laser radars can be unified into the same coordinate system.
Referring to fig. 4, the method for dynamically measuring a cargo volume according to an embodiment of the present invention, after the step of performing three-dimensional reconstruction according to calibration data to obtain a cargo point cloud model, further includes:
s300: after the point cloud of the boundary part of the goods point cloud model is projected to a plane, the size of the bottom surface of the goods is obtained, and the point clouds of the goods range in the plane are filled up, so that the point cloud is closed to obtain a complete goods point cloud model.
It is understood that the cargo point cloud model constructed from data collected by three lidar systems is only preliminary and poor at the bottom of the model. Specifically, the cargo point cloud obtained after the three-dimensional reconstruction is the point cloud of the missing cargo bottom surface, and the volume measurement is performed after the completion of the bottom surface point cloud supplementation. The light curtain arranged at the bottom of the portal frame can obtain the height of the goods from the ground. At this level, a plane parallel to the ground is established, which is the plane of the bottom surface of the cargo. And projecting the point cloud of the edge of the goods onto the plane to obtain the size of the bottom surface of the specific goods, and completing the point cloud supplementation of the range of the goods in the plane to obtain closed point cloud.
According to the technical scheme, the number of the point cloud can be collected, the laser radar frame rate can reach 50Hz, the angle resolution is 0.36 degrees, and therefore more point cloud data are collected and generated. The measuring range is big, and depth camera is just relatively poor at the depth distance information after exceeding 3 meters, and current solid-state laser radar is because the problem of resolution ratio, and the size error can increase when the goods size of measurement is too big. The technical scheme of the application can well solve the two problems by using the three single-line laser radar measuring methods. The laser radar measuring distance can reach dozens of meters, and even hundreds of meters can obtain higher precision. The current solid state laser radar is not enough in the precision of resolution ratio in the vertical direction, and the system that this application adopted three single line laser radar to constitute is by the velocity of motion and the laser radar frame rate decision of goods, therefore the precision of resolution ratio is far higher than solid state laser radar in the vertical direction of this application technical scheme. Compared with the prior art, the method and the device have the advantages of being fast, simple, convenient, efficient, large in measurement range and capable of accurately measuring the cargo volume.
The above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A dynamic measurement method for cargo volume is characterized by comprising the following steps:
starting the three laser radars and calibrating according to mutual postures of the three laser radars, so that data collected by the three laser radars are unified to a same coordinate system;
judging whether goods exist on the forklift or not, if so, measuring the goods by the three laser radars to obtain measurement data;
performing three-dimensional reconstruction according to the measurement data to obtain a cargo point cloud model;
and measuring the cargo volume according to the cargo point cloud model.
2. The dynamic cargo volume measurement method according to claim 1, further comprising:
starting a depth camera and the laser radar to acquire data;
each of the lidar is converted to the same coordinate by the depth camera and the collected data.
3. The method of claim 2, wherein said step of converting each of said lidar data to the same coordinate system via said depth camera and said collected data comprises:
fixing the depth camera to calculate a transformation matrix from a single laser radar coordinate system to a depth camera coordinate system;
and sequentially calibrating to obtain transformation matrixes from the two laser radars to the depth camera, and further unifying the data collected by the three laser radars to the same coordinate system.
4. The dynamic cargo volume measurement method according to claim 3, wherein the transformation matrix is
Where the top left corner of the matrix is a 3 x 3 rotation matrix, the top right corner is a 3 x 1 displacement vector, and the bottom left corner is a 1 x 3 scaling vector.
5. The dynamic cargo volume measurement method according to claim 1,
the step of judging whether the forklift is provided with goods comprises the following steps:
through laser radar detects the light curtain and whether is sheltered from, if the light curtain is sheltered from then judges there is the goods.
6. The dynamic cargo volume measurement method according to claim 5,
and the distance from the cargo to the farthest distance of the three laser radars is less than 10 meters, so that effective data are obtained.
7. The dynamic cargo volume measurement method of claim 1, wherein the step of performing a three-dimensional reconstruction from the measurement data to obtain a cargo point cloud model comprises:
two-dimensional coordinates x and y are established by data detected by the two laser radars;
and the other laser radar acquires the position and the speed of the goods in real time to establish a z axis and form three-dimensional point cloud data.
8. The method for dynamically measuring cargo volume according to claim 1, wherein the step of performing three-dimensional reconstruction from the calibration data to obtain a cargo point cloud model further comprises:
and after the point cloud of the boundary part of the cargo point cloud model is projected to a plane, the size of the bottom surface of the cargo is obtained, and the cargo range point cloud in the plane is filled up, so that the point cloud is closed to obtain a complete cargo point cloud model.
9. The dynamic cargo volume measurement method according to claim 1,
the step of measuring the cargo volume according to the cargo point cloud model comprises:
triangulating the cargo point cloud model using a vtkTriangleFilter class of vtk library;
the volume of the cargo point cloud model is obtained using the GetVolume method provided by the vtkMassProperties class.
10. A dynamic cargo volume measuring device is characterized by comprising a detection frame, a portal frame and a light curtain arranged in a detection area, wherein two laser radars with acute detection light angles are arranged on the detection frame, and the portal frame is provided with another laser radar and a depth camera; wherein three of the lidar, the depth camera, and the light curtain cooperate to perform the method of dynamic cargo volume measurement of any of claims 1-9 above.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110370457.1A CN113281777B (en) | 2021-04-07 | 2021-04-07 | Cargo volume dynamic measurement method and measurement device thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110370457.1A CN113281777B (en) | 2021-04-07 | 2021-04-07 | Cargo volume dynamic measurement method and measurement device thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN113281777A true CN113281777A (en) | 2021-08-20 |
| CN113281777B CN113281777B (en) | 2024-10-22 |
Family
ID=77276507
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110370457.1A Active CN113281777B (en) | 2021-04-07 | 2021-04-07 | Cargo volume dynamic measurement method and measurement device thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113281777B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114061445A (en) * | 2021-10-20 | 2022-02-18 | 成都利君环际智能装备科技有限公司 | Carriage size measurement system calibration method based on multiple three-dimensional scanning devices |
| CN114494453A (en) * | 2021-12-29 | 2022-05-13 | 劢微机器人科技(深圳)有限公司 | Automatic loading and unloading method and automatic loading and unloading system based on radar and camera |
| CN114779273A (en) * | 2022-05-05 | 2022-07-22 | 京东科技信息技术有限公司 | Method and device for detecting remaining cargo volume |
| CN115930791A (en) * | 2022-12-02 | 2023-04-07 | 湖北润铁轨道装备有限公司 | Multi-mode data container cargo position and size detection method |
| CN116449393A (en) * | 2023-06-15 | 2023-07-18 | 天津美腾科技股份有限公司 | Multi-sensor measurement method and system for large and medium-sized stockpiles |
Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102155913A (en) * | 2011-03-07 | 2011-08-17 | 湖南新航程智能测控技术有限公司 | Method and device for automatically measuring coal pile volume based on image and laser |
| CN103411531A (en) * | 2013-07-09 | 2013-11-27 | 山东科技大学 | Volume dynamic measuring device based on laser scanning radars and measuring method thereof |
| CN107037438A (en) * | 2016-02-04 | 2017-08-11 | 梅特勒-托莱多有限公司 | The apparatus and method of the size of the object carried for the vehicle for determining to move in measured zone |
| CN109160452A (en) * | 2018-10-23 | 2019-01-08 | 西安中科光电精密工程有限公司 | Unmanned transhipment fork truck and air navigation aid based on laser positioning and stereoscopic vision |
| CN109948189A (en) * | 2019-02-19 | 2019-06-28 | 江苏徐工工程机械研究院有限公司 | Material volume and weight measuring system for excavator bucket |
| WO2019242174A1 (en) * | 2018-06-21 | 2019-12-26 | 华南理工大学 | Method for automatically detecting building structure and generating 3d model based on laser radar |
| CN111310740A (en) * | 2020-04-14 | 2020-06-19 | 深圳市异方科技有限公司 | Pedestrian luggage volume measuring device under motion condition |
| CN111429507A (en) * | 2020-04-14 | 2020-07-17 | 深圳市异方科技有限公司 | Volume measurement device based on multiple 3D lenses |
| CN111442814A (en) * | 2020-05-13 | 2020-07-24 | 武汉市异方体科技有限公司 | Device for non-contact measurement of drainage volume of special-shaped object |
| US20200394813A1 (en) * | 2019-06-17 | 2020-12-17 | SafeAI, Inc. | Techniques for volumetric estimation |
-
2021
- 2021-04-07 CN CN202110370457.1A patent/CN113281777B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102155913A (en) * | 2011-03-07 | 2011-08-17 | 湖南新航程智能测控技术有限公司 | Method and device for automatically measuring coal pile volume based on image and laser |
| CN103411531A (en) * | 2013-07-09 | 2013-11-27 | 山东科技大学 | Volume dynamic measuring device based on laser scanning radars and measuring method thereof |
| CN107037438A (en) * | 2016-02-04 | 2017-08-11 | 梅特勒-托莱多有限公司 | The apparatus and method of the size of the object carried for the vehicle for determining to move in measured zone |
| WO2019242174A1 (en) * | 2018-06-21 | 2019-12-26 | 华南理工大学 | Method for automatically detecting building structure and generating 3d model based on laser radar |
| CN109160452A (en) * | 2018-10-23 | 2019-01-08 | 西安中科光电精密工程有限公司 | Unmanned transhipment fork truck and air navigation aid based on laser positioning and stereoscopic vision |
| CN109948189A (en) * | 2019-02-19 | 2019-06-28 | 江苏徐工工程机械研究院有限公司 | Material volume and weight measuring system for excavator bucket |
| US20200394813A1 (en) * | 2019-06-17 | 2020-12-17 | SafeAI, Inc. | Techniques for volumetric estimation |
| CN111310740A (en) * | 2020-04-14 | 2020-06-19 | 深圳市异方科技有限公司 | Pedestrian luggage volume measuring device under motion condition |
| CN111429507A (en) * | 2020-04-14 | 2020-07-17 | 深圳市异方科技有限公司 | Volume measurement device based on multiple 3D lenses |
| CN111442814A (en) * | 2020-05-13 | 2020-07-24 | 武汉市异方体科技有限公司 | Device for non-contact measurement of drainage volume of special-shaped object |
Non-Patent Citations (2)
| Title |
|---|
| 张巍 等: "基于激光扫描的体积测量系统", 计算机系统应用, vol. 29, no. 04, 30 April 2020 (2020-04-30), pages 107 - 112 * |
| 范亮;: "地面激光雷达技术在土方变化量监测中的应用", 测绘与空间地理信息, no. 11 * |
Cited By (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114061445A (en) * | 2021-10-20 | 2022-02-18 | 成都利君环际智能装备科技有限公司 | Carriage size measurement system calibration method based on multiple three-dimensional scanning devices |
| CN114061445B (en) * | 2021-10-20 | 2024-05-03 | 成都利君环际智能装备科技有限公司 | Compartment size measurement system calibration method based on multiple three-dimensional scanning equipment |
| CN114494453A (en) * | 2021-12-29 | 2022-05-13 | 劢微机器人科技(深圳)有限公司 | Automatic loading and unloading method and automatic loading and unloading system based on radar and camera |
| CN114779273A (en) * | 2022-05-05 | 2022-07-22 | 京东科技信息技术有限公司 | Method and device for detecting remaining cargo volume |
| CN115930791A (en) * | 2022-12-02 | 2023-04-07 | 湖北润铁轨道装备有限公司 | Multi-mode data container cargo position and size detection method |
| CN115930791B (en) * | 2022-12-02 | 2024-01-12 | 湖北润铁轨道装备有限公司 | Multi-mode data container cargo position and size detection method |
| CN116449393A (en) * | 2023-06-15 | 2023-07-18 | 天津美腾科技股份有限公司 | Multi-sensor measurement method and system for large and medium-sized stockpiles |
| CN116449393B (en) * | 2023-06-15 | 2023-09-22 | 天津美腾科技股份有限公司 | Multi-sensor measurement method and system for large and medium-sized stockpiles |
Also Published As
| Publication number | Publication date |
|---|---|
| CN113281777B (en) | 2024-10-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113281777A (en) | Dynamic measuring method and device for cargo volume | |
| CN107340522B (en) | Laser radar positioning method, device and system | |
| KR102559661B1 (en) | Volumetric methods, systems, equipment and computer readable storage media | |
| US8711214B2 (en) | Position and orientation measurement apparatus, position and orientation measurement method, and storage medium | |
| Soudarissanane et al. | Optimizing terrestrial laser scanning measurement set-up | |
| CN112034431B (en) | External parameter calibration method and device for radar and RTK | |
| CN111046776A (en) | Mobile robot traveling path obstacle detection method based on depth camera | |
| US20150022640A1 (en) | Method and system for volume determination using a structure from motion algorithm | |
| US20120114181A1 (en) | Vehicle pose estimation and load profiling | |
| JP2008309533A (en) | Method and apparatus for measuring vehicle shape | |
| EP3435028B1 (en) | Live metrology of an object during manufacturing or other operations | |
| CN113295089B (en) | Carriage volume rate measuring method based on visual inertia SLAM | |
| WO2022217988A1 (en) | Sensor configuration scheme determination method and apparatus, computer device, storage medium, and program | |
| CN103176180B (en) | Device and method for detecting positioning precision of high-precision airborne laser radar system | |
| JP2021038939A (en) | Calibration device | |
| CN113959331A (en) | Object packaging volume measuring method and device | |
| Moller et al. | An automatic evaluation procedure for 3-D scanners in robotics applications | |
| CN118518009B (en) | Calibration parameter determining method, calibration method, medium and equipment | |
| Yamada et al. | Probability-Based LIDAR–Camera Calibration Considering Target Positions and Parameter Evaluation Using a Data Fusion Map | |
| CN117346689A (en) | Method and apparatus for detecting contour measuring system and readable storage medium | |
| Fritsch et al. | Multi-sensors and multiray reconstruction for digital preservation | |
| US20230035883A1 (en) | Filling rate measurement method, information processing device, and recording medium | |
| CN110058211B (en) | Method and device for acquiring calibration initial value of vehicle-mounted LiDAR measurement system | |
| CN114791610A (en) | A method and device for determining conversion parameters | |
| Koyuncu et al. | Development of an optical 3D scanner based on structured light |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |