CN112285676B - Laser radar and IMU external parameter calibration method and device - Google Patents
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Abstract
The application relates to a laser radar and IMU external parameter calibration method and device, which belong to the technical field of computers, and the method comprises the following steps: acquiring point cloud data acquired by a laser radar and inertial measurement data acquired by an IMU; performing point cloud matching calculation on the current laser radar pose information based on the point cloud data; calculating current IMU pose information based on the inertial measurement data; aligning the laser radar pose information and the IMU pose information based on the time stamp to obtain an aligned pose information set; determining an extrinsic reference value based on the aligned pose information group by using a RANSAC algorithm; the problem of poor stability and accuracy caused by performing external parameter calibration by using the hand-eye calibration equation only can be solved; because the RANSAC algorithm can well remove the influence of abnormal pose in the hand and eye mark positioning pose on the result, the accuracy and stability of the calibration result can be improved.
Description
Technical Field
The application relates to a laser radar and IMU external parameter calibration method and device, and belongs to the technical field of computers.
Background
Mapping and positioning are key technologies for realizing automatic driving of unmanned vehicles. In actual operation of the vehicle, the use of a single sensor for measurement may result in a large measurement error due to limitations of the sensor itself. Currently, the approach of using laser radar and inertial measurement unit (Inertial Measurement Unit, IMU) fusion is the dominant research direction in the industry at present. Because different sensors are respectively assembled at different positions on the carrier, and the attitude angles during installation are possibly inconsistent, calibrating the deviation of the accurate position and attitude angle between the two sensors (namely, the external parameters of the sensors) is an important basis for realizing the fusion of the sensor parameters.
When the external parameters of the laser radar and the IMU are calibrated, the hand-eye calibration equation is used for calibrating, namely, the motion information of the laser radar and the IMU at a specific moment is solved respectively, then the constraint between the pose of the laser radar and the IMU is utilized for establishing a target equation, and a result is obtained by optimizing the target equation.
However, the obtained sensor mileage information contains a lot of abnormal data, and the calibration result obtained by directly solving the sensor mileage information by using the result is unstable and has a large error.
Disclosure of Invention
The application provides a laser radar and IMU external parameter calibration method and device, which can solve the problem of poor stability and accuracy caused by performing external parameter calibration by using an eye calibration equation. The application provides the following technical scheme:
in a first aspect, a method for calibrating external parameters of a laser radar and an IMU is provided, the method comprising:
acquiring point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU;
calculating current laser radar pose information based on the point cloud data;
calculating current IMU pose information based on the inertial measurement data;
aligning the laser radar pose information and the IMU pose information based on a time stamp to obtain an aligned pose information set;
and determining an extrinsic reference value based on the aligned pose information group by using the RANSAC algorithm.
Optionally, the determining, using the RANSAC algorithm, an extrinsic reference value based on the aligned pose information set includes:
the following steps are circularly executed until the circulation condition does not meet the preset condition: inputting a plurality of groups of aligned pose information groups into a hand-eye calibration equation to obtain a rough external parameter estimated value; determining a rough model score and a rough outlier set and a rough inlier set in the plurality of sets of aligned pose information sets using the RANSAC algorithm and the rough outlier estimate; when the rough model score is higher than a score threshold value, determining a parameter model corresponding to the rough external parameter estimation value as a candidate parameter model;
and circularly executing the following steps until a unique parameter model is screened out from a plurality of candidate parameter models, and stopping to obtain an external parameter calibration value corresponding to the parameter model: inputting the rough inner point set into the hand-eye calibration equation again for the rough inner point set corresponding to each candidate parameter model to obtain a fine outer parameter estimated value; re-determining a fine model score, and a fine outlier set and a fine inlier set in the coarse inlier set using the RANSAC algorithm and the fine outlier estimate; and screening out candidate parameter models which do not meet the model conditions based on the fine model scores.
Optionally, the preset condition is that the execution times reach preset times; or the number of candidate parameter models reaches the preset number.
Optionally, the model condition is that after each candidate parameter model is ranked according to the fine model score from high to low, the candidate parameter models in the first N bits are ranked, wherein n=1/N, and N is the number of candidate parameter models.
Optionally, the aligning the laser radar pose information and the IMU pose information based on the time stamp to obtain an aligned pose information set includes:
acquiring a target time stamp corresponding to pose information of each frame of laser radar;
determining two time stamps adjacent to the target time stamp from the time stamps of the IMU pose information;
and performing interpolation compensation on the IMU pose information according to the difference value between the target timestamp and the determined two IMU adjacent timestamps to obtain the pose information set aligned to the target timestamp.
Optionally, after the acquiring the point cloud data acquired by the laser radar and the inertial measurement data acquired by the IMU, the method further includes:
preprocessing the point cloud data to remove noise points;
and carrying out downsampling treatment on the preprocessed point cloud data to obtain processed point cloud data, wherein the processed point cloud data is used for calculating the laser radar pose information.
Optionally, the calculating the current laser radar pose information based on the point cloud data includes:
registering the processed point cloud data of the adjacent frames by using a normal distribution transformation NDT algorithm;
and obtaining the laser radar pose information according to the pose transformation integral obtained by registration.
Optionally, the calculating current IMU pose information based on the inertial measurement data includes:
and pre-integrating the inertial measurement data to obtain the IMU pose information.
In a second aspect, a laser radar and IMU external parameter calibration device is provided, the device includes:
the data acquisition module is used for acquiring point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU;
the first calculation module is used for calculating current laser radar pose information based on the point cloud data;
the second calculation module is used for calculating current IMU pose information based on the inertial measurement data;
the data alignment module is used for aligning the laser radar pose information and the IMU pose information based on the time stamp to obtain an aligned pose information group;
and the external parameter calibration module is used for determining an external parameter calibration value based on the aligned pose information group by using the RANSAC algorithm.
The beneficial effects of this application lie in: acquiring point cloud data acquired by a laser radar and inertial measurement data acquired by an IMU; calculating current laser radar pose information based on the point cloud data; calculating current IMU pose information based on the inertial measurement data; aligning the laser radar pose information and the IMU pose information based on the time stamp to obtain an aligned pose information set; determining an extrinsic reference value based on the aligned pose information group by using a RANSAC algorithm; the problem of poor stability and accuracy caused by performing external parameter calibration by using the hand-eye calibration equation only can be solved; because the RANSAC algorithm can well remove the influence of abnormal pose in the hand and eye mark positioning pose on the result, the accuracy and stability of the calibration result can be improved.
The foregoing description is only an overview of the technical solutions of the present application, and in order to make the technical means of the present application more clearly understood, it can be implemented according to the content of the specification, and the following detailed description of the preferred embodiments of the present application will be given with reference to the accompanying drawings.
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FIG. 1 is a flow chart of a laser radar and IMU external parameter calibration method provided in one embodiment of the present application;
FIG. 2 is a flow chart of a laser radar and IMU external parameter calibration method according to another embodiment of the present application;
fig. 3 is a block diagram of a laser radar and IMU external parameter calibration device according to an embodiment of the present application.
Detailed Description
The detailed description of the present application is further described in detail below with reference to the drawings and examples. The following examples are illustrative of the present application, but are not intended to limit the scope of the present application.
First, several terms referred to in this application are described.
Random sample consensus algorithm (Random Sample Consensus, RANSAC): mathematical model parameters of the data may be iteratively calculated from a set of sample data sets containing anomaly data to distinguish between normal and anomaly data and to obtain model parameters conforming to the data.
The principle of the RANSAC algorithm consists of the following steps performed in a loop:
1. the subset is randomly selected and assumed to be the intra-office point.
2. A model is fitted with the intra-office points, which is adapted to the assumed intra-office points, from which all unknown parameters can be calculated.
3. The model obtained in 2 is used for testing other data in the whole data, and if a certain point is suitable for the estimated model, the certain point is considered to be an intra-local point, and the intra-local point is expanded.
4. If there are enough points to be classified as hypothetical local points, then the estimated model is reasonable enough.
5. The model is re-estimated with all the augmented local points.
6. The model is evaluated by estimating the error rate of the local points and the model.
7. If the current model is better performing than the best model, the current model is selected as the best model, otherwise the current model is discarded.
Inertial measurement unit (Inertial Measurement Unit, IMU): the device for measuring the three-axis attitude angle and the acceleration of the object can calculate the motion attitude of the object in a period of time and can be applied to vehicle positioning.
Normal distribution transformation algorithm (Normal Distributed Transform, NDT): the point cloud registration algorithm is applied to a statistical model of three-dimensional point clouds, and an optimal matching between the two point clouds is determined by using an iterative optimization method, so that corresponding pose transformation is obtained.
Calibrating the hand and the eye: the method is used for calibrating rigid coordinate conversion between the manipulator end camera and the manipulator end at the earliest time. Under the world coordinate system, for motion at any moment, the tail end pose A of the manipulator and the camera pose B both meet motion constraint AX=XB, and the external parameter X can be obtained by optimizing and solving the target equation.
Alternatively, the present application describes, as an example, the execution subject of each embodiment as a control device, which may be installed in a vehicle or a device independent from the vehicle, and the embodiment does not limit the implementation manner of the control device. The control device is in communication connection with the laser radar and the IMU in the current vehicle, and can acquire point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU.
Fig. 1 is a flowchart of a laser radar and IMU external parameter calibration method according to an embodiment of the present application. The method at least comprises the following steps:
and step 101, acquiring point cloud data acquired by a laser radar and inertial measurement data acquired by an IMU.
The frequencies at which the lidar and IMU collect data are typically different, and typically the frequency at which the lidar collects is lower than the frequency at which the IMU collects. In other words, the acquisition speed of the IMU is faster than that of the lidar. Based on the principle, the control device does not acquire point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU at the same time.
Among other things, inertial measurement data includes, but is not limited to: acceleration values and angle values. Of course, the inertial measurement data may also include other parameters, which are not listed here.
Step 102, calculating current laser radar pose information based on the point cloud data.
Optionally, after acquiring the point cloud data acquired by the laser radar and the inertial measurement data acquired by the IMU, preprocessing the point cloud data to remove noise points; and carrying out downsampling treatment on the preprocessed point cloud data to obtain processed point cloud data, wherein the processed point cloud data is used for calculating laser radar pose information. At this time, calculating current laser radar pose information based on the point cloud data includes: registering the processed point cloud data of the adjacent frames by using an NDT algorithm; and obtaining the laser radar pose information according to the pose transformation integral obtained by registration.
And step 103, calculating current IMU pose information based on the inertial measurement data.
Illustratively, inertial measurement data is pre-integrated to obtain IMU pose information.
The laser radar pose information and the IMU pose information are used for indicating the current position and the running angle of the vehicle.
In addition, the present embodiment does not limit the execution order of step 103 and step 102.
And 104, aligning the laser radar pose information and the IMU pose information based on the time stamp to obtain an aligned pose information group.
Each set of aligned pose information sets includes aligned laser radar pose information and IMU pose information.
Schematically, aligning the laser radar pose information and the IMU pose information based on the time stamp to obtain an aligned pose information set, including: acquiring a target time stamp corresponding to pose information of each frame of laser radar; determining two time stamps adjacent to and nearest to the target time stamp from the time stamps of the IMU pose information; and carrying out interpolation compensation on the IMU pose information according to the difference between the target timestamp and the determined two IMU adjacent timestamps to obtain a pose information group aligned to the target timestamp.
Assume that the laser radar pose information for the target timestamp t1 is X1; because the collection frequency of the IMU is very high, there are two time stamps t0 and t2 adjacent to t1, the time stamp t0 corresponds to the IMU pose information Y1, the time stamp t1 corresponds to the IMU pose information Y2, and there may be no IMU pose information corresponding to the target time stamp t 1. Therefore, in this embodiment, the IMU pose information Y corresponding to the target timestamp t1 may be estimated using the IMU pose information Y1 corresponding to the timestamp t0 and the IMU pose information Y2 corresponding to the timestamp t 1. Wherein, t0< t1< t2, Y takes the intermediate value proportionally according to the time difference, and is expressed by the following formula:
Y＝(t1-t0)/(t2-t0)*Y1+(t2-t1)/(t2-t0)*Y2。
and 105, determining an eye calibration equation and an external reference value corresponding to the eye calibration equation based on the aligned pose information set by using a RANSAC algorithm.
The method for determining the external parameter calibration values corresponding to the hand-eye calibration equation and the hand-eye calibration equation based on the aligned pose information set by using the RANSAC algorithm comprises the following steps:
the following steps are circularly executed until the circulation condition does not meet the preset condition: inputting a plurality of groups of aligned pose information groups into a hand-eye calibration equation to obtain a rough extrinsic parameter estimated value; determining a rough model score and a rough outer point set and a rough inner point set in a plurality of groups of aligned pose information groups by using a RANSAC algorithm and a rough outer parameter estimated value; when the score of the rough model is higher than the score threshold, determining a parameter model corresponding to the rough external parameter estimation value as a candidate parameter model;
the following steps are circularly executed until a unique parameter model is screened out from a plurality of candidate parameter models, and the external parameter calibration value corresponding to the parameter model is obtained: inputting the rough inner point set into the hand-eye calibration equation again for the rough inner point set corresponding to each candidate parameter model to obtain a fine outer parameter estimated value; determining again a fine model score, a fine outlier set and a fine inlier set in the coarse inlier set using a RANSAC algorithm and the fine outlier estimate; candidate parametric models that do not meet the model conditions are screened out based on the fine model scores.
Optionally, the preset condition is that the execution times reach preset times; or the number of candidate parameter models reaches the preset number.
Schematically, the model condition is that after each candidate parameter model is ranked according to the fine model score from high to low, the candidate parameter models in the first N bits are ranked, wherein n=n/2, and N is the number of candidate parameter models.
In order to more clearly understand the laser radar and IMU external parameter calibration method provided in the present application, an example of the method is described below. Referring to fig. 2, the method includes the steps of:
and step 21, preprocessing and downsampling the point cloud data of the laser radar to obtain the processed point cloud data.
And 22, registering the processed point clouds of the adjacent frames by using an NDT algorithm, and continuously integrating and outputting corresponding laser radar pose information according to pose transformation obtained by registration.
And step 23, pre-integrating the inertial measurement data of the IMU to obtain corresponding IMU pose information.
The present embodiment does not limit the execution sequence between step 23 and step 21.
And step 24, performing time synchronization on the laser radar pose information and the IMU pose information to obtain an aligned pose information set.
And 25, randomly selecting a plurality of groups of aligned pose information groups, inputting the pose information groups into a hand-eye calibration equation, and calculating to obtain a rough external parameter estimated value.
And step 26, calculating a corresponding model score according to the rough external parameter estimation value, and distinguishing a rough internal point set and a rough external point set in the plurality of groups of aligned pose information groups.
Step 27, if the rough model score of the parameter model corresponding to the rough external parameter estimation value is higher than the set score threshold, adding the parameter model into the candidate queue, and repeating the steps 25 and 26 until the preset number of times is run or the number of candidate parameter models in the candidate model queue reaches the preset number.
And 28, randomly selecting a plurality of pose information groups in the rough interior point set of each candidate parameter model in the candidate queue to replace the pose information groups into a hand eye calibration equation, so as to obtain a fine external parameter estimated value.
Step 29, calculating a fine model score according to the fine outlier estimation value, and further distinguishing a fine inner point set and a fine outer point set in the rough inner point set.
Step 291, sorting all candidate parametric models according to the order of the fine model scores from high to low; and deleting half candidate parameter models with lower fine model scores, and repeatedly executing the steps 28 and 29 until only one candidate parameter model is left in the queue, so as to obtain a parameter model corresponding to the final external parameter calibration value, namely, obtain the external parameter calibration value.
In summary, according to the laser radar and IMU external parameter calibration method provided in the embodiment, point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU are obtained; calculating current laser radar pose information based on the point cloud data; calculating current IMU pose information based on the inertial measurement data; aligning the laser radar pose information and the IMU pose information based on the time stamp to obtain an aligned pose information set; determining an extrinsic reference value based on the aligned pose information group by using a RANSAC algorithm; the problem of poor stability and accuracy caused by performing external parameter calibration by using the hand-eye calibration equation only can be solved; because the RANSAC algorithm can well remove the influence of abnormal pose in the hand and eye mark positioning pose on the result, the accuracy and stability of the calibration result can be improved.
Fig. 3 is a block diagram of a laser radar and IMU external parameter calibration device according to an embodiment of the present application. The device at least comprises the following modules: the system comprises a data acquisition module 310, a first calculation module 320, a second calculation module 330, a data alignment module 340 and an external parameter calibration module 350.
A data acquisition module 310, configured to acquire point cloud data acquired by the lidar and inertial measurement data acquired by the IMU;
a first calculation module 320, configured to calculate current laser radar pose information based on the point cloud data;
a second calculation module 330, configured to calculate current IMU pose information based on the inertial measurement data;
the data alignment module 340 is configured to align the laser radar pose information and the IMU pose information based on a timestamp, so as to obtain an aligned pose information set;
an extrinsic calibration module 350, configured to determine an extrinsic calibration value based on the aligned pose information set using the RANSAC algorithm.
For relevant details reference is made to the method embodiments described above.
It should be noted that: the laser radar and IMU external parameter calibration device provided in the above embodiment is only exemplified by the division of the above functional modules when performing laser radar and IMU external parameter calibration, and in practical application, the above functional allocation may be completed by different functional modules according to needs, that is, the internal structure of the laser radar and IMU external parameter calibration device is divided into different functional modules, so as to complete all or part of the functions described above. In addition, the laser radar and IMU external parameter calibration device provided in the foregoing embodiments and the laser radar and IMU external parameter calibration method embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein.
Optionally, the application further provides a computer readable storage medium, and a program is stored in the computer readable storage medium, and the program is loaded and executed by a processor to implement the laser radar and IMU external parameter calibration method of the method embodiment.
Optionally, the application further provides a computer product, which comprises a computer readable storage medium, wherein a program is stored in the computer readable storage medium, and the program is loaded and executed by a processor to realize the laser radar and IMU external parameter calibration method of the method embodiment.
The technical features of the above-described embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above-described embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present application, which are described in more detail and are not to be construed as limiting the scope of the invention. It should be noted that it would be apparent to those skilled in the art that various modifications and improvements could be made without departing from the spirit of the present application, which would be within the scope of the present application. Accordingly, the scope of protection of the present application is to be determined by the claims appended hereto.
Claims (8)
1. A laser radar and IMU external parameter calibration method is characterized by comprising the following steps:
acquiring point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU;
calculating current laser radar pose information based on the point cloud data;
calculating current IMU pose information based on the inertial measurement data;
aligning the laser radar pose information and the IMU pose information based on a time stamp to obtain an aligned pose information set;
determining an extrinsic reference value based on the aligned pose information group by using a RANSAC algorithm;
wherein the determining, using a RANSAC algorithm, an extrinsic reference value based on the aligned pose information set includes:
the following steps are circularly executed until the circulation condition does not meet the preset condition: inputting a plurality of groups of aligned pose information groups into a hand-eye calibration equation to obtain a rough external parameter estimated value; determining a rough model score and a rough outer point set and a rough inner point set in a plurality of groups of aligned pose information groups by using the RANSAC algorithm and the rough outer parameter estimation value; when the rough model score is higher than a score threshold value, determining a parameter model corresponding to the rough external parameter estimation value as a candidate parameter model;
and circularly executing the following steps until a unique parameter model is screened out from a plurality of candidate parameter models, and stopping to obtain an external parameter calibration value corresponding to the parameter model: inputting the rough inner point set into the hand-eye calibration equation again for the rough inner point set corresponding to each candidate parameter model to obtain a fine outer parameter estimated value; re-determining a fine model score, and a fine outlier set and a fine inlier set in the coarse inlier set using the RANSAC algorithm and the fine outlier estimate; and screening out candidate parameter models which do not meet the model conditions based on the fine model scores.
2. The method according to claim 1, wherein the preset condition is that the number of executions reaches a preset number; or the number of candidate parameter models reaches the preset number.
3. The method according to claim 1, wherein the model condition is that the candidate parametric models are ranked in the top N bits after ranking the candidate parametric models according to the fine model score from high to low, where n=n/2, and N is the number of candidate parametric models.
4. The method of claim 1, wherein aligning the lidar pose information and the IMU pose information based on a timestamp results in an aligned set of pose information, comprising:
acquiring a target time stamp corresponding to pose information of each frame of laser radar;
determining two time stamps adjacent to the target time stamp from the time stamps of the IMU pose information;
and performing interpolation compensation on the IMU pose information according to the difference value between the target timestamp and the determined two IMU adjacent timestamps to obtain a pose information set aligned to the target timestamp.
5. The method of claim 1, wherein after the acquiring the point cloud data acquired by the lidar and the inertial measurement data acquired by the IMU, further comprises:
preprocessing the point cloud data to remove noise points;
and carrying out downsampling treatment on the preprocessed point cloud data to obtain processed point cloud data, wherein the processed point cloud data is used for calculating the laser radar pose information.
6. The method of claim 5, wherein the calculating current lidar pose information based on the point cloud data comprises:
registering the processed point cloud data of the adjacent frames by using a normal distribution transformation NDT algorithm;
and obtaining the laser radar pose information according to the pose transformation integral obtained by registration.
7. The method of claim 1, wherein the calculating current IMU pose information based on the inertial measurement data comprises:
and pre-integrating the inertial measurement data to obtain the IMU pose information.
8. A laser radar and IMU external parameter calibration device, the device comprising:
the data acquisition module is used for acquiring point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU;
the first calculation module is used for calculating current laser radar pose information based on the point cloud data;
the second calculation module is used for calculating current IMU pose information based on the inertial measurement data;
the data alignment module is used for aligning the laser radar pose information and the IMU pose information based on the time stamp to obtain an aligned pose information group;
the external parameter calibration module is used for determining an external parameter calibration value based on the aligned pose information group by using a RANSAC algorithm;
the external parameter calibration module is specifically used for:
the following steps are circularly executed until the circulation condition does not meet the preset condition: inputting a plurality of groups of aligned pose information groups into a hand-eye calibration equation to obtain a rough external parameter estimated value; determining a rough model score and a rough outer point set and a rough inner point set in a plurality of groups of aligned pose information groups by using the RANSAC algorithm and the rough outer parameter estimation value; when the rough model score is higher than a score threshold value, determining a parameter model corresponding to the rough external parameter estimation value as a candidate parameter model;
and circularly executing the following steps until a unique parameter model is screened out from a plurality of candidate parameter models, and stopping to obtain an external parameter calibration value corresponding to the parameter model: inputting the rough inner point set into the hand-eye calibration equation again for the rough inner point set corresponding to each candidate parameter model to obtain a fine outer parameter estimated value; re-determining a fine model score, and a fine outlier set and a fine inlier set in the coarse inlier set using the RANSAC algorithm and the fine outlier estimate; and screening out candidate parameter models which do not meet the model conditions based on the fine model scores.
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| CN112946681B (en) * | 2021-05-17 | 2021-08-17 | 知行汽车科技（苏州）有限公司 | Laser radar positioning method fusing combined navigation information |
| CN113484843B (en) * | 2021-06-02 | 2024-09-06 | 福瑞泰克智能系统有限公司 | Method and device for determining external parameters between laser radar and integrated navigation |
| CN113495281B (en) * | 2021-06-21 | 2023-08-22 | 杭州飞步科技有限公司 | Real-time positioning method and device for movable platform |
| CN113759349B (en) * | 2021-09-22 | 2022-10-04 | 阿波罗智能技术(北京)有限公司 | Calibration method of laser radar and positioning equipment Equipment and autonomous driving vehicle |
| CN114166219B (en) * | 2021-12-01 | 2024-06-14 | 珠海一微半导体股份有限公司 | Inertial navigation error correction method, chip and robot |
| CN114413887B (en) * | 2021-12-24 | 2024-04-02 | 北京理工大学前沿技术研究院 | Sensor external parameter calibration method, device and medium |
| CN114325667A (en) * | 2022-01-05 | 2022-04-12 | 上海三一重机股份有限公司 | External parameter calibration method and device for combined navigation equipment and laser radar |
| CN115265581B (en) * | 2022-05-18 | 2024-08-16 | 广州文远知行科技有限公司 | Calibration parameter determining method of laser radar and inertial measurement unit and related equipment |
| CN116380935A (en) * | 2023-06-02 | 2023-07-04 | 中南大学 | High-speed railway box girder damage detection robot car and damage detection method |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109143205A (en) * | 2018-08-27 | 2019-01-04 | 深圳清创新科技有限公司 | Integrated transducer external parameters calibration method, apparatus |
| CN109901139A (en) * | 2018-12-28 | 2019-06-18 | 文远知行有限公司 | Laser radar scaling method, device, equipment and storage medium |
| CN109949371A (en) * | 2019-03-18 | 2019-06-28 | 北京智行者科技有限公司 | A kind of scaling method for laser radar and camera data |
| CN110579754A (en) * | 2019-10-15 | 2019-12-17 | 戴姆勒股份公司 | Method for determining external parameters of a lidar and other sensors of a vehicle |
| CN111207774A (en) * | 2020-01-17 | 2020-05-29 | 山东大学 | Method and system for laser-IMU external reference calibration |
| CN111443337A (en) * | 2020-03-27 | 2020-07-24 | 北京航空航天大学 | Radar-IMU calibration method based on hand-eye calibration |
-
2020
- 2020-10-22 CN CN202011136461.3A patent/CN112285676B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109143205A (en) * | 2018-08-27 | 2019-01-04 | 深圳清创新科技有限公司 | Integrated transducer external parameters calibration method, apparatus |
| CN109901139A (en) * | 2018-12-28 | 2019-06-18 | 文远知行有限公司 | Laser radar scaling method, device, equipment and storage medium |
| CN109949371A (en) * | 2019-03-18 | 2019-06-28 | 北京智行者科技有限公司 | A kind of scaling method for laser radar and camera data |
| CN110579754A (en) * | 2019-10-15 | 2019-12-17 | 戴姆勒股份公司 | Method for determining external parameters of a lidar and other sensors of a vehicle |
| CN111207774A (en) * | 2020-01-17 | 2020-05-29 | 山东大学 | Method and system for laser-IMU external reference calibration |
| CN111443337A (en) * | 2020-03-27 | 2020-07-24 | 北京航空航天大学 | Radar-IMU calibration method based on hand-eye calibration |
Non-Patent Citations (2)
| Title |
|---|
| 基于RANSAC的便携式激光扫描测量臂手眼标定方法;吴晗;李巍;董明利;;计算机工程与应用(第23期);全文 * |
| 基于单目视觉的GPS辅助相机外参数标定;吴修振;刘刚;于凤全;张源原;;光学精密工程(第08期);全文 * |
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