CN112285676A - Laser radar and IMU external reference calibration method and device - Google Patents
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Abstract
The application relates to a method and a device for calibrating external parameters of a laser radar and an IMU (inertial measurement Unit), belonging to the technical field of computers, wherein the method comprises the following steps: acquiring point cloud data acquired by a laser radar and inertial measurement data acquired by an IMU (inertial measurement Unit); performing point cloud matching on the point cloud data to calculate the current position and orientation information of the laser radar; 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 timestamp to obtain an aligned pose information group; determining an external reference calibration value based on the aligned pose information group by using a RANSAC algorithm; the problem of poor stability and accuracy caused by using only the hand-eye calibration equation to perform external reference calibration can be solved; the RANSAC algorithm can well remove the influence of the abnormal pose in the hand-eye calibration pose on the result, so that the accuracy and the stability of the calibration result can be improved.
Description
Technical Field
The application relates to a laser radar and IMU external reference calibration method and device, and belongs to the technical field of computers.
Background
The mapping and positioning are key technologies for realizing automatic driving of the unmanned vehicle. In actual operation of the vehicle, measurement using a single sensor may cause a large measurement error due to the limitation of the sensor itself. At present, a solution using a laser radar and an Inertial Measurement Unit (IMU) fusion is the mainstream research direction in the industry at present. Because different sensors are respectively assembled at different positions on a carrier, and attitude angles during simultaneous installation are possibly inconsistent, calibrating the deviation of the accurate position and attitude angle between the two sensors (namely external parameters of the sensors) is an important basis for realizing the parameter fusion of the sensors.
When external parameters of the laser radar and the IMU are calibrated, a hand-eye calibration equation is used for calibration, namely motion information of the laser radar and the IMU at a specific moment is respectively solved, a target equation is established by utilizing constraint between poses of the laser radar and the IMU, 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 obtained result is unstable by using the result, and a large error exists.
Disclosure of Invention
The application provides a laser radar and IMU external reference calibration method and device, which can solve the problem of poor stability and accuracy caused by external reference calibration only by using a hand-eye calibration equation. The application provides the following technical scheme:
in a first aspect, a method for calibrating laser radar and IMU external parameters is provided, where the method includes:
acquiring point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU;
calculating the current position and orientation information of the laser radar 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 timestamp to obtain an aligned pose information group;
determining an external reference calibration value based on the aligned set of pose information using the RANSAC algorithm.
Optionally, the determining, using the RANSAC algorithm, an external reference calibration value based on the aligned set of pose information includes:
circularly executing the following steps until the circulation condition does not meet the preset condition: inputting a plurality of groups of aligned pose information into a hand-eye calibration equation to obtain a rough external parameter estimation value; determining a coarse model score, and a set of coarse outliers and a set of coarse inliers in the plurality of sets of the aligned sets of pose information using the RANSAC algorithm and the coarse outlier estimates; when the score of the rough model is higher than a score threshold value, determining a parameter model corresponding to the rough external parameter estimation value as a candidate parameter model;
circularly executing the following steps until the only parameter model is screened out from the candidate parameter models, and obtaining the external reference value corresponding to the parameter model: for the rough internal point set corresponding to each candidate parameter model, inputting the rough internal point set into the hand-eye calibration equation again to obtain a fine external parameter estimation 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 condition based on the fine model score.
Optionally, the preset condition is that the execution times reach preset times; or, the number of the candidate parameter models reaches a preset number.
Optionally, the model condition is that after the candidate parametric models are sorted according to the fine model score from high to low, the candidate parametric models are sorted in the top N bits, where N is 1/N, and N is the number of the candidate parametric models.
Optionally, the aligning the lidar pose information and the IMU pose information based on a timestamp to obtain an aligned pose information group includes:
acquiring a target timestamp corresponding to each frame of laser radar pose information;
determining two timestamps adjacent and nearest to the target timestamp from timestamps of the IMU pose information;
and performing interpolation compensation on the IMU pose information according to the target timestamp and the determined difference value between the two IMUs adjacent timestamps to obtain the pose information group aligned to the target timestamp.
Optionally, after the acquiring point cloud data acquired by the lidar and inertial measurement data acquired by the IMU, the method further includes:
preprocessing the point cloud data to remove noise;
and performing down-sampling processing 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 current lidar 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 lidar and IMU external reference calibration apparatus is provided, the apparatus 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 the current laser radar pose information based on the point cloud data;
the second calculation module is used for calculating the 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 a timestamp to obtain an aligned pose information group;
and the external reference calibration module is used for determining an external reference calibration value based on the aligned pose information group by using the RANSAC algorithm.
The beneficial effect of this application lies in: the method comprises the steps of acquiring point cloud data acquired by a laser radar and inertial measurement data acquired by an IMU; calculating the current position and orientation information of the laser radar 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 timestamp to obtain an aligned pose information group; determining an external reference calibration value based on the aligned pose information group by using a RANSAC algorithm; the problem of poor stability and accuracy caused by using only the hand-eye calibration equation to perform external reference calibration can be solved; the RANSAC algorithm can well remove the influence of the abnormal pose in the hand-eye calibration pose on the result, so that the accuracy and the 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 solutions of the present application more clear and clear, and to implement the technical solutions according to the content of the description, the following detailed description is made with reference to the preferred embodiments of the present application and the accompanying drawings.
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Fig. 1 is a flowchart of a lidar and IMU external reference calibration method according to an embodiment of the present disclosure;
FIG. 2 is a flowchart of a lidar and IMU external reference calibration method according to another embodiment of the present disclosure;
fig. 3 is a block diagram of a lidar and IMU external reference calibration apparatus according to an embodiment of the present disclosure.
Detailed Description
The following detailed description of embodiments of the present application will be described in conjunction with the accompanying drawings and examples. The following examples are intended to illustrate the present application but are not intended to limit the scope of the present application.
First, several terms referred to in the present application will be described.
Random Sample Consensus algorithm (Random Sample Consensus, RANSAC): mathematical model parameters of the data can be iteratively calculated according to a set of sample data sets containing abnormal data, so that normal data and abnormal data are distinguished, and model parameters conforming to the data are obtained.
The principle of the RANSAC algorithm includes the following steps, performed cyclically:
1. a subset is chosen randomly and assumed to be an in-office point.
2. And fitting a model by using the local interior points, wherein the model is adapted to the assumed local interior points, and all unknown parameters can be calculated from the assumed local interior points.
3. And (3) testing other data in the whole data by using the model obtained in the step (2), and if a certain point is suitable for the estimated model, considering the certain point to be also an intra-local point, and expanding the intra-local point.
4. If enough points are classified as hypothetical intra-office points, the estimated model is reasonable enough.
5. All the expanded local interior points are used to re-estimate the model.
6. The model is evaluated by estimating the error rate of the local inliers to the model.
7. If the current model works better than the best model and is selected as the best model, otherwise the current model is discarded.
Inertial Measurement Unit (IMU): the device is used 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 Distributed Transform algorithm (NDT): the method is a point cloud registration algorithm, is applied to a statistical model of three-dimensional point cloud, determines the optimal matching between two point clouds by using an iterative optimization method and obtains corresponding pose transformation.
Calibrating the hands and eyes: and is used for calibrating rigid body coordinate conversion between the tail end camera of the mechanical arm and the tail end of the mechanical arm at the earliest time. In a world coordinate system, for the motion at any moment, the terminal pose A and the terminal pose B of the manipulator meet the motion constraint AX (XB), and the external parameter X can be obtained by solving the objective equation through optimization.
Optionally, the present application takes the execution subject of each embodiment as an example of a control device, the control device may be installed in a vehicle or a device independent from the vehicle, and the implementation manner of the control device is not limited in this embodiment. 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 lidar and IMU external reference calibration method according to an embodiment of the present disclosure. The method at least comprises the following steps:
The frequency at which the lidar and the IMU acquire data is typically different, and in general, the acquisition frequency of the lidar is lower than that of the IMU. 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 simultaneously acquire point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU.
Among these, inertial measurement data include, but are not limited to: acceleration values and angle values. Of course, the inertial measurement data may also include other parameters, which are not listed here.
And 102, calculating the current laser radar pose information based on the point cloud data.
Optionally, after point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU are acquired, preprocessing the point cloud data to remove noise; and performing down-sampling processing on the preprocessed point cloud data to obtain processed point cloud data, wherein the processed point cloud data is used for calculating the position and attitude information of the laser radar. At this time, calculating the current laser radar pose information based on the point cloud data, including: using an NDT algorithm to register the processed point cloud data of the adjacent frames; and obtaining the laser radar pose information according to the pose transformation integral obtained by registration.
And 103, calculating the current IMU pose information based on the inertial measurement data.
Schematically, the inertial measurement data is pre-integrated to obtain IMU pose information.
And the laser radar pose information and the IMU pose information are used for indicating the position and the running angle of the current vehicle.
In addition, the execution order of step 103 and step 102 is not limited in this embodiment.
And step 104, aligning the laser radar pose information and the IMU pose information based on the timestamp to obtain an aligned pose information group.
And each aligned pose information group comprises aligned laser radar pose information and aligned IMU pose information.
Schematically, aligning the laser radar pose information and the IMU pose information based on the timestamp to obtain an aligned pose information group, including: acquiring a target timestamp corresponding to each frame of laser radar pose information; determining two timestamps adjacent and closest to the target timestamp from the timestamps of the IMU pose information; and performing interpolation compensation on the IMU pose information according to the target timestamp and the determined difference value between the two IMUs adjacent to the target timestamp to obtain a pose information group aligned to the target timestamp.
Assume the lidar pose information for target timestamp t1 is X1; because of the high acquisition frequency of the IMU, there are two timestamps t0 and t2 that are nearest to t1, the timestamp t0 corresponds to IMU pose information Y1, the timestamp t1 corresponds to IMU pose information Y2, and there may be no IMU pose information corresponding to the target timestamp t 1. Therefore, in the present embodiment, 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 IMU pose information Y2 corresponding to the timestamp t 1. Where t0< t1< t2, Y is proportional to the time difference to an intermediate value, and is represented by the following equation:
Y＝(t1-t0)/(t2-t0)*Y1+(t2-t1)/(t2-t0)*Y2。
and 105, determining a hand-eye calibration equation and an external reference calibration value corresponding to the hand-eye calibration equation based on the aligned pose information set by using a RANSAC algorithm.
The method for determining the hand-eye calibration equation and the external reference calibration value corresponding to the hand-eye calibration equation based on the aligned pose information set by using the RANSAC algorithm comprises the following steps:
circularly executing the following steps until the circulation condition does not meet the preset condition: inputting a plurality of groups of aligned pose information into a hand-eye calibration equation to obtain a rough external parameter estimation value; determining a rough model score and rough outer point sets and rough inner point sets in a plurality of groups of aligned pose information sets by using a RANSAC algorithm and rough outer parameter estimation values; when the score of the rough model is higher than a score threshold value, determining a parameter model corresponding to the rough external parameter estimation value as a candidate parameter model;
the following steps are executed in a circulating mode until the only parameter model is screened out from the candidate parameter models, and the external reference value corresponding to the parameter model is obtained: for the rough internal point set corresponding to each candidate parameter model, inputting the rough internal point set into the hand-eye calibration equation again to obtain a fine external parameter estimation value; determining the score of the fine model and the fine outer point set and the fine inner point set in the rough inner point set again by using a RANSAC algorithm and the fine outer parameter estimation value; candidate parametric models that do not meet the model conditions are screened out based on the refined model scores.
Optionally, the preset condition is that the execution times reach preset times; or, the number of the candidate parameter models reaches a preset number.
Illustratively, the model condition is that after each candidate parameter model is sorted according to the fine model score from high to low, the candidate parameter models in the top N bits are sorted, wherein N is N/2, and N is the number of the candidate parameter models.
In order to more clearly understand the lidar and IMU external reference calibration method provided by the present application, the method is described as an example. Referring to fig. 2, the method includes the following steps:
and step 21, preprocessing and downsampling the point cloud data of the laser radar to obtain processed point cloud data.
And step 22, registering the processed point clouds of the adjacent frames by using an NDT algorithm, and continuously outputting corresponding laser radar pose information in an integral manner 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 24, carrying out 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 aligned pose information sets and inputting the pose information sets into a hand-eye calibration equation to be calculated to obtain rough external parameter estimation values.
And 26, calculating corresponding model scores according to the rough external parameter estimated values, and distinguishing rough inner point sets and rough outer point sets in the plurality of aligned pose information sets.
And 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 value, adding the parameter model into the candidate queue, and repeating the steps 25 and 26 until the preset times of operation or the number of the candidate parameter models in the candidate model queue reaches the preset number.
And step 28, randomly selecting several groups of posture information groups in the rough interior point set of each candidate parameter model in the candidate queue to substitute into the hand-eye calibration equation to obtain a fine external parameter estimation value.
And 29, calculating the score of the fine model according to the fine extrinsic parameter estimated 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 parameter models according to the sequence of the fine model scores from high to low; and deleting half of the 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 to obtain the final parameter model corresponding to the external reference calibration value, namely the external reference calibration value.
In summary, according to the method for calibrating the external reference of the laser radar and the IMU provided by the embodiment, point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU are acquired; calculating the current position and orientation information of the laser radar 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 timestamp to obtain an aligned pose information group; determining an external reference calibration value based on the aligned pose information group by using a RANSAC algorithm; the problem of poor stability and accuracy caused by using only the hand-eye calibration equation to perform external reference calibration can be solved; the RANSAC algorithm can well remove the influence of the abnormal pose in the hand-eye calibration pose on the result, so that the accuracy and the stability of the calibration result can be improved.
Fig. 3 is a block diagram of a lidar and IMU external reference calibration apparatus according to an embodiment of the present disclosure. The device at least comprises the following modules: a data acquisition module 310, a first calculation module 320, a second calculation module 330, a data alignment module 340, and an external reference calibration module 350.
A data acquisition module 310, configured to acquire point cloud data acquired by the laser radar and inertial measurement data acquired by the IMU;
a first calculation module 320, configured to calculate current lidar 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;
a data alignment module 340, configured to align the laser radar pose information and the IMU pose information based on a timestamp, to obtain an aligned pose information group;
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 above-described method embodiments.
It should be noted that: in the lidar and IMU external reference calibration device provided in the above embodiment, when the lidar and IMU external reference calibration is performed, only the division of the above functional modules is used for illustration, and in practical applications, the above function distribution may be completed by different functional modules as needed, that is, the internal structure of the lidar and IMU external reference calibration device is divided into different functional modules to complete all or part of the above described functions. In addition, the lidar and IMU external reference calibration apparatus provided by the above embodiment and the lidar and IMU external reference calibration method embodiment belong to the same concept, and the specific implementation process thereof is detailed in the method embodiment and is not described herein again.
Optionally, the present application further provides a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the lidar and IMU external reference calibration method according to the foregoing method embodiment.
Optionally, the present application further provides a computer product, which includes a computer-readable storage medium, where a program is stored in the computer-readable storage medium, and the program is loaded and executed by a processor to implement the lidar and IMU external reference calibration method according to the foregoing method embodiment.
The technical features of the embodiments described above may be arbitrarily combined, and for the sake of brevity, all possible combinations of the technical features in the embodiments described above are not described, but should be considered as being within the scope of the present specification as long as there is no contradiction between the combinations of the technical features.
The above-mentioned embodiments only express several embodiments of the present application, and the description thereof is more specific and detailed, but not construed as limiting the scope of the invention. It should be noted that, for a person skilled in the art, several variations and modifications can be made without departing from the concept of the present application, which falls within the scope of protection of the present application. Therefore, the protection scope of the present patent shall be subject to the appended claims.
Claims (9)
1. A laser radar and IMU external reference 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 the current laser radar position and orientation 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 timestamp to obtain an aligned pose information group;
determining an external reference calibration value based on the aligned set of pose information using the RANSAC algorithm.
2. The method of claim 1, wherein the determining an extrinsic calibration value based on the set of aligned pose information using the RANSAC algorithm comprises:
circularly executing the following steps until the circulation condition does not meet the preset condition: inputting a plurality of groups of aligned pose information into a hand-eye calibration equation to obtain a rough external parameter estimation value; determining a coarse model score, and a set of coarse outliers and a set of coarse inliers in the plurality of sets of the aligned sets of pose information using the RANSAC algorithm and the coarse outlier estimates; when the score of the rough model is higher than a score threshold value, determining a parameter model corresponding to the rough external parameter estimation value as a candidate parameter model;
circularly executing the following steps until the only parameter model is screened out from the candidate parameter models, and obtaining the external reference value corresponding to the parameter model: for the rough internal point set corresponding to each candidate parameter model, inputting the rough internal point set into the hand-eye calibration equation again to obtain a fine external parameter estimation 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 condition based on the fine model score.
3. The method according to claim 2, wherein the preset condition is that the execution times reach a preset number; or, the number of the candidate parameter models reaches a preset number.
4. The method according to claim 2, wherein the model condition is that after each candidate parametric model is sorted from high to low according to the fine model score, the candidate parametric model is sorted in the top N bits, where N is N/2, and N is the number of candidate parametric models.
5. The method of claim 1, wherein aligning the lidar pose information and the IMU pose information based on timestamps to obtain an aligned set of pose information comprises:
acquiring a target timestamp corresponding to each frame of laser radar pose information;
determining two timestamps adjacent and nearest to the target timestamp from timestamps of the IMU pose information;
and performing interpolation compensation on the IMU pose information according to the target timestamp and the determined difference value between the two IMUs adjacent timestamps to obtain the pose information group aligned to the target timestamp.
6. The method of claim 1, wherein after the acquiring the lidar acquired point cloud data and the IMU acquired inertial measurement data, further comprising:
preprocessing the point cloud data to remove noise;
and performing down-sampling processing 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.
7. The method of claim 6, 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.
8. The method of claim 1, wherein said calculating current IMU pose information based on said inertial measurement data comprises:
and pre-integrating the inertial measurement data to obtain the IMU pose information.
9. A laser radar and IMU external reference calibration device is characterized by 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 the current laser radar pose information based on the point cloud data;
the second calculation module is used for calculating the 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 a timestamp to obtain an aligned pose information group;
and the external reference calibration module is used for determining an external reference calibration value based on the aligned pose information group by using the RANSAC algorithm.
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| CN113484843A (en) * | 2021-06-02 | 2021-10-08 | 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 |
| CN113495281A (en) * | 2021-06-21 | 2021-10-12 | 杭州飞步科技有限公司 | Real-time positioning method and device for movable platform |
| CN113759349A (en) * | 2021-09-22 | 2021-12-07 | 阿波罗智能技术(北京)有限公司 | Calibration method and device for laser radar and positioning device and automatic driving vehicle |
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| CN114413887A (en) * | 2021-12-24 | 2022-04-29 | 北京理工大学前沿技术研究院 | Method, equipment and medium for calibrating external parameters of sensor |
| 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 |
| WO2023131123A1 (en) * | 2022-01-05 | 2023-07-13 | 上海三一重机股份有限公司 | External parameter calibration method and apparatus for combined navigation device and laser radar |
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