CN112362084A - Data calibration method, device and system - Google Patents
Data calibration method, device and system Download PDFInfo
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- CN112362084A CN112362084A CN202011321128.XA CN202011321128A CN112362084A CN 112362084 A CN112362084 A CN 112362084A CN 202011321128 A CN202011321128 A CN 202011321128A CN 112362084 A CN112362084 A CN 112362084A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
- G01C25/005—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass initial alignment, calibration or starting-up of inertial devices
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C25/00—Manufacturing, calibrating, cleaning, or repairing instruments or devices referred to in the other groups of this subclass
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
- G01S7/40—Means for monitoring or calibrating
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/497—Means for monitoring or calibrating
Abstract
The specification discloses a data calibration method, a data calibration device and a data calibration system, which can acquire sensing data acquired by each type of sensor arranged on equipment to be calibrated, determine observation pose data of the equipment to be calibrated under the type of sensor according to the sensing data acquired by the type of sensor, acquire calibration pose data corresponding to the equipment to be calibrated, minimize the deviation between the observation pose data and the calibration pose data of the equipment to be calibrated under the various types of sensors, and take the deviation between the observation pose data of the equipment to be calibrated under the various types of sensors as an optimization target, and calibrate initial external parameter data among the various types of sensors to calibrate actual external parameter data among the various types of sensors. As external reference data among all types of sensors on the equipment to be calibrated can be calibrated simultaneously, the efficiency of data calibration is effectively improved.
Description
Technical Field
The specification relates to the field of unmanned driving, in particular to a data calibration method, a data calibration device and a data calibration system.
Background
Currently, the unmanned equipment is gradually applied to daily life of people, and more convenient services are brought to the life of people.
Various types of sensors are usually arranged on the unmanned device, and the sensors can acquire some state data of the unmanned device in the driving process so that the unmanned device can make decisions based on the state data. In order to ensure the safe driving of the unmanned equipment, external parameters between the sensors need to be calibrated.
However, in practical applications, the external parameters between two different types of sensors can only be calibrated, and after calibration is completed, the external parameters between other two different types of sensors are calibrated, so that not only is the calibration efficiency low, but also the error transmission condition exists in the calibration process, and the accuracy of the calibrated external parameters is reduced.
Therefore, how to improve the calibration efficiency and accuracy of the external reference data is an urgent problem to be solved.
Disclosure of Invention
The present specification provides a data calibration method, a data calibration device, and a data calibration system, so as to partially solve the above problems in the prior art.
The technical scheme adopted by the specification is as follows:
the present specification provides a data calibration method, in which a device to be calibrated is provided with various types of sensors, including:
acquiring sensing data acquired by each type of sensor set for the equipment to be calibrated;
according to the sensing data acquired by the sensor of the type, determining the observation pose data of the equipment to be calibrated under the sensor of the type, and acquiring the calibration pose data corresponding to the equipment to be calibrated;
and calibrating initial external parameter data among the various types of sensors to calibrate the actual external parameter data among the various types of sensors by taking the minimum deviation between the observation pose data of the equipment to be calibrated under the various types of sensors and the calibration pose data and the deviation between the observation pose data of the equipment to be calibrated under the various types of sensors as optimization targets.
Optionally, before calibrating the initial extrinsic parameter data between the various types of sensors, the method further comprises:
and optimizing observation pose data of the equipment to be calibrated under the type of sensor according to a preset noise model aiming at each type of sensor arranged on the equipment to be calibrated to obtain the optimized observation pose data of the equipment to be calibrated under the type of sensor.
Optionally, with the objective of minimizing deviation between the observation pose data of the device to be calibrated under various types of sensors and the calibration pose data, and deviation between the observation pose data of the device to be calibrated under various types of sensors as optimization objectives, calibrating the initial extrinsic parameter data between the various types of sensors specifically includes:
and taking the minimized deviation between the optimized observation pose data of the equipment to be calibrated under various types of sensors and the calibrated pose data and the deviation between the optimized observation pose data of the equipment to be calibrated under various types of sensors as optimization targets, and calibrating the initial external parameter data between various types of sensors.
Optionally, before calibrating the initial extrinsic parameter data between the various types of sensors, the method further comprises:
selecting a specified type of sensor from various types of sensors set by the equipment to be calibrated;
taking the minimum deviation between the observation pose data of the equipment to be calibrated under various types of sensors and the calibration pose data and the deviation between the observation pose data of the equipment to be calibrated under various types of sensors as optimization targets, and calibrating the initial external parameter data among the various types of sensors, which specifically comprises the following steps:
determining a preset coordinate system;
for each other type of sensor except the specified type of sensor, determining observation pose data of the other type of sensor in the preset coordinate system according to a transformation matrix of the other type of sensor to the preset coordinate system, and determining observation pose data of the specified type of sensor in the preset coordinate system according to the transformation matrix of the specified type of sensor to the preset coordinate system;
and calibrating initial external parameter data among the various types of sensors by taking the deviation between the observation pose data of each other type of sensor in the preset coordinate system and the observation pose data of the specified type of sensor in the preset coordinate system and the deviation between the observation pose data of the specified type of sensor in the preset coordinate system and the calibration pose data as optimization targets.
Optionally, before calibrating the initial extrinsic parameter data between the various types of sensors, the method further comprises:
selecting a specified type of sensor from various types of sensors set by the equipment to be calibrated;
taking the minimum deviation between the observation pose data of the equipment to be calibrated under various types of sensors and the calibration pose data and the deviation between the observation pose data of the equipment to be calibrated under various types of sensors as optimization targets, and calibrating the initial external parameter data among the various types of sensors, which specifically comprises the following steps:
for each other type of sensor except the specified type of sensor in the various types of sensors, taking the deviation between the observation pose data of the device to be calibrated under the specified type of sensor and the observation pose data of the device to be calibrated under the other type of sensor and the deviation between the observation pose data of the device to be calibrated under the specified type of sensor and the calibration pose data as optimization targets, and calibrating initial external parameter data between the specified type of sensor and the other type of sensor to obtain actual external parameter data between the specified type of sensor and the other type of sensor;
and determining actual external parameter data between the sensors of other types according to the actual external parameter data between the sensor of the specified type and the sensors of other types.
Optionally, the specified type of sensor comprises: an inertial measurement unit IMU.
The specification provides a data calibration system, which comprises a device to be calibrated, a data calibration device and a control console, wherein when the device to be calibrated is positioned on the control console, the device to be calibrated does not move relative to the control console, and the device to be calibrated is provided with various sensors;
the control console is used for executing corresponding control actions according to preset control instructions so that the equipment to be calibrated on the control console moves according to the control actions executed by the control console;
the data calibration device is used for acquiring sensing data acquired by each type of sensor contained in the device to be calibrated, determining observation pose data of the device to be calibrated under the type of sensor according to the sensing data acquired by the type of sensor, acquiring calibration pose data corresponding to the device to be calibrated so as to minimize the deviation between the observation pose data of the device to be calibrated under the types of sensors and the calibration pose data, and taking the deviation between the observation pose data of the device to be calibrated under the types of sensors as an optimization target, and calibrating initial external parameter data among the types of sensors so as to calibrate actual external parameter data among the types of sensors.
This specification provides a data calibration device, treats that calibration equipment is provided with various types of sensor, includes:
the acquisition module is used for acquiring the sensing data acquired by each type of sensor set by the equipment to be calibrated;
the determining module is used for determining observation pose data of the equipment to be calibrated under the type of sensor according to the sensing data acquired by the type of sensor and acquiring calibration pose data corresponding to the equipment to be calibrated;
and the calibration module is used for calibrating the initial external parameter data among the various types of sensors to calibrate the actual external parameter data among the various types of sensors by taking the minimum deviation between the observation pose data of the equipment to be calibrated under the various types of sensors and the calibration pose data and the deviation between the observation pose data of the equipment to be calibrated under the various types of sensors as optimization targets.
The present specification provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the above-described data scaling method.
The present specification provides an electronic device, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the data calibration method when executing the program.
The technical scheme adopted by the specification can achieve the following beneficial effects:
in the data calibration method provided in this specification, the sensing data acquired by each type of sensor provided on the device to be calibrated may be acquired, the observation pose data of the device to be calibrated under the type of sensor may be determined according to the sensing data acquired by the type of sensor, the calibration pose data corresponding to the device to be calibrated may be acquired, and then the initial external parameter data between the various types of sensors may be calibrated to calibrate the actual external parameter data between the various types of sensors, with the deviation between the observation pose data and the calibration pose data of the device to be calibrated under the various types of sensors being minimized, and the deviation between the observation pose data of the device to be calibrated under the various types of sensors being the optimization target.
According to the method, the actual external reference data among all types of sensors on the equipment to be calibrated can be calibrated simultaneously, so that the error transmission of each type of sensor on the data can be effectively eliminated, the accuracy of the calibrated actual external reference data among various types of sensors is ensured, and the efficiency of data calibration is effectively improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the specification and are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description serve to explain the specification and not to limit the specification in a non-limiting sense. In the drawings:
FIG. 1 is a schematic flow chart of a data calibration method in this specification;
FIG. 2 is a schematic diagram of a data calibration system provided herein;
FIG. 3 is a schematic diagram of a data calibration apparatus provided herein;
fig. 4 is a schematic diagram of an electronic device corresponding to fig. 1 provided in the present specification.
Detailed Description
In order to make the objects, technical solutions and advantages of the present disclosure more clear, the technical solutions of the present disclosure will be clearly and completely described below with reference to the specific embodiments of the present disclosure and the accompanying drawings. It is to be understood that the embodiments described are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments obtained by a person of ordinary skill in the art based on the embodiments in the present specification without any creative effort belong to the protection scope of the present specification.
The technical solutions provided by the embodiments of the present description are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a data calibration method in this specification, which specifically includes the following steps:
s101: and acquiring the sensing data acquired by each type of sensor aiming at the equipment to be calibrated.
In this specification, a device to be calibrated is a device that needs to calibrate external parameter data, and the device to be calibrated may be provided with a plurality of different types of sensors, such as a camera, a laser radar, a millimeter wave radar, an Inertial Measurement Unit (IMU), and the like. External reference data exist among different types of sensors, and the external reference data among the different types of sensors are used for representing the relative position relation among the different types of sensors on the equipment to be calibrated. For example, external reference data between the IMU and the camera is used to characterize the relative positional relationship of the IMU and the camera on the device to be calibrated. However, because the pose data determined by the sensors of different types are determined in the respective coordinate systems, external reference data between the sensors of different types is needed to realize the mutual transformation of the pose data determined by the sensors of different types (or the pose data in the different coordinate systems). In order to ensure that pose data can be accurately converted in different coordinate systems, external parameter data between sensors of different types need to be calibrated.
The equipment to be calibrated mentioned in the specification can be unmanned vehicles, robots, automatic distribution equipment and other equipment capable of realizing automatic driving. Therefore, the unmanned equipment subjected to data calibration by the data calibration method provided by the specification can be used for executing distribution tasks in the distribution field, such as business scenes of distribution such as express delivery, logistics and takeaway by using the unmanned equipment.
The present specification provides a data calibration system, which includes a device to be calibrated, a data calibration device and a console, as shown in fig. 2.
Fig. 2 is a schematic diagram of a data calibration system provided in this specification.
In the data calibration system of fig. 2, a console is shown, on which the device to be calibrated can be placed, and which can perform corresponding control actions based on preset control instructions. The console rotates, ascends, translates front and back and left and right, and the like. Since the device to be calibrated does not move relative to the console when located on the console, that is, the device to be calibrated is understood to be fixed on the console. In this way, when the console rotates in a certain direction, the device to be calibrated on the console will also rotate correspondingly, and when the console translates in a certain direction and speed, the device to be calibrated will also translate in a corresponding direction and speed (only the rotation is shown in fig. 2, but the console can actually be raised or lowered in the vertical direction and translated in the horizontal direction).
The data calibration system is provided with a data calibration device (not shown in fig. 2), and the data calibration device can simultaneously calibrate external parameter data between various types of sensors arranged on the device to be calibrated according to acquired sensing data acquired by various types of sensors arranged on the device to be calibrated and based on the sensing data.
And calibration plates are also arranged around the console and are mainly used for enabling sensors such as cameras, laser radars, millimeter wave radars and the like to obtain corresponding sensing data based on the calibration plates. The calibration plate may be in the form of a checkerboard as shown in fig. 2.
In this specification, the console may execute a corresponding control action according to a preset control command, and the device to be calibrated, which is fixed on the console, may perform a corresponding movement, such as translation and rotation, along with the control action executed by the console. At this time, various types of sensors arranged on the device to be calibrated acquire corresponding sensing data and send the sensing data to the data calibration device. For example, in the process that the device to be calibrated moves along with the console, the camera on the device to be calibrated can acquire image data containing the calibration board to obtain corresponding sensing data. And then, the acquired sensing data can be sent to data calibration equipment for external parameter calibration according to a preset data transmission mode. That is, at the same time, the sensing data collected by the various types of sensors on the device to be calibrated is obtained based on the same action performed by the device to be calibrated.
In order to obtain effective sensing data, the control action executed by the console should satisfy a certain condition, for example, in order to obtain image data with high image quality, the console should not be too fast in the process of translation or rotation. The console should not be too slow in translation or rotation in order to ensure effective actuation of the IMU.
S102: according to the sensing data acquired by the sensor of the type, the observation pose data of the equipment to be calibrated under the sensor of the type is determined, and the calibration pose data corresponding to the equipment to be calibrated is acquired.
After the sensing data acquired by various types of sensors is acquired, the observation pose data of the equipment to be calibrated under the various types of sensors can be further determined. Wherein, the adopted modes are different for determining the observation pose data under different types of sensors. For example, a sensor of the camera type can determine corresponding observation pose data by analyzing and matching corner points on an image data calibration board after acquiring the acquired image data. For the sensor of the IMU type, the acceleration and the angular velocity in the sensing data acquired by the IMU can be integrated, so that corresponding observation pose data can be obtained.
Since various types of sensors are arranged on the same device to be calibrated, the observation pose data of the device to be calibrated under the various types of sensors should be theoretically the same when the device to be calibrated moves. Therefore, the data calibration equipment can calibrate the external parameter data among the sensors of different types after acquiring the observation pose data of the equipment to be calibrated under the sensors of various types.
It should be noted that the observation pose data mentioned in this specification is different from the actual pose data of the device to be calibrated, and because in practical applications, the pose data determined by the sensing data acquired by the sensor has an error, the observation pose data is pose data with an error. In order to ensure that external reference data between various types of sensors can be accurately calibrated, actual pose data of the device to be calibrated is also obtained, so in this specification, the data calibration device also needs to obtain calibration pose data corresponding to the device to be calibrated, the calibration pose data mentioned here is the actual pose data, and the calibration pose data can be artificially determined and sent to the data calibration device.
It should be noted that since various types of sensors are disposed on the same device to be calibrated, the position and posture data determined based on these sensors should be theoretically the same, and thus, the calibration position and posture data mentioned here may also be one position and posture data. Of course, since the pose data determined based on the sensing data collected by different sensors are all completed in respective coordinate systems, there may be a plurality of calibration pose data mentioned here, that is, corresponding to each type of sensor, there may be a corresponding calibration pose data.
S103: and calibrating initial external parameter data among the various types of sensors to calibrate the actual external parameter data among the various types of sensors by taking the minimum deviation between the observation pose data of the equipment to be calibrated under the various types of sensors and the calibration pose data and the deviation between the observation pose data of the equipment to be calibrated under the various types of sensors as optimization targets.
Since the various types of sensors are all arranged on the same device to be calibrated, the observation pose data determined based on the sensing data collected by the sensors should be theoretically the same, and in order to ensure that the external reference data between the finally calibrated sensors of various types is accurate, it is also necessary to ensure that the observation pose data should be as close as possible to the acquired calibration pose data. Therefore, in the present specification, the data calibration apparatus can minimize the deviation between the observation pose data and the calibration pose data of the apparatus to be calibrated under various types of sensors, and the deviation between the observation pose data of the apparatus to be calibrated under various types of sensors as an optimization target, and calibrate the initial extrinsic parameter data between various types of sensors to calibrate the actual extrinsic parameter data between various types of sensors.
In this specification, after obtaining sensing data acquired based on sensors and determining corresponding observation pose data, the observation pose data can be optimized to obtain optimized observation pose data, and then in a subsequent process, external parameter data between each type of sensor is calibrated through the optimized observation pose data of the device to be calibrated under each type of sensor.
In practical applications, various noises exist in the determined pose data based on the sensing data collected by the sensor, so that the determined pose data and the actual pose data have certain deviation. To eliminate the influence of such variations, it is necessary to optimize the posture data.
Therefore, in this specification, the data calibration device may optimize, for each type of sensor provided on the device to be calibrated, the observation pose data of the device to be calibrated under the type of sensor according to the preset noise model, so as to obtain the optimized observation pose data of the device to be calibrated under the type of sensor. That is to say, the noise model can be used to eliminate the influence of noise on the observation pose data to a certain extent, so as to achieve the purpose of optimization.
Further, after determining the optimized observation pose data of the device to be calibrated under various types of sensors, the deviation between the optimized observation pose data and the calibration pose data of the device to be calibrated under various types of sensors and the deviation between the optimized observation pose data of the device to be calibrated under various types of sensors can be minimized as optimization targets, and the initial external parameter data between various types of sensors can be calibrated to calibrate the actual external parameter data between various types of sensors.
Certainly, noise models corresponding to different types of sensors may be different, for example, for the IMU, the offsets of the acceleration value and the angular velocity value determined by the IMU satisfy a gaussian white noise model, and therefore, it is necessary to optimize observation pose data determined based on sensing data acquired by the IMU through the gaussian white noise model to obtain optimized observation pose data of the device to be calibrated under the IMU.
Besides the optimization mode, the data calibration equipment can optimize the observation pose data in other modes. For example, for the laser radar, since the actual distance between the device to be calibrated and the calibration plate in fig. 2 can be directly measured, the observation pose data of the device to be calibrated under the laser radar can be optimized according to the actual measured actual distance between the device to be calibrated and the calibration plate. For the IMU, the obtained sensing data of acceleration, angular velocity and the like can only obtain corresponding observation pose data in an integral mode, so that each group of observation pose data corresponding to the IMU can be optimized in a nonlinear optimization mode to obtain optimized observation pose data, or the observation pose data corresponding to the laser radar is determined to be more accurate, and then the observation pose data of the device to be calibrated under the IMU can be optimized through the observation pose data of the device to be calibrated under the laser radar to obtain corresponding optimized observation pose data.
In this specification, the data calibration device may select one type of sensor from various types of sensors as a designated type of sensor, and then perform data calibration on extrinsic parameter data between the various types of sensors based on the designated type of sensor.
Among other things, the IMU sensor may be considered a designated type of sensor because the frequency of observation of the IMU is relatively high compared to other types of sensors. And the calibration is mainly realized based on data synchronization among various types of sensors in the process of calibrating external parameter data. If other types of sensors are selected as the designated type of sensors, data synchronization between the types of sensors may not be well achieved, that is, in the process of calibrating the external parameter data, pose data corresponding to the different types of sensors may not be obtained synchronously. And because the observation frequency of the IMU is higher than that of other types of sensors, more sensing data are obtained, so that the situation that the pose data corresponding to the IMU in other types of sensors are synchronous in data can be found when the IMU is used as a specified type of sensor. Of course, other types of sensors such as cameras, lidar, millimeter wave radar, etc. may also be employed as the designated type of sensor.
After the appointed type sensor is determined, the data calibration equipment determines a preset coordinate system, and aiming at each other type sensor except the appointed type sensor, the data calibration equipment determines the observation pose data of the other type sensor in the preset coordinate system according to the transformation matrix of the other type sensor to the preset coordinate system, and determines the observation pose data of the appointed type sensor in the preset coordinate system according to the transformation matrix of the appointed type sensor to the preset coordinate system. The preset coordinate system mentioned herein may refer to a world coordinate system.
Further, the data calibration device can minimize the deviation between the observation pose data of each other type of sensor in the preset coordinate system and the observation pose data of the specified type of sensor in the preset coordinate system, and the deviation between the observation pose data of the specified type of sensor in the preset coordinate system and the calibration pose data as optimization targets, and calibrate the initial external parameter data among the various types of sensors, so as to obtain the actual external parameter data among the various types of sensors.
In the process, the data calibration device actually selects one type of sensor and a preset coordinate system as a reference in the process of calibrating the external parameter data among the various types of sensors, then converts the observation pose data corresponding to the various types of sensors into the same coordinate system through the conversion matrix of the various types of sensors aiming at the preset coordinate system to obtain the observation pose data under the same coordinate system, and then solves the actual external parameter data among the various types of sensors by taking the minimum deviation between the observation pose data and the observation pose data of the specified type of sensors under the preset coordinate system as one of optimization targets.
Further, a same preset coordinate system is selected, so that in the process of calibrating the external reference data, the conversion matrixes of the various types of sensors for the preset coordinate system are actually calibrated first, and once the conversion matrixes of the various types of sensors for the preset coordinate system are calibrated, the actual external reference data among the various types of sensors can be solved by taking the preset coordinate system as a reference.
According to the method, the data calibration equipment can simultaneously solve the actual external reference data among the sensors of various types, so that the error transfer among the sensors of various types can be effectively eliminated, the accuracy of the determined external reference data among the sensors of different types is ensured, and the data calibration efficiency of the external reference data is effectively improved.
It should be noted that, in this specification, after the specified type of sensor is selected, for each other type of sensor in the various types of sensors except for the specified type of sensor, the initial extrinsic parameter data between the specified type of sensor and the other type of sensor may be calibrated to obtain the actual extrinsic parameter data between the specified type of sensor and the other type of sensor, with the objective of minimizing the deviation between the observation pose data of the device to be calibrated under the specified type of sensor and the observation pose data of the device to be calibrated under the other type of sensor, and the deviation between the observation pose data of the device to be calibrated under the specified type of sensor and the calibration pose data as optimization objectives. The data calibration device may then further determine actual external reference data between each of the other types of sensors based on the actual external reference data between the specified type of sensor and each of the other types of sensors.
For example, assuming that the type a sensor is a designated type sensor, after the actual external reference data between the type a sensor and the type B sensor, the actual external reference data between the type a sensor and the type C sensor, and the actual external reference data between the type a sensor and the type D sensor are respectively determined, the actual external reference data between the type B sensor and the type C sensor, the actual external reference data between the type B sensor and the type D sensor, and the actual external reference data between the type C sensor and the type D sensor may be respectively determined using the observation pose data of the type a sensor as an equivalence condition.
It should be further noted that the device to be calibrated may also perform data calibration on external parameters between various types of sensors, in which case, the device to be calibrated is the above-mentioned data calibration device. In this specification, the optimization method for solving the external reference data between various types of sensors may be various, for example, a gauss-newton algorithm, Levenberg-Marquardt method (LM), etc.
Further, the data calibration device or the device to be calibrated may also perform data calibration on internal parameter data of various types of sensors based on the acquired sensing data. The data calibration method of the internal reference data is a conventional calibration method, and the specification does not specifically limit the data calibration method.
Based on the same idea, the data calibration method provided above for one or more embodiments of the present specification further provides a corresponding data calibration apparatus, as shown in fig. 3.
Fig. 3 is a schematic diagram of a data calibration apparatus provided in this specification, which specifically includes:
an obtaining module 301, configured to obtain, for each type of sensor set in the device to be calibrated, sensing data acquired by the type of sensor;
a determining module 302, configured to determine, according to sensing data acquired by a sensor of the type, observation pose data of the device to be calibrated under the sensor of the type, and acquire calibration pose data corresponding to the device to be calibrated;
a calibration module 303, configured to calibrate the initial extrinsic parameter data between the various types of sensors to calibrate the actual extrinsic parameter data between the various types of sensors, with a goal of minimizing a deviation between the observation pose data of the device to be calibrated under the various types of sensors and the calibration pose data, and a deviation between the observation pose data of the device to be calibrated under the various types of sensors as optimization goals.
Optionally, before the calibration module 303 calibrates the initial external parameter data between the various types of sensors, the determining module 302 is further configured to, for each type of sensor set in the device to be calibrated, optimize the observation pose data of the device to be calibrated under the sensor of the type according to a preset noise model, so as to obtain optimized observation pose data of the device to be calibrated under the sensor of the type.
Optionally, the calibration module 303 is specifically configured to calibrate the initial external parameter data between the various types of sensors by taking a minimum of a deviation between the optimized observation pose data of the device to be calibrated under the various types of sensors and the calibration pose data, and a deviation between the optimized observation pose data of the device to be calibrated under the various types of sensors as optimization targets.
Optionally, before the calibration module 303 calibrates the initial external parameter data between the various types of sensors, the determination module 302 is further configured to select a specific type of sensor from the various types of sensors set by the device to be calibrated;
the calibration module 303 is specifically configured to determine a preset coordinate system; for each other type of sensor except the specified type of sensor, determining observation pose data of the other type of sensor in the preset coordinate system according to a transformation matrix of the other type of sensor to the preset coordinate system, and determining observation pose data of the specified type of sensor in the preset coordinate system according to the transformation matrix of the specified type of sensor to the preset coordinate system; and calibrating initial external parameter data among the various types of sensors by taking the deviation between the observation pose data of each other type of sensor in the preset coordinate system and the observation pose data of the specified type of sensor in the preset coordinate system and the deviation between the observation pose data of the specified type of sensor in the preset coordinate system and the calibration pose data as optimization targets.
Optionally, before the calibration module 303 calibrates the initial external parameter data between the various types of sensors, the determination module 302 is further configured to select a specific type of sensor from the various types of sensors set by the device to be calibrated;
the calibration module 303 is specifically configured to, for each other type of sensor in the various types of sensors except the specified type of sensor, calibrate initial extrinsic parameter data between the specified type of sensor and the other type of sensor to obtain actual extrinsic parameter data between the specified type of sensor and the other type of sensor, with a goal of minimizing a deviation between the observation pose data of the device to be calibrated under the specified type of sensor and the observation pose data of the device to be calibrated under the other type of sensor, and a deviation between the observation pose data of the device to be calibrated under the specified type of sensor and the calibration pose data as optimization goals; and determining actual external parameter data between the sensors of other types according to the actual external parameter data between the sensor of the specified type and the sensors of other types.
Optionally, the specified type of sensor comprises: an inertial measurement unit IMU.
The present specification also provides a computer readable storage medium storing a computer program, which can be used to execute the data calibration method provided in fig. 1.
This specification also provides a schematic block diagram of the electronic device shown in fig. 4. As shown in fig. 4, at the hardware level, the electronic device includes a processor, an internal bus, a network interface, a memory, and a non-volatile memory, and may also include hardware required for other services. The processor reads a corresponding computer program from the non-volatile memory into the memory and then runs the computer program to implement the data calibration method described in fig. 1. Of course, besides the software implementation, the present specification does not exclude other implementations, such as logic devices or a combination of software and hardware, and the like, that is, the execution subject of the following processing flow is not limited to each logic unit, and may be hardware or logic devices.
In the 90 s of the 20 th century, improvements in a technology could clearly distinguish between improvements in hardware (e.g., improvements in circuit structures such as diodes, transistors, switches, etc.) and improvements in software (improvements in process flow). However, as technology advances, many of today's process flow improvements have been seen as direct improvements in hardware circuit architecture. Designers almost always obtain the corresponding hardware circuit structure by programming an improved method flow into the hardware circuit. Thus, it cannot be said that an improvement in the process flow cannot be realized by hardware physical modules. For example, a Programmable Logic Device (PLD), such as a Field Programmable Gate Array (FPGA), is an integrated circuit whose Logic functions are determined by programming the Device by a user. A digital system is "integrated" on a PLD by the designer's own programming without requiring the chip manufacturer to design and fabricate application-specific integrated circuit chips. Furthermore, nowadays, instead of manually making an Integrated Circuit chip, such Programming is often implemented by "logic compiler" software, which is similar to a software compiler used in program development and writing, but the original code before compiling is also written by a specific Programming Language, which is called Hardware Description Language (HDL), and HDL is not only one but many, such as abel (advanced Boolean Expression Language), ahdl (alternate Hardware Description Language), traffic, pl (core universal Programming Language), HDCal (jhdware Description Language), lang, Lola, HDL, laspam, hardward Description Language (vhr Description Language), vhal (Hardware Description Language), and vhigh-Language, which are currently used in most common. It will also be apparent to those skilled in the art that hardware circuitry that implements the logical method flows can be readily obtained by merely slightly programming the method flows into an integrated circuit using the hardware description languages described above.
The controller may be implemented in any suitable manner, for example, the controller may take the form of, for example, a microprocessor or processor and a computer-readable medium storing computer-readable program code (e.g., software or firmware) executable by the (micro) processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and an embedded microcontroller, examples of which include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320, the memory controller may also be implemented as part of the control logic for the memory. Those skilled in the art will also appreciate that, in addition to implementing the controller as pure computer readable program code, the same functionality can be implemented by logically programming method steps such that the controller is in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers and the like. Such a controller may thus be considered a hardware component, and the means included therein for performing the various functions may also be considered as a structure within the hardware component. Or even means for performing the functions may be regarded as being both a software module for performing the method and a structure within a hardware component.
The systems, devices, modules or units illustrated in the above embodiments may be implemented by a computer chip or an entity, or by a product with certain functions. One typical implementation device is a computer. In particular, the computer may be, for example, a personal computer, a laptop computer, a cellular telephone, a camera phone, a smartphone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
For convenience of description, the above devices are described as being divided into various units by function, and are described separately. Of course, the functions of the various elements may be implemented in the same one or more software and/or hardware implementations of the present description.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In a typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, Random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
Computer-readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), Static Random Access Memory (SRAM), Dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), Read Only Memory (ROM), Electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), Digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer readable medium does not include a transitory computer readable medium such as a modulated data signal and a carrier wave.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As will be appreciated by one skilled in the art, embodiments of the present description may be provided as a method, system, or computer program product. Accordingly, the description may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the description may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
This description may be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform particular tasks or implement particular abstract data types. The specification may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote computer storage media including memory storage devices.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only an example of the present specification, and is not intended to limit the present specification. Various modifications and alterations to this description will become apparent to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present specification should be included in the scope of the claims of the present specification.
Claims (10)
1. A data calibration method is characterized in that equipment to be calibrated is provided with various types of sensors, and comprises the following steps:
acquiring sensing data acquired by each type of sensor set for the equipment to be calibrated;
according to the sensing data acquired by the sensor of the type, determining the observation pose data of the equipment to be calibrated under the sensor of the type, and acquiring the calibration pose data corresponding to the equipment to be calibrated;
and calibrating initial external parameter data among the various types of sensors to calibrate the actual external parameter data among the various types of sensors by taking the minimum deviation between the observation pose data of the equipment to be calibrated under the various types of sensors and the calibration pose data and the deviation between the observation pose data of the equipment to be calibrated under the various types of sensors as optimization targets.
2. The method of claim 1, wherein prior to calibrating the initial extrinsic parameter data between the various types of sensors, the method further comprises:
and optimizing observation pose data of the equipment to be calibrated under the type of sensor according to a preset noise model aiming at each type of sensor arranged on the equipment to be calibrated to obtain the optimized observation pose data of the equipment to be calibrated under the type of sensor.
3. The method according to claim 2, wherein calibrating the initial extrinsic data between the various types of sensors with the objective of minimizing the deviation between the observed pose data of the device to be calibrated under the various types of sensors and the calibration pose data and the deviation between the observed pose data of the device to be calibrated under the various types of sensors comprises:
and taking the minimized deviation between the optimized observation pose data of the equipment to be calibrated under various types of sensors and the calibrated pose data and the deviation between the optimized observation pose data of the equipment to be calibrated under various types of sensors as optimization targets, and calibrating the initial external parameter data between various types of sensors.
4. The method of claim 1, wherein prior to calibrating the initial extrinsic parameter data between the various types of sensors, the method further comprises:
selecting a specified type of sensor from various types of sensors set by the equipment to be calibrated;
taking the minimum deviation between the observation pose data of the equipment to be calibrated under various types of sensors and the calibration pose data and the deviation between the observation pose data of the equipment to be calibrated under various types of sensors as optimization targets, and calibrating the initial external parameter data among the various types of sensors, which specifically comprises the following steps:
determining a preset coordinate system;
for each other type of sensor except the specified type of sensor, determining observation pose data of the other type of sensor in the preset coordinate system according to a transformation matrix of the other type of sensor to the preset coordinate system, and determining observation pose data of the specified type of sensor in the preset coordinate system according to the transformation matrix of the specified type of sensor to the preset coordinate system;
and calibrating initial external parameter data among the various types of sensors by taking the deviation between the observation pose data of each other type of sensor in the preset coordinate system and the observation pose data of the specified type of sensor in the preset coordinate system and the deviation between the observation pose data of the specified type of sensor in the preset coordinate system and the calibration pose data as optimization targets.
5. The method of claim 1, wherein prior to calibrating the initial extrinsic parameter data between the various types of sensors, the method further comprises:
selecting a specified type of sensor from various types of sensors set by the equipment to be calibrated;
taking the minimum deviation between the observation pose data of the equipment to be calibrated under various types of sensors and the calibration pose data and the deviation between the observation pose data of the equipment to be calibrated under various types of sensors as optimization targets, and calibrating the initial external parameter data among the various types of sensors, which specifically comprises the following steps:
for each other type of sensor except the specified type of sensor in the various types of sensors, taking the deviation between the observation pose data of the device to be calibrated under the specified type of sensor and the observation pose data of the device to be calibrated under the other type of sensor and the deviation between the observation pose data of the device to be calibrated under the specified type of sensor and the calibration pose data as optimization targets, and calibrating initial external parameter data between the specified type of sensor and the other type of sensor to obtain actual external parameter data between the specified type of sensor and the other type of sensor;
and determining actual external parameter data between the sensors of other types according to the actual external parameter data between the sensor of the specified type and the sensors of other types.
6. The method of claim 4 or 5, wherein the specifying a type of sensor comprises: an inertial measurement unit IMU.
7. A data calibration system is characterized by comprising equipment to be calibrated, data calibration equipment and a control console, wherein when the equipment to be calibrated is positioned on the control console, the equipment to be calibrated does not move relative to the control console, and the equipment to be calibrated is provided with various sensors;
the control console is used for executing corresponding control actions according to preset control instructions so that the equipment to be calibrated on the control console moves according to the control actions executed by the control console;
the data calibration device is used for acquiring sensing data acquired by each type of sensor contained in the device to be calibrated, determining observation pose data of the device to be calibrated under the type of sensor according to the sensing data acquired by the type of sensor, acquiring calibration pose data corresponding to the device to be calibrated so as to minimize the deviation between the observation pose data of the device to be calibrated under the types of sensors and the calibration pose data, and taking the deviation between the observation pose data of the device to be calibrated under the types of sensors as an optimization target, and calibrating initial external parameter data among the types of sensors so as to calibrate actual external parameter data among the types of sensors.
8. A data calibration device, characterized in that a device to be calibrated is provided with various types of sensors, comprising:
the acquisition module is used for acquiring the sensing data acquired by each type of sensor set by the equipment to be calibrated;
the determining module is used for determining observation pose data of the equipment to be calibrated under the type of sensor according to the sensing data acquired by the type of sensor and acquiring calibration pose data corresponding to the equipment to be calibrated;
and the calibration module is used for calibrating the initial external parameter data among the various types of sensors to calibrate the actual external parameter data among the various types of sensors by taking the minimum deviation between the observation pose data of the equipment to be calibrated under the various types of sensors and the calibration pose data and the deviation between the observation pose data of the equipment to be calibrated under the various types of sensors as optimization targets.
9. A computer-readable storage medium, characterized in that the storage medium stores a computer program which, when executed by a processor, implements the method of any of the preceding claims 1 to 6.
10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the method of any of claims 1 to 6 when executing the program.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112629565A (en) * | 2021-03-08 | 2021-04-09 | 中国人民解放军国防科技大学 | Method, device and equipment for calibrating rotation relation between camera and inertial measurement unit |
| CN112902988A (en) * | 2021-03-12 | 2021-06-04 | Oppo广东移动通信有限公司 | Parameter calibration method, device, terminal and storage medium |
Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140176418A1 (en) * | 2012-12-21 | 2014-06-26 | Qualcomm Incorporated | Display of separate computer vision based pose and inertial sensor based pose |
| US20160356882A1 (en) * | 2013-12-27 | 2016-12-08 | Advanced Telecommunications Research Institute International | Calibration apparatus, calibration method and calibration program |
| JP2017096813A (en) * | 2015-11-25 | 2017-06-01 | 株式会社国際電気通信基礎技術研究所 | Calibration device, calibration method, and calibration program |
| CN108871311A (en) * | 2018-05-31 | 2018-11-23 | 北京字节跳动网络技术有限公司 | Pose determines method and apparatus |
| US20190113537A1 (en) * | 2016-06-03 | 2019-04-18 | SZ DJI Technology Co., Ltd. | Simple multi-sensor calibration |
| CN109712189A (en) * | 2019-03-26 | 2019-05-03 | 深兰人工智能芯片研究院（江苏）有限公司 | A kind of method and apparatus of sensor combined calibrating |
| CN109767475A (en) * | 2018-12-28 | 2019-05-17 | 广州小鹏汽车科技有限公司 | A kind of method for calibrating external parameters and system of sensor |
| CN109901123A (en) * | 2018-12-24 | 2019-06-18 | 文远知行有限公司 | Transducer calibration method, device, computer equipment and storage medium |
| CN109959381A (en) * | 2017-12-22 | 2019-07-02 | 深圳市优必选科技有限公司 | A kind of localization method, device, robot and computer readable storage medium |
| CN110514225A (en) * | 2019-08-29 | 2019-11-29 | 中国矿业大学 | The calibrating external parameters and precise positioning method of Multi-sensor Fusion under a kind of mine |
| WO2020024182A1 (en) * | 2018-08-01 | 2020-02-06 | 深圳市大疆创新科技有限公司 | Parameter processing method and apparatus, camera device and aircraft |
| CN110782496A (en) * | 2019-09-06 | 2020-02-11 | 深圳市道通智能航空技术有限公司 | Calibration method, calibration device, aerial photographing equipment and storage medium |
| US20200081119A1 (en) * | 2018-09-07 | 2020-03-12 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method and apparatus for determining relative pose, device and medium |
| CN110880189A (en) * | 2018-09-06 | 2020-03-13 | 舜宇光学（浙江）研究院有限公司 | Combined calibration method and combined calibration device thereof and electronic equipment |
| CN111207774A (en) * | 2020-01-17 | 2020-05-29 | 山东大学 | Method and system for laser-IMU external reference calibration |
| US10723281B1 (en) * | 2019-03-21 | 2020-07-28 | Lyft, Inc. | Calibration of vehicle sensor array alignment |
| CN111551191A (en) * | 2020-04-28 | 2020-08-18 | 浙江商汤科技开发有限公司 | Sensor external parameter calibration method and device, electronic equipment and storage medium |
| CN111928847A (en) * | 2020-09-22 | 2020-11-13 | 蘑菇车联信息科技有限公司 | Inertial measurement unit pose data optimization method and device and electronic equipment |
-
2020
- 2020-11-23 CN CN202011321128.XA patent/CN112362084A/en not_active Withdrawn
Patent Citations (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20140176418A1 (en) * | 2012-12-21 | 2014-06-26 | Qualcomm Incorporated | Display of separate computer vision based pose and inertial sensor based pose |
| US20160356882A1 (en) * | 2013-12-27 | 2016-12-08 | Advanced Telecommunications Research Institute International | Calibration apparatus, calibration method and calibration program |
| JP2017096813A (en) * | 2015-11-25 | 2017-06-01 | 株式会社国際電気通信基礎技術研究所 | Calibration device, calibration method, and calibration program |
| US20190113537A1 (en) * | 2016-06-03 | 2019-04-18 | SZ DJI Technology Co., Ltd. | Simple multi-sensor calibration |
| CN109959381A (en) * | 2017-12-22 | 2019-07-02 | 深圳市优必选科技有限公司 | A kind of localization method, device, robot and computer readable storage medium |
| CN108871311A (en) * | 2018-05-31 | 2018-11-23 | 北京字节跳动网络技术有限公司 | Pose determines method and apparatus |
| WO2020024182A1 (en) * | 2018-08-01 | 2020-02-06 | 深圳市大疆创新科技有限公司 | Parameter processing method and apparatus, camera device and aircraft |
| CN110880189A (en) * | 2018-09-06 | 2020-03-13 | 舜宇光学（浙江）研究院有限公司 | Combined calibration method and combined calibration device thereof and electronic equipment |
| US20200081119A1 (en) * | 2018-09-07 | 2020-03-12 | Baidu Online Network Technology (Beijing) Co., Ltd. | Method and apparatus for determining relative pose, device and medium |
| CN109901123A (en) * | 2018-12-24 | 2019-06-18 | 文远知行有限公司 | Transducer calibration method, device, computer equipment and storage medium |
| CN109767475A (en) * | 2018-12-28 | 2019-05-17 | 广州小鹏汽车科技有限公司 | A kind of method for calibrating external parameters and system of sensor |
| US10723281B1 (en) * | 2019-03-21 | 2020-07-28 | Lyft, Inc. | Calibration of vehicle sensor array alignment |
| CN109712189A (en) * | 2019-03-26 | 2019-05-03 | 深兰人工智能芯片研究院（江苏）有限公司 | A kind of method and apparatus of sensor combined calibrating |
| CN110514225A (en) * | 2019-08-29 | 2019-11-29 | 中国矿业大学 | The calibrating external parameters and precise positioning method of Multi-sensor Fusion under a kind of mine |
| CN110782496A (en) * | 2019-09-06 | 2020-02-11 | 深圳市道通智能航空技术有限公司 | Calibration method, calibration device, aerial photographing equipment and storage medium |
| CN111207774A (en) * | 2020-01-17 | 2020-05-29 | 山东大学 | Method and system for laser-IMU external reference calibration |
| CN111551191A (en) * | 2020-04-28 | 2020-08-18 | 浙江商汤科技开发有限公司 | Sensor external parameter calibration method and device, electronic equipment and storage medium |
| CN111928847A (en) * | 2020-09-22 | 2020-11-13 | 蘑菇车联信息科技有限公司 | Inertial measurement unit pose data optimization method and device and electronic equipment |
Non-Patent Citations (4)
| Title |
|---|
| 姜广浩等: "基于EKF的摄像机-IMU相对姿态标定方法", 《计算机应用与软件》 * |
| 张慧智等: "基于激光视觉技术的运动目标位姿测量与误差分析", 《激光杂志》 * |
| 杨浩等: "摄像机和惯性测量单元的相对位姿标定方法", 《机器人》 * |
| 陈集辉等: "深度相机与惯性测量单元的相对姿态标定", 《哈尔滨工业大学学报》 * |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112629565A (en) * | 2021-03-08 | 2021-04-09 | 中国人民解放军国防科技大学 | Method, device and equipment for calibrating rotation relation between camera and inertial measurement unit |
| CN112629565B (en) * | 2021-03-08 | 2021-06-01 | 中国人民解放军国防科技大学 | Method, device and equipment for calibrating rotation relation between camera and inertial measurement unit |
| CN112902988A (en) * | 2021-03-12 | 2021-06-04 | Oppo广东移动通信有限公司 | Parameter calibration method, device, terminal and storage medium |
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