CN112051590B - Detection method and related device for laser radar and inertial measurement unit - Google Patents
Detection method and related device for laser radar and inertial measurement unit Download PDFInfo
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Abstract
The embodiment of the invention discloses a detection method and a related device for a laser radar and an inertia measurement unit, wherein the method comprises the following steps: acquiring point cloud data acquired by a laser radar on a local geographic environment and first attitude data detected by an inertial measurement unit when a vehicle runs in the geographic environment; mapping the point cloud data to a preset point cloud map by taking the first position and attitude data as a mapping coefficient to obtain second position and attitude data of the laser radar relative to the point cloud map; and measuring the deviation between the second position and attitude data of the laser radar and the first position and attitude data of the inertial measurement unit in the same coordinate system. The point cloud data are mapped to the point cloud map by utilizing the conversion relation, the point cloud data aligned and corrected with the point cloud map are obtained, a target reference object is not needed, the universality is strong, the deviation calculation method is simple, the automatic detection of the joint calibration parameters of the laser radar and the inertial measurement unit can be realized, and the detection precision and efficiency are improved.
Description
Technical Field
The embodiment of the invention relates to an automatic driving technology, in particular to a detection method and a related device for a laser radar and an inertia measurement unit.
Background
With the development of the unmanned driving technology, a vehicle-mounted laser radar and an Inertial Measurement Unit (IMU) are used as sensors commonly used in a vehicle-mounted automatic driving system for detecting information to perform fusion, perception, planning, decision, control and the like.
In the running process of the vehicle-mounted automatic driving system, inevitable displacement is caused by various reasons (such as collision, hardware disassembly and assembly, long-term bump driving and the like) of each sensor, and changes relative to the original position, so that the original calibration parameters are invalid. Therefore, the periodic detection of the calibration parameters becomes a critical part.
The calibration of the laser radar generally refers to the relative pose of the laser radar and the inertial measurement unit. In the traditional calibration detection method, manual physical measurement is adopted, namely, a measuring tool is used for actually and physically measuring the relative position between the laser radar and other sensors, and the method has low efficiency, large measurement error exists and automatic calibration detection cannot be realized. Therefore, at present, an automatic laser radar calibration method is mostly adopted to detect calibration parameters, that is, a complete automatic millimeter wave radar calibration process is repeated periodically, or data acquisition, detection and matching are performed on a typical target to complete detection. However, although the automatic laser radar calibration method can realize automation, the complete calibration process still needs to be repeated once, and the method is not suitable for the detection requirement of the calibration parameters for long-term high frequency; the precision of data acquisition, detection and matching of typical targets to complete detection is high, but the dependence on specific targets is not beneficial to the general use and the expansion of the detection method.
Disclosure of Invention
The embodiment of the invention provides a detection method and a related device for a laser radar and an inertia measurement unit, and aims to solve the problems of low detection efficiency, complex detection process and poor detection method universality in the prior art.
In a first aspect, an embodiment of the present invention provides a method for detecting a laser radar and an inertial measurement unit, where the method includes:
acquiring point cloud data acquired by a laser radar on a local geographic environment, first attitude data detected by an inertial measurement unit and calibration parameters of the laser radar under the inertial measurement unit when a vehicle runs in the geographic environment;
mapping the point cloud data to a preset point cloud map by taking the first position posture data as a mapping coefficient to obtain second position posture data of the laser radar relative to the point cloud map, wherein the point cloud map is point cloud data of the global geographic environment;
and measuring the deviation between the second attitude data of the laser radar and the first attitude data of the inertial measurement unit in the same coordinate system.
In a second aspect, an embodiment of the present invention further provides a detection apparatus for a laser radar and an inertial measurement unit, where the apparatus includes:
the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring point cloud data acquired by a laser radar in a local geographic environment, first attitude data detected by an inertial measurement unit and calibration parameters of the laser radar under the inertial measurement unit when a vehicle runs in the geographic environment;
the relative pose acquisition module is used for mapping the point cloud data to a preset point cloud map by taking the first position pose data as a mapping coefficient to acquire second position pose data of the laser radar relative to the point cloud map, and the point cloud map is point cloud data of the global geographic environment;
and the deviation calculation module is used for measuring the deviation between the second position and attitude data of the laser radar and the first position and attitude data of the inertial measurement unit in the same coordinate system.
In a third aspect, an embodiment of the present invention further provides a computer device, where the computer device includes:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the lidar and inertial measurement unit detection method of the first aspect.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the detection method for the lidar and the inertial measurement unit according to the first aspect.
The method comprises the steps of acquiring point cloud data acquired by a laser radar on a local geographic environment and first attitude data detected by an inertia measurement unit when a vehicle runs in the geographic environment; mapping the point cloud data to a preset point cloud map by taking the first position and attitude data as a mapping coefficient to obtain second position and attitude data of the laser radar relative to the point cloud map; and measuring the deviation between the second position and attitude data of the laser radar and the first position and attitude data of the inertial measurement unit in the same coordinate system. Wherein, the point cloud map is point cloud data of global geographic environment, the position information of the point cloud map is more accurate, the point cloud data is mapped under a preset point cloud map by utilizing a position conversion relation, the point cloud data of the laser radar aligned and corrected with the point cloud map is obtained, second attitude data of the laser radar and the point cloud map under the same coordinate system is obtained by resolving the point cloud data aligned and corrected, the deviation between the second attitude data and the first attitude data of the inertial measurement unit under the same coordinate system is calculated, whether the calibration parameter of the laser radar is accurate can be measured by utilizing the deviation, the whole process does not need manual intervention, the automatic detection of the calibration parameter of the laser radar can be realized, the dependence on a specific target in the geographic environment can be eliminated, the requirement of daily high-frequency monitoring of the calibration parameter is met, and the whole detection process is simpler, the detection method has the advantages that the calculation resource consumption is low, the detection efficiency can be improved and the detection time can be shortened on the premise of ensuring the detection precision, and meanwhile, the detection method is easy to expand and high in universality.
Drawings
FIG. 1 is a schematic diagram of an unmanned vehicle according to an embodiment of the present invention;
fig. 2 is a flowchart of a detection method of a laser radar and an inertial measurement unit according to an embodiment of the present invention;
fig. 3 is a flowchart of a detection method of a laser radar and an inertial measurement unit according to a second embodiment of the present invention;
fig. 4 is a schematic structural diagram of a detection apparatus for a laser radar and an inertial measurement unit according to a third embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be further noted that, for the convenience of description, only some of the structures related to the present invention are shown in the drawings, not all of the structures.
The process of acquiring the relative pose (including the relative position and orientation) between the sensors is the calibration process between every two sensors. All sensor information can be unified to the same coordinate system for processing through calibration, and fusion, perception, planning, decision, control and the like are carried out on the vehicle multi-sensor detection information based on the environment.
The laser radar plays an important role in the whole vehicle-mounted automatic driving system due to the high resolution, 360-degree all-dimensional and three-dimensional environment sensing capacity.
Before the vehicle-mounted automatic driving system is started, in order to fuse measured values between a laser radar and an inertia measurement unit which are installed on a vehicle, initial pose data jointly calibrated by the laser radar and the inertia measurement unit needs to be obtained in advance and used as test pose data (namely calibration parameters to be detected).
Referring to fig. 1, there is shown an unmanned vehicle 100 to which embodiments of the detection method of the lidar and inertial measurement unit, and the detection apparatus of the lidar and inertial measurement unit of the present invention may be applied.
As shown in fig. 1, the unmanned vehicle 100 may include a driving Control device 101, a vehicle body bus 102, an ECU (Electronic Control Unit) 103, an ECU 104, an ECU105, a sensor 106, a sensor 107, a sensor 108, and an actuator 109, an actuator 110, and an actuator 111.
A driving control device (also referred to as an in-vehicle brain) 101 is responsible for overall intelligent control of the entire unmanned vehicle 100. The driving control device 101 may be a controller that is separately provided, such as a Programmable Logic Controller (PLC), a single chip microcomputer, an industrial controller, and the like; or the equipment consists of other electronic devices which have input/output ports and have the operation control function; but also a computer device installed with a vehicle driving control type application. The driving control device can analyze and process the data sent by each ECU and/or the data sent by each sensor received from the vehicle body bus 102, make a corresponding decision, and send an instruction corresponding to the decision to the vehicle body bus.
The body bus 102 may be a bus for connecting the driving control apparatus 101, the ECU 103, the ECU 104, the ECU105, the sensor 106, the sensor 107, the sensor 108, and other devices of the unmanned vehicle 100, which are not shown. Since the high performance and reliability of a CAN (Controller area network) bus are widely accepted, a vehicle body bus commonly used in a motor vehicle is a CAN bus. Of course, it is understood that the body bus may be other types of buses.
The vehicle body bus 102 may transmit the instruction sent by the driving control device 101 to the ECU 103, the ECU 104, and the ECU105, and the ECU 103, the ECU 104, and the ECU105 analyze and process the instruction and send the instruction to the corresponding execution device for execution.
The sensors 106, 107, 108 include, but are not limited to, laser radars, cameras, inertial measurement units, millimeter wave radars, and the like.
The laser radar is a device for detecting and measuring distance of an object by using laser as a sensor commonly used in the field of unmanned driving, and the sensor is internally provided with a rotating structure and can send millions of light pulses to the environment every second and output point cloud data.
Cameras are generally used to take pictures of the surroundings of an unmanned vehicle and record the scene in which the vehicle is traveling.
An Inertial Measurement Unit (IMU) is a device that measures the three-axis attitude angle (or angular velocity) and acceleration of an object, and can provide precise coordinates of a vehicle in a world coordinate system. Generally, an inertial measurement unit comprises three single-axis accelerometers and three single-axis gyroscopes, wherein the accelerometers are also called gravity sensors and are used for detecting acceleration signals of an object in independent three axes of a carrier coordinate system, namely, the magnitude and direction of acceleration in an axial direction are obtained by measuring the stress condition of a component in a certain axial direction; and the gyroscope is also called as a ground sensor and is used for detecting an angular velocity signal of the carrier relative to a navigation coordinate system, measuring the angular velocity and the acceleration of the object in a three-dimensional space, and calculating the attitude angle of the object according to the angular velocity and the acceleration, wherein the attitude angle comprises a course angle, a pitch angle and a roll angle. Some inertial measurement units also integrate magnetometers, which are also called geomagnetic and magnetic sensors, and can be used for testing the intensity and direction of magnetic field and positioning the orientation of equipment.
The millimeter wave Radar (Radar) is a Radar which operates in a millimeter wave band for detection, generally, the millimeter wave refers to an electromagnetic wave with a length of 1-10 mm, and the corresponding frequency range is 30-300 GHz. The millimeter wave radar can realize accurate measurement of information such as target position, speed and the like, and has the characteristics of all-time, all-weather, low cost, low power consumption and long service life. The millimeter wave radar can distinguish and identify very small targets and can identify a plurality of targets simultaneously. The principle of the millimeter wave radar is to emit electromagnetic waves (millimeter waves), receive echoes, and measure position data and relative distance of a target according to a time difference between the transmission and reception. From the propagation velocity of the electromagnetic wave, the distance formula of the target can be determined as: and s is c t/2, wherein s is the target distance, t is the time from the emission of the electromagnetic wave from the radar to the reception of the target echo, and c is the speed of light. The basic task of the millimeter wave radar is to emit electromagnetic waves to irradiate a target and receive the echo of the target, so as to obtain state parameters such as the detection distance, the direction and the speed from the target to an electromagnetic wave emission point. Millimeter wave radars are widely used in vehicle driving assistance systems such as Adaptive Cruise Control (ACC), forward collision avoidance (FCW), Blind Spot Detection (BSD), assisted Parking (PA), assisted Lane Change (LCA), and the like.
It should be noted that the detection method of the lidar and the inertial measurement unit provided by the embodiment of the present invention may be executed by the driving control apparatus 101, and accordingly, the detection device of the lidar and the inertial measurement unit is generally disposed in the driving control apparatus 101.
It should be understood that the numbers of unmanned vehicles, driving control devices, body buses, ECUs, actuators, and sensors in fig. 1 are merely illustrative. There may be any number of unmanned vehicles, driving control devices, body buses, ECUs, and sensors, as desired for implementation.
Example one
Fig. 2 is a flowchart of a detection method for a lidar and an inertial measurement unit according to an embodiment of the present invention, where this embodiment is applicable to a case where point cloud data of the lidar is mapped to a point cloud map to obtain pose data, and the pose data is compared with calibration parameters to detect calibration parameters of the lidar under the inertial measurement unit, and the method may be executed by a detection apparatus for the lidar and the inertial measurement unit, where the detection apparatus may be implemented by software and/or hardware, and may be configured in computer equipment, such as unmanned equipment for unmanned vehicles, robots, unmanned aerial vehicles, and the like, and computing equipment for servers, personal computers, and the like, and the method specifically includes the following steps:
s101, point cloud data collected by a laser radar on a local geographic environment and first attitude data detected by an inertial measurement unit are obtained when a vehicle runs in the geographic environment.
In this embodiment, the geographic environment may be an environment in which the surrounding environment does not cause significant signal interference to the onboard sensors. The geographic environment has information similar to wall surfaces, buildings and the like, and is generally a relatively wide outdoor environment. Most of the vehicle test special sites or general city streets all meet the requirements, so the embodiment has better universality.
When the vehicle runs in the designated geographic environment, the laser radar and the inertial measurement unit can carry out information detection on the geographic environment so as to obtain point cloud data and first position and orientation data.
The point cloud data may be a reflected signal carrying information such as azimuth and distance reflected when the lidar signal irradiates the surface of an object in the geographic environment, and the point cloud data may include position information of a plurality of points and intensity information of the reflected lidar signal corresponding to the position information.
The inertial measurement unit carries out integral calculation on attitude angles (or angular rates) and accelerated speeds acquired in the running process of the vehicle, posture data of the vehicle in a world coordinate system in the whole running process are detected and obtained, the posture data comprise position coordinates and attitude angles (a course angle, a pitch angle and a roll angle) of the vehicle in the world coordinate system, and fused data of the position coordinates and the attitude angles are used as first attitude data detected by the inertial measurement unit.
Due to the fact that the working frequencies of the laser radar and the inertia measurement unit are different, and the problems of delay in data processing and network transmission exist, and the like, the problem of data asynchronization exists between point cloud data detected by the laser radar and first attitude data detected by the inertia measurement unit. Therefore, data synchronization of the point cloud data and the first pose data is required.
The method comprises the steps that point cloud data acquired by a laser radar and first position and orientation data detected by an inertial measurement unit are generally measured by taking a frame as a unit, and a point cloud data frame sequence and a position and orientation data frame sequence are respectively obtained, namely multi-frame point cloud data are acquired by the laser radar, a timestamp for acquiring the current frame and the current moment is marked on each frame of point cloud data, multi-frame first position and orientation data are also acquired by the inertial measurement unit, a timestamp for acquiring the current frame and the current moment is also marked on each frame of first position and orientation data, wherein each frame of point cloud data comprises three-dimensional space position information of a plurality of measurement points in a coordinate system taking the laser radar as a center, and each frame of first position and orientation data comprises three-dimensional space position information of a vehicle at the current moment under a world coordinate system.
In this embodiment, an interframe synchronization method may be adopted to perform data synchronization on the point cloud data and the first pose data. The frequency of the data frame of the inertial measurement unit is generally much higher than that of the data frame of the laser radar, so the inter-frame synchronization adopts a time sequence matching mode by taking the data frame of the laser radar as a reference.
Illustratively, the data frame sequence of the laser radar and the data frame sequence of the inertial measurement unit are respectively ordered from small to large according to the time stamps; searching a frame of first position data of an inertial measurement unit closest to each frame of point cloud data of the laser radar on a time axis; and traversing all data frames of the laser radar, and finding out a frame of first position and orientation data corresponding to each frame of point cloud data, thereby completing the data synchronization between the multi-frame point cloud data and the multi-frame first position and orientation data.
And S102, mapping the point cloud data to a preset point cloud map by taking the first position and attitude data as a mapping coefficient, and obtaining second position and attitude data of the laser radar relative to the point cloud map.
In this embodiment, the preset point cloud map is point cloud data of a global geographic environment, and the point cloud map may be obtained in an existing map library, or may be acquired in field data acquisition and offline spliced, for example, by acquiring multi-frame point cloud data in a specified global geographic environment, performing data correction on the multi-frame point cloud data, and then performing matching and splicing on the multi-frame point cloud data based on a high-precision pose solved by a GPS or an inertial navigation system for the global geographic environment. Because the geographic environment that the vehicle travels is generally a comparatively open outdoor environment, and the environment has information like wall surfaces, buildings and the like, the method is beneficial to the GPS to obtain an accurate positioning result and an accurate point cloud matching result. Therefore, the position accuracy of each measuring point in the preset point cloud map is high.
In this embodiment, multiple frames of point cloud data and multiple frames of first pose data after data synchronization are obtained, and at the same time, the point cloud data of the current frame synchronized with the first pose data is mapped to a preset point cloud map by using the first pose data of the current frame as a mapping coefficient, so that a frame of second pose data of the laser radar under the current frame relative to the point cloud map can be obtained. And performing mapping operation on each frame of point cloud data of the laser radar, mapping all the frame of point cloud data under the point cloud map, and finally obtaining multi-frame second position posture data of the laser radar relative to the point cloud map.
The essence of mapping is to convert the original spatial coordinates of each point in the point cloud data into another spatial coordinate system through a spatial position conversion relationship to obtain different expressions of the coordinates of the same point, for example, converting the coordinates of one spatial point in the lidar coordinate system into the coordinates in the world coordinate system. There are many ways of mapping, and the embodiment of the present invention is not limited thereto.
S103, measuring the deviation between the second position and attitude data of the laser radar and the first position and attitude data of the inertial measurement unit in the same coordinate system.
In this embodiment, multi-frame point cloud data detected by a laser radar is mapped one by one to a preset point cloud map to obtain multi-frame second position and posture data of the laser radar relative to the point cloud map, because the multi-frame point cloud data and the multi-frame first position and posture data of an inertia measurement unit are subjected to data synchronization in advance according to a timestamp, the multi-frame second position and posture data are position and posture data obtained after position conversion of the multi-frame point cloud data, and the second position and posture data are synchronized with the timestamp of the point cloud data, the timestamp of the multi-frame second position and the timestamp of the multi-frame first position and posture data are also synchronized, and each frame of second position and posture data has one frame of first position and posture data corresponding to the second position and posture data.
Specifically, the deviation between each frame of second position data of the laser radar and each frame of first position data of the inertial measurement unit in the same coordinate system may be measured respectively according to a time sequence, the deviations of all data frames are counted, and the deviation between the second position data of the laser radar and the first position data of the inertial measurement unit in the same coordinate system is finally obtained.
The deviation is used for detecting the combined calibration pose (also called calibration parameter) of the laser radar and the inertial measurement unit, and the detection result is obtained.
The method comprises the steps of acquiring point cloud data acquired by a laser radar on a local geographic environment and first attitude data detected by an inertia measurement unit when a vehicle runs in the geographic environment; mapping the point cloud data to a preset point cloud map by taking the first position and attitude data as a mapping coefficient to obtain second position and attitude data of the laser radar relative to the point cloud map; and measuring the deviation between the second position and attitude data of the laser radar and the first position and attitude data of the inertial measurement unit in the same coordinate system. Wherein, the point cloud map is point cloud data of global geographic environment, the position information of the point cloud map is more accurate, the point cloud data is mapped under a preset point cloud map by utilizing a position conversion relation, the point cloud data of the laser radar aligned and corrected with the point cloud map is obtained, second attitude data of the laser radar and the point cloud map under the same coordinate system is obtained by resolving the point cloud data aligned and corrected, the deviation between the second attitude data and the first attitude data of the inertial measurement unit under the same coordinate system is calculated, whether the calibration parameter of the laser radar is accurate can be measured by utilizing the deviation, the whole process does not need manual intervention, the automatic detection of the calibration parameter of the laser radar can be realized, the dependence on a specific target in the geographic environment can be eliminated, the requirement of daily high-frequency monitoring of the calibration parameter is met, and the whole detection process is simpler, the detection method has the advantages that the calculation resource consumption is low, the detection efficiency can be improved and the detection time can be shortened on the premise of ensuring the detection precision, and meanwhile, the detection method is easy to expand and high in universality.
Example two
Fig. 3 is a flowchart of a detection method for a lidar and an inertial measurement unit according to a second embodiment of the present invention, where the present embodiment is based on the foregoing embodiment, and supplements and refines the content of the detection method for the lidar and the inertial measurement unit, and the method specifically includes the following steps:
s201, point cloud data collected by a laser radar on a local geographic environment and first attitude data detected by an inertial measurement unit are obtained when a vehicle runs in the specified geographic environment.
The method comprises the steps of obtaining multi-frame point cloud data (each frame of point cloud data can cover an annular view angle of 360 degrees) of a laser radar in the whole vehicle running process, obtaining multi-frame pose data of a vehicle detected by an inertial measurement unit in a world coordinate system in the whole running process, and using the multi-frame pose data as multi-frame first pose data, wherein each frame of first pose data can be represented as a pose transformation matrix T (comprising a rotation matrix R and a translational vector T).
S202, acquiring the combined calibration pose of the laser radar and the inertial measurement unit as test pose data.
In the process of data fusion of different sensors, different sensors have different coordinate systems, and the conversion of data in different coordinate systems requires the use of calibration parameters between two coordinate systems, the calibration parameters are also the combined calibration poses of the two sensors, and are usually represented as a pose transformation matrix T, or a rotation matrix R and a translational vector T, wherein the rotation matrix R can be obtained by the conversion of attitude angles (heading angle, pitch angle, roll angle), and the rotation matrix R can also be converted into a quaternion Q (a hyper-complex consisting of real numbers and imaginary numbers).
The joint calibration of the laser radar and the inertial measurement unit refers to converting a data frame (point cloud data) of the laser radar and a data frame (pose data) of the inertial measurement unit into data in the same coordinate system, wherein the data frame (point cloud data) of the laser radar to be calibrated is converted into the coordinate system of the data frame (pose data) of the inertial measurement unit, the data frame (pose data) of the inertial measurement unit is converted into the coordinate system of the data frame (point cloud data) of the laser radar to be calibrated, and the data frame (point cloud data) of the laser radar to be calibrated and the data frame (pose data) of the inertial measurement unit are converted into a third-party coordinate system.
In this embodiment, initial pose data obtained when the laser radar and the inertial measurement unit perform joint calibration at a certain stationary time may be obtained by a manual measurement method or other calibration devices, and used as test pose data (i.e., calibration parameters to be detected), where the test pose data may be represented as a pose transformation matrix T (including a rotation matrix R and a translational vector T).
And S203, taking the product of the test pose data and the first pose data as a mapping coefficient, and mapping the point cloud data to a preset point cloud map to obtain second pose data of the laser radar relative to the point cloud map.
In this embodiment, the point cloud data may be mapped to a preset point cloud map by a preset point cloud registration algorithm, so as to obtain second pose data of the laser radar relative to the point cloud map. The point cloud registration refers to converting two frames of point cloud data into the same coordinate system to obtain a relative pose transformation between the two frames of point cloud data, wherein the relative pose transformation is usually represented by a pose transformation matrix. The Point cloud registration algorithm may be based on an Iterative Closest Point (ICP) algorithm or a Normal Distribution Transform (NDT) algorithm, which is not limited in the present invention.
In order to avoid the point cloud registration algorithm from falling into a local optimal solution, an initial pose transformation matrix is usually required to be given. The method comprises the steps of obtaining first position data of a laser radar, obtaining test position data of a point cloud map, obtaining first position data of a current frame, obtaining a first position data of the laser radar, obtaining second position data of the laser radar, and obtaining a second position data of the laser radar.
In one example, a Normal Distribution Transform (NDT) algorithm is employed for point cloud registration. For any two frames of point clouds Ci、CjUnder the condition of giving an initial value, the NDT algorithm can accurately calculate the relative pose transformation between two frames of point clouds and represents the relative pose transformation as a pose transformation matrix Ti,jTwo frame point clouds using NDT algorithmThe expression of registration is denoted as Ti,j＝NDT(Ci,Cj)。
For two frame point clouds Ci、CjThe same point P on the corresponding real object in (1)i、Pj，Ti,jCan satisfy a transformation relationship, denoted herein as Pi＝Ti,jPj。
In this example, before NDT registration is performed on each frame of point cloud and point cloud map, the problem of initial value needs to be solved, and the first pose data of the current frame is recorded as { H }k}k＝1,2,…,kThat is, the pose data of the current frame vehicle in the world coordinate system, and the test pose data (i.e., the calibration parameters to be detected) are
The initial value required for NDT registration of the point cloud data of the current frame is
Can be expressed as
Thus, each frame of point cloud data CkAnd point cloud map CmapNDT registration is carried out to obtain second position and attitude data T of the laser radar relative to the point cloud mapmap,kThe NDT registration expression at this time may be expressed as Tmap,k＝NDT(Cmap,Ck) K is 1,2, …, K. It should be noted that K is used to indicate a frame number, and K is used to indicate the total number of frames.
The point cloud data are mapped to the preset point cloud map, so that the problems of inaccuracy caused by manual physical measurement and low efficiency caused by methods such as visual marking, manual calibration, repeated automatic calibration and the like can be effectively solved, the point cloud map is prepared in advance, the method does not need to depend on typical targets in a geographic environment, the second attitude data are obtained by adopting a point cloud registration mode, the calculation process is high in efficiency, the implementation is simple, the result is reliable, the method has good universality and expandability, and the detection requirement of high frequency for a long time on calibration parameters can be met.
And S204, converting the second position data of the laser radar into a coordinate system of the inertial measurement unit through the first position data of the inertial measurement unit to obtain third position data of the laser radar.
In this embodiment, the second pose data of the laser radar relative to the multiple frames of the point cloud map are all pose data of the laser radar in the world coordinate system at different times. In order to detect the joint calibration parameters of the laser radar and the inertial measurement unit, the second attitude data needs to be converted into a coordinate system of the inertial measurement unit.
Specifically, according to the sequence of the corresponding data frames, each frame of second position data of the laser radar is converted into the coordinate system of the inertial measurement unit through each frame of first position data of the inertial measurement unit corresponding to the second position data of the laser radar, so that third position data of the current frame of the laser radar in the coordinate system of the inertial measurement unit is obtained, and all data frames (namely, multiple frames of second position data) of the laser radar are converted into multiple frames of third position data in the coordinate system of the inertial measurement unit.
In one example, an inverse of a pose transformation matrix represented by each frame of first pose data for an inertial measurement unit is solved
Will be provided with
A pose transformation matrix T of a frame of second pose data corresponding to the first pose data is pre-multipliedmap,kCalculating the relative position of the laser radar and the inertial measurement unit under the current frame, namely calculating the third position data of the laser radar under the coordinate system of the inertial measurement unit
The calculation method is
Then need to be
And
and comparing, namely comparing the third pose data of each frame with the test pose data.
And S205, measuring the deviation of the joint calibration parameters of the laser radar and the inertia measurement unit based on the third attitude data.
In this embodiment, the test pose data is a pre-acquired pose jointly calibrated by the laser radar and the inertial measurement unit, and in order to detect the joint calibration parameter of the laser radar and the inertial measurement unit, the third pose data of each frame may be compared with the test pose data, and the comparison results of all frames may be counted to measure the deviation of the joint calibration parameter of the laser radar and the inertial measurement unit.
As an example, for convenience of calculation, the pose transformation matrix representing the third pose data is converted into a first translation vector and a first quaternion, and the pose transformation matrix representing the test pose data is converted into a second translation vector and a second quaternion to calculate a deviation between the third pose data and the test pose data of the laser radar.
Specifically, the pose transformation matrix of 4 × 4 is converted into a translation vector t (representing translation) of 1 × 3 and a quaternion Q (representing rotation) of 1 × 4, as shown in the following formula:
wherein,
is a pose transformation matrix of the third pose data,
to test the pose transformation matrix of the pose data,
in order to be the first translation vector, the translation vector,
is a first quaternion, and is,
in order to be the second translation vector, the translation vector,
is a second quaternion.
Calculating a translational deviation between the first translational vector and the second translational vector, as shown in the following formula:
calculating a quaternion offset between the first quaternion and the second quaternion, comprising: obtaining the absolute value of the difference between the first quaternion and the second quaternion, i.e. obtaining the absolute value of the difference
Obtaining the absolute value of the sum between the first quaternion and the second quaternion, i.e. the sum
And calculating the minimum value between the absolute value of the difference value and the absolute value of the sum value to obtain the quaternion deviation. As shown in the following equation:
and fusing the translation deviation and the quaternion deviation to obtain the deviation of the joint calibration parameters of the laser radar and the inertia measurement unit.
In this example, the preset weight may be multiplied by the four-element deviation and added to the translational deviation to obtain the total deviation, as shown in the following formula:
wherein μ is a weight parameter for adjusting the weight between the translational deviation and the quaternion deviation, and the weight can be selected from a test value or an empirical value.
Calculating the average deviation of the total deviation D to obtain the deviation of the combined calibration parameters of the laser radar and the inertia measurement unit
As shown in the following equation:
in this example, the average deviation is calculated for the total deviation, so that the influence of the geographic environment on the detection result can be reduced, and the result of detecting the joint calibration parameters of the laser radar and the inertial measurement unit based on the deviation is more reliable.
And S206, comparing the deviation with a preset threshold value.
In this embodiment, whether the joint calibration parameter of the laser radar and the inertia measurement unit passes the detection may be determined according to a result of comparing the deviation with a preset threshold.
The preset threshold value has no exact range and can change along with the change of factors such as the actual geographic environment, the data acquisition time and the like, so that a reasonable numerical value needs to be adjusted according to the actual running condition of the vehicle. If the deviation is greater than or equal to the preset threshold value, the calibration parameters are accurate and pass the detection.
And S207, if the deviation is larger than or equal to the threshold value, determining that the joint calibration parameters of the laser radar and the inertia measurement unit pass the detection.
And S208, if the deviation is smaller than the threshold value, determining that the joint calibration parameters of the laser radar and the inertia measurement unit fail to be detected.
In this embodiment, a predetermined threshold is given as α if the deviation is large
Judging that the initial pose data jointly calibrated by the laser radar and the inertial measurement unit is accurate and passing the detection; if there is a deviation
And judging that the initial pose data jointly calibrated by the laser radar and the inertial measurement unit is inaccurate and fails to pass the detection.
EXAMPLE III
Fig. 4 is a schematic structural diagram of a detection apparatus for a laser radar and an inertial measurement unit according to a third embodiment of the present invention, where the detection apparatus may specifically include the following modules:
the data acquisition module 401 is configured to acquire point cloud data acquired by a laser radar in a local geographic environment, first attitude data detected by an inertial measurement unit, and calibration parameters of the laser radar under the inertial measurement unit when a vehicle runs in the geographic environment;
a relative pose acquisition module 402, configured to map the point cloud data to a preset point cloud map by using the first pose data as a mapping coefficient, so as to obtain second pose data of the laser radar relative to the point cloud map, where the point cloud map is point cloud data of the global geographic environment;
a deviation calculating module 403, configured to measure a deviation between the second position and orientation data of the laser radar and the first position and orientation data of the inertial measurement unit in the same coordinate system.
In one embodiment of the present invention, the relative pose acquisition module 402 includes:
the test pose data acquisition sub-module is used for acquiring the pose jointly calibrated by the laser radar and the inertial measurement unit as test pose data;
and the data mapping sub-module is used for mapping the point cloud data to a preset point cloud map by taking the product of the test pose data and the first pose data as a mapping coefficient so as to obtain second pose data of the laser radar relative to the point cloud map.
In one embodiment of the present invention, the data mapping sub-module includes:
the coefficient acquisition unit is used for multiplying the first pose data by the test pose data to obtain a mapping coefficient;
and the point cloud registration unit is used for mapping the point cloud data to a preset point cloud map by taking the coefficient as an initial value of a preset point cloud registration algorithm so as to obtain second position posture data of the laser radar relative to the point cloud map.
In one embodiment of the present invention, the deviation calculation module 403 includes:
the position and posture conversion sub-module is used for converting the second position and posture data of the laser radar to a coordinate system of the inertial measurement unit through the first position and posture data of the inertial measurement unit to obtain third position and posture data of the laser radar;
and the deviation measurement submodule is used for measuring the deviation of the joint calibration parameters of the laser radar and the inertia measurement unit based on the third attitude data.
In one embodiment of the invention, the deviation measurement submodule comprises:
the test pose data acquisition unit is used for acquiring the pose jointly calibrated by the laser radar and the inertial measurement unit as test pose data;
the first attitude conversion unit is used for converting the third attitude data into a first translation vector and a first quaternion;
the second pose conversion unit is used for converting the test pose data into a second translation vector and a second quaternion;
a translation deviation calculation unit for calculating a translation deviation between the first translation vector and the second translation vector;
a quaternion deviation calculating unit for calculating a quaternion deviation between the first quaternion and the second quaternion;
and the fusion calculation unit is used for fusing the translation deviation and the quaternion deviation to obtain the deviation of the joint calibration parameters of the laser radar and the inertial measurement unit.
In one embodiment of the present invention, the quaternion deviation calculating unit includes:
a difference calculation subunit, configured to obtain an absolute value of a difference between the first quaternion and the second quaternion;
the sum operator unit is used for acquiring the absolute value of the sum between the first quaternion and the second quaternion;
and the minimum value obtaining subunit is used for calculating the minimum value between the absolute value of the difference value and the absolute value of the sum value to obtain the quaternion deviation.
In one embodiment of the present invention, the fusion calculation unit includes:
the total deviation calculating subunit is used for multiplying a preset weight by the four-element deviation and adding the translation deviation to obtain a total deviation;
and the average deviation calculation subunit is used for solving the average deviation of the total deviation to obtain the deviation of the joint calibration parameters of the laser radar and the inertia measurement unit.
In an embodiment of the present invention, the detection apparatus for lidar and inertial measurement unit further includes:
the threshold value comparison module is used for comparing the deviation with a preset threshold value;
the first detection module is used for determining that the joint calibration parameters of the laser radar and the inertial measurement unit pass detection if the deviation is greater than or equal to the threshold value;
and the second detection module is used for determining that the joint calibration parameters of the laser radar and the inertial measurement unit are not detected if the deviation is smaller than the threshold value.
The detection device for the laser radar and the inertia measurement unit provided by the embodiment of the invention can execute the detection method for the laser radar and the inertia measurement unit provided by any embodiment of the invention, and has corresponding functional modules and beneficial effects of the execution method.
Example four
Fig. 5 is a schematic structural diagram of a computer device according to a fourth embodiment of the present invention. As shown in fig. 5, the computer apparatus includes a processor 500, a memory 501, a communication module 502, an input device 503, and an output device 504; the number of the processors 500 in the computer device may be one or more, and one processor 500 is taken as an example in fig. 5; the processor 500, the memory 501, the communication module 502, the input device 503 and the output device 504 in the computer apparatus may be connected by a bus or other means, and fig. 5 illustrates the connection by a bus as an example.
The memory 501 is a computer-readable storage medium, and can be used to store software programs, computer-executable programs, and modules, such as modules corresponding to the detection method of the lidar and inertial measurement unit in the present embodiment (for example, the data acquisition module 401, the relative pose acquisition module 402, and the deviation calculation module 403 in the detection apparatus of the lidar and inertial measurement unit shown in fig. 4). The processor 500 executes various functional applications and data processing of the computer device by executing software programs, instructions and modules stored in the memory 501, so as to implement the detection method of the lidar and the inertial measurement unit.
The memory 501 may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the computer device, and the like. Further, the memory 501 may include high speed random access memory, and may also include non-volatile memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some examples, the memory 501 may further include memory located remotely from the processor 500, which may be connected to a computer device through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
And the communication module 502 is used for establishing connection with the display screen and realizing data interaction with the display screen.
The input means 503 may be used to receive input numeric or character information and generate key signal inputs related to user settings and function control of the computer device, and may also be a camera for acquiring images and a sound pickup device for acquiring audio data.
The output device 504 may include an audio device such as a speaker.
The specific composition of the input device 503 and the output device 504 can be set according to actual conditions.
The processor 500 executes various functional applications and data processing of the device by executing software programs, instructions and modules stored in the memory 501, so as to implement the detection method of the lidar and the inertial measurement unit.
The computer device provided by the embodiment of the invention can execute the detection method of the laser radar and the inertia measurement unit provided by any embodiment of the invention, and has corresponding functions and beneficial effects.
EXAMPLE five
An embodiment of the present invention further provides a storage medium containing computer-executable instructions, which when executed by a computer processor, are configured to perform a method for detecting a lidar and an inertial measurement unit, the method including:
acquiring point cloud data acquired by a local geographic environment and first attitude data detected by an inertial measurement unit by a laser radar when a vehicle runs in the geographic environment;
mapping the point cloud data to a preset point cloud map by taking the first position posture data as a mapping coefficient to obtain second position posture data of the laser radar relative to the point cloud map, wherein the point cloud map is point cloud data of the global geographic environment;
and measuring the deviation between the second attitude data of the laser radar and the first attitude data of the inertial measurement unit in the same coordinate system.
Of course, the storage medium provided by the embodiment of the present invention contains computer-executable instructions, and the computer-executable instructions are not limited to the operations of the method described above, and may also perform related operations in the detection method of the lidar and the inertial measurement unit provided by any embodiment of the present invention.
From the above description of the embodiments, it is obvious for those skilled in the art that the present invention can be implemented by software and necessary general hardware, and certainly, can also be implemented by hardware, but the former is a better embodiment in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which can be stored in a computer-readable storage medium, such as a floppy disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a FLASH Memory (FLASH), a hard disk or an optical disk of a computer, and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device) to execute the methods according to the embodiments of the present invention.
It should be noted that, in the embodiment of the detection apparatus for a laser radar and an inertial measurement unit, each included unit and module are only divided according to functional logic, but are not limited to the above division, as long as the corresponding function can be realized; in addition, specific names of the functional units are only for convenience of distinguishing from each other, and are not used for limiting the protection scope of the present invention.
It is to be noted that the foregoing is only illustrative of the preferred embodiments of the present invention and the technical principles employed. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, although the present invention has been described in greater detail by the above embodiments, the present invention is not limited to the above embodiments, and may include other equivalent embodiments without departing from the spirit of the present invention, and the scope of the present invention is determined by the scope of the appended claims.
Claims (9)
1. A detection method for a laser radar and an inertial measurement unit is characterized by comprising the following steps:
acquiring point cloud data acquired by a local geographic environment and first attitude data detected by an inertial measurement unit by a laser radar when a vehicle runs in the geographic environment;
acquiring a combined calibration pose of the laser radar and the inertial measurement unit as test pose data; the first pose data is multiplied by the test pose data to obtain a mapping coefficient; mapping the point cloud data to a preset point cloud map by taking the coefficient as an initial value of a preset point cloud registration algorithm to obtain second attitude data of the laser radar relative to the point cloud map, wherein the point cloud map is the point cloud data of the global geographic environment;
and measuring the deviation between the second attitude data of the laser radar and the first attitude data of the inertial measurement unit in the same coordinate system.
2. The method of claim 1, wherein the measuring a deviation between the second attitude data of the lidar and the first attitude data of the inertial measurement unit in a same coordinate system comprises:
converting the second position and attitude data of the laser radar to a coordinate system of the inertial measurement unit through the first position and attitude data of the inertial measurement unit to obtain third position and attitude data of the laser radar;
and measuring the deviation of the joint calibration parameters of the laser radar and the inertial measurement unit based on the third attitude data.
3. The method of claim 2, wherein measuring the deviation of the lidar and the inertial measurement unit joint calibration parameters based on the third attitude data comprises:
acquiring a combined calibration pose of the laser radar and the inertial measurement unit as test pose data;
converting the third pose data into a first translation vector and a first quaternion;
converting the test pose data into a second translation vector and a second quaternion;
calculating a translational offset between the first translational vector and the second translational vector;
calculating a quaternion offset between the first quaternion and the second quaternion;
and fusing the translation deviation and the quaternion deviation to obtain the deviation of the joint calibration parameters of the laser radar and the inertial measurement unit.
4. The method of claim 3, wherein calculating a quaternion offset between the first quaternion and the second quaternion comprises:
acquiring an absolute value of a difference value between the first quaternion and the second quaternion;
acquiring an absolute value of a sum value between the first quaternion and the second quaternion;
and calculating the minimum value between the absolute value of the difference value and the absolute value of the sum value to obtain the quaternion deviation.
5. The method according to claim 3, wherein the fusing the translational deviation and the quaternion deviation to obtain a deviation of the joint calibration parameter of the lidar and the inertial measurement unit comprises:
multiplying a preset weight by the quaternion deviation and adding the translation deviation to obtain a total deviation;
and calculating the average deviation of the total deviation to obtain the deviation of the joint calibration parameters of the laser radar and the inertial measurement unit.
6. The method of claim 1, further comprising:
comparing the deviation with a preset threshold value;
if the deviation is larger than or equal to the threshold value, determining that the joint calibration parameters of the laser radar and the inertial measurement unit pass detection;
and if the deviation is smaller than the threshold value, determining that the joint calibration parameters of the laser radar and the inertial measurement unit fail to be detected.
7. A detection device for a laser radar and an inertial measurement unit is characterized by comprising:
the system comprises a data acquisition module, a data acquisition module and a data processing module, wherein the data acquisition module is used for acquiring point cloud data acquired by a laser radar in a local geographic environment, first attitude data detected by an inertial measurement unit and calibration parameters of the laser radar under the inertial measurement unit when a vehicle runs in the geographic environment;
the relative pose acquisition module is used for obtaining a pose jointly calibrated by the laser radar and the inertial measurement unit as test pose data, carrying out left multiplication on the first pose data by the test pose data to obtain a mapping coefficient, taking the coefficient as an initial value of a preset point cloud registration algorithm, mapping the point cloud data to a preset point cloud map, and obtaining second pose data of the laser radar relative to the point cloud map, wherein the point cloud map is point cloud data of the global geographic environment;
and the deviation calculation module is used for measuring the deviation between the second position and attitude data of the laser radar and the first position and attitude data of the inertial measurement unit in the same coordinate system.
8. A computer device, characterized in that the computer device comprises:
one or more processors;
a memory for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the lidar and inertial measurement unit detection method of any of claims 1-6.
9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method for lidar and inertial measurement unit detection according to any one of claims 1 to 6.
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Families Citing this family (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
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| CN115597624A (en) * | 2022-08-31 | 2023-01-13 | 广州文远知行科技有限公司（Cn） | Performance detection method, device and equipment of inertial navigation unit and storage medium |
| CN116385550B (en) * | 2022-12-16 | 2024-07-23 | 北京斯年智驾科技有限公司 | External parameter calibration method, device, computing equipment, medium and vehicle |
| CN116934832A (en) * | 2023-07-19 | 2023-10-24 | 齐鲁工业大学(山东省科学院) | Real-time calibration method of three-dimensional point cloud based on field reference object |
| CN117168472B (en) * | 2023-10-31 | 2024-02-13 | 北京理工大学前沿技术研究院 | Repositioning method, repositioning system, storage medium and repositioning equipment for unmanned vehicle |
| CN117706563B (en) * | 2024-02-05 | 2024-05-07 | 中南大学 | Method, system, equipment and storage medium for positioning drilling holes in vertical section of mine |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105717500A (en) * | 2016-02-24 | 2016-06-29 | 深圳乐行天下科技有限公司 | Laser radar and data correcting method thereof |
| US9587977B2 (en) * | 2012-08-31 | 2017-03-07 | Nikon Corporation | Boresight error monitor for laser radar integrated optical assembly |
| CN107340522A (en) * | 2017-07-10 | 2017-11-10 | 浙江国自机器人技术有限公司 | A kind of method, apparatus and system of laser radar positioning |
| CN110673115A (en) * | 2019-09-25 | 2020-01-10 | 杭州飞步科技有限公司 | Combined calibration method, device, equipment and medium for radar and integrated navigation system |
| CN110686704A (en) * | 2019-10-18 | 2020-01-14 | 深圳市镭神智能***有限公司 | Pose calibration method, system and medium for laser radar and combined inertial navigation |
| US20200132832A1 (en) * | 2018-10-25 | 2020-04-30 | TransRobotics, Inc. | Technologies for opportunistic synthetic aperture radar |
-
2020
- 2020-08-31 CN CN202010899803.0A patent/CN112051590B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US9587977B2 (en) * | 2012-08-31 | 2017-03-07 | Nikon Corporation | Boresight error monitor for laser radar integrated optical assembly |
| CN105717500A (en) * | 2016-02-24 | 2016-06-29 | 深圳乐行天下科技有限公司 | Laser radar and data correcting method thereof |
| CN107340522A (en) * | 2017-07-10 | 2017-11-10 | 浙江国自机器人技术有限公司 | A kind of method, apparatus and system of laser radar positioning |
| US20200132832A1 (en) * | 2018-10-25 | 2020-04-30 | TransRobotics, Inc. | Technologies for opportunistic synthetic aperture radar |
| CN110673115A (en) * | 2019-09-25 | 2020-01-10 | 杭州飞步科技有限公司 | Combined calibration method, device, equipment and medium for radar and integrated navigation system |
| CN110686704A (en) * | 2019-10-18 | 2020-01-14 | 深圳市镭神智能***有限公司 | Pose calibration method, system and medium for laser radar and combined inertial navigation |
Non-Patent Citations (2)
| Title |
|---|
| 低精度IMU在机载激光扫描***中误差模型与应用;田水根 等;《应用激光》;20190630;第39卷(第3期);第509-513页 * |
| 基于多传感器融合的机器人位姿估计研究;罗耀耀 等;《实验技术与管理》;20200531;第37卷(第5期);第58-62页 * |
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