CN115236714A - Multi-source data fusion positioning method, device and equipment and computer storage medium - Google Patents
Multi-source data fusion positioning method, device and equipment and computer storage medium Download PDFInfo
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- 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
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
- G01S19/45—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement
- G01S19/47—Determining position by combining measurements of signals from the satellite radio beacon positioning system with a supplementary measurement the supplementary measurement being an inertial measurement, e.g. tightly coupled inertial
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
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- G—PHYSICS
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- G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
- G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
- G01C21/10—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration
- G01C21/12—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning
- G01C21/16—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation
- G01C21/165—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments
- G01C21/1652—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 by using measurements of speed or acceleration executed aboard the object being navigated; Dead reckoning by integrating acceleration or speed, i.e. inertial navigation combined with non-inertial navigation instruments with ranging devices, e.g. LIDAR or RADAR
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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
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/86—Combinations of lidar systems with systems other than lidar, radar or sonar, e.g. with direction finders
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Abstract
The invention provides a multi-source data fusion positioning method, a device, equipment and a computer storage medium, wherein the method comprises the following steps: acquiring multi-source positioning data of a positioning target, wherein the multi-source positioning data comprises point cloud data acquired by using a laser radar, inertial positioning data acquired by using an inertial sensor and GPS data; calculating relative pose parameters of the positioning target based on the inertial positioning data, and correcting motion distortion of the point cloud data according to the relative pose parameters to obtain local positioning data; and fusing the local positioning data and the GPS data to determine a global positioning result of the positioning target. By fusing the collected multi-source positioning data, positioning results of different precisions of the positioning target can be fused, and the positioning precision of the positioning target is improved under the condition of not depending on satellite signals, so that the independent accurate positioning of the positioning target is realized.
Description
Technical Field
The invention relates to the technical field of navigation positioning, in particular to a multi-source data fusion positioning method, device and equipment and a computer storage medium.
Background
At present, outdoor positioning of terminal equipment such as unmanned aerial vehicles, unmanned vehicles and robots mainly depends on a GPS (global positioning system), and when the terminal equipment is accurately positioned, a difference method is often used for improving positioning accuracy, so that the positioning accuracy completely depends on the strength of satellite signals. However, in practical applications, for example, in an area where satellite signals are unstable, autonomous accurate positioning independent of the outside is required, which cannot be achieved by the existing methods.
Disclosure of Invention
The invention provides a multi-source data fusion positioning method, a multi-source data fusion positioning device, multi-source data fusion positioning equipment and a computer storage medium, which are used for solving the defect that the positioning precision depends on the satellite signal strength in the prior art and realizing independent accurate positioning independent of the outside.
The invention provides a multi-source data fusion positioning method, which comprises the following steps:
acquiring multi-source positioning data of a positioning target, wherein the multi-source positioning data comprises point cloud data acquired by using a laser radar, inertial positioning data acquired by using an inertial sensor and GPS data;
calculating relative pose parameters of the positioning target based on the inertial positioning data, and correcting motion distortion of the point cloud data according to the relative pose parameters to obtain local positioning data;
and fusing the local positioning data and the GPS data to determine a global positioning result of the positioning target.
According to the multi-source data fusion positioning method provided by the invention, the step of calculating the relative pose parameter of the positioning target based on the inertial positioning data comprises the following steps:
creating a local coordinate system corresponding to the inertial positioning data, and determining a state matrix of the positioning target in the local coordinate system based on the inertial positioning data;
filtering and integrating the inertial positioning data according to the state matrix to obtain motion parameters of the positioning target under the local coordinate system, wherein the motion parameters comprise a rotation angle, an acceleration and an angular velocity;
and calculating relative pose parameters of the positioning target according to the motion parameters, wherein the relative pose parameters comprise relative speed and relative displacement.
According to the multi-source data fusion positioning method provided by the invention, the step of correcting the motion distortion of the point cloud data according to the relative pose parameter to obtain local positioning data comprises the following steps:
converting the point cloud data into the local coordinate system, and determining the corresponding local point cloud data of the point cloud data in the local coordinate system;
in the local coordinate system, based on the relative pose parameters, performing interframe interpolation processing on a corresponding pose result of the positioning target in the local point cloud data, and determining pose information of the positioning target at each point in the local point cloud data at the corresponding moment;
and converting each frame of data point in the local point cloud data into a local coordinate system at the frame scanning end moment according to the pose information so as to correct the motion distortion of the point cloud data and obtain local positioning data.
According to the multi-source data fusion positioning method provided by the invention, the step of fusing the local positioning data and the GPS data to determine the global positioning result of the positioning target comprises the following steps:
performing point cloud matching on the local positioning data to determine a local positioning result of the positioning target;
converting the GPS data into the local coordinate system, and fusing the local positioning result and the positioning result of the GPS data to obtain a fused positioning result of the positioning target;
and converting the fusion positioning result into a global coordinate system corresponding to the GPS data to obtain a global positioning result of the positioning target.
According to the multi-source data fusion positioning method provided by the invention, the step of performing point cloud matching on the local positioning data and determining the local positioning result of the positioning target comprises the following steps:
determining a relative pose relationship of the inter-frame point cloud according to the local positioning data;
performing voxel segmentation on each data point in the local positioning data, and calculating a mean value and a covariance matrix of each voxel obtained by segmentation;
taking the relative pose relationship as an initial parameter, and iteratively optimizing the mean value and the covariance matrix to obtain an optimal parameter;
and superposing each frame of point cloud data in the local positioning data according to the optimal parameters so as to match each frame of point cloud data in the local positioning data, thereby obtaining a local positioning result of the positioning target in the local coordinate system.
According to the multi-source data fusion positioning method provided by the invention, the step of converting the GPS data into the local coordinate system comprises the following steps:
creating a geodetic coordinate system and converting each data point in the GPS data into the geodetic coordinate system;
determining initial data points from each corresponding data point of the GPS data in the geodetic coordinate system according to the time corresponding to the first frame of point cloud data in the point cloud data;
each data point in the GPS data is transformed from the geodetic coordinate system into the local coordinate system by making a difference with the initial data point.
According to the multi-source data fusion positioning method provided by the invention, before the step of converting the GPS data into the local coordinate system, the method further comprises:
and rejecting outlier data points in the GPS data.
The invention also provides a multi-source data fusion positioning device, which comprises:
the system comprises a multi-source data acquisition module, a positioning module and a control module, wherein the multi-source data acquisition module is used for acquiring multi-source positioning data of a positioning target, the multi-source data acquisition module comprises a laser radar and an inertial sensor, and the multi-source positioning data comprises point cloud data acquired by the laser radar, inertial positioning data acquired by the inertial sensor and GPS data;
the data correction module is used for calculating relative pose parameters of the positioning target based on the inertial positioning data and correcting motion distortion of the point cloud data according to the relative pose parameters to obtain local positioning data;
and the fusion positioning module is used for fusing the local positioning data and the GPS data to determine a global positioning result of the positioning target.
The invention also provides an electronic device, which comprises a memory, a processor and a computer program which is stored on the memory and can run on the processor, wherein the processor executes the program to realize the multi-source data fusion positioning method.
The present invention also provides a non-transitory computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements a multi-source data fusion localization method as described in any of the above.
The invention also provides a computer program product comprising a computer program, wherein the computer program is used for realizing the multi-source data fusion positioning method when being executed by a processor.
According to the multi-source data fusion positioning method, device, equipment and computer storage medium, the collected multi-source data are fused, the high-precision positioning data of the positioning target is subjected to motion distortion correction, the local positioning data of the positioning target is obtained, the positioning target is positioned by fusing the local positioning data and the GPS data, the positioning results of different precisions of the positioning target can be fused, the positioning precision of the positioning target is improved under the condition of not depending on satellite signals, and therefore the independent accurate positioning of the positioning target is achieved.
Drawings
In order to more clearly illustrate the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a schematic flow chart of a multi-source data fusion positioning method provided by the present invention;
FIG. 2 is a second schematic flow chart of the multi-source data fusion positioning method provided by the present invention;
FIG. 3 is a schematic structural diagram of a multi-source data fusion positioning apparatus provided by the present invention;
fig. 4 is a schematic structural diagram of an electronic device provided in the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings, and it is obvious that the described embodiments are some, but not all embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The multi-source data fusion positioning method of the invention is described below with reference to fig. 1-2.
Fig. 1 is one of the flow diagrams of the multi-source data fusion positioning method provided by the embodiment of the present invention, and it should be noted that, currently, when performing outdoor positioning on terminal devices such as an unmanned aerial vehicle, an unmanned vehicle, and a robot, the positioning accuracy almost completely depends on the strength of a satellite signal by mainly relying on a GPS. In practical applications, for example, when the terminal device operates in an area with weak satellite signals, it is often necessary to perform autonomous precise positioning that does not depend completely on the outside. Based on the above, the embodiment of the invention provides a multi-source data fusion positioning method, which realizes autonomous accurate positioning independent of GPS precision by utilizing fusion complementation of various sensors and GPS data. Specifically, referring to fig. 1, the multi-source data fusion positioning method provided by the embodiment of the present invention includes:
firstly, gather the multisource location data of location target, wherein, the location target can be unmanned aerial vehicle, unmanned car, can also be the robot, generally, positioner installs on the location target, and the location target is positioner's carrier, and the following called location target is the carrier. Fixed mounting has devices such as GPS, inertial sensor (IMU) on the carrier to 9 axles IMU are for example, and 9 axles IMU include gyroscope, accelerometer and magnetometer) and lidar, and the multisource location data of gathering includes GPS data, utilizes the point cloud data of lidar collection and the inertial location data (IMU data) that utilizes IMU collection, and the data that the source is different through different device gathers are different to the positioning accuracy of carrier.
200, calculating relative pose parameters of the positioning target based on the inertial positioning data, and correcting motion distortion of the point cloud data according to the relative pose parameters to obtain local positioning data;
the relative pose parameter of the carrier is calculated based on IMU data acquired by the IMU, and motion distortion of point cloud data acquired by the laser radar is corrected according to the calculated relative pose parameter of the carrier, so that autonomous accurate positioning independent of satellite signals of the carrier is realized. Therefore, one frame of point cloud data of the laser radar is not scanned and acquired at the same time, but the point cloud data acquired through one rotation and accumulation is used as one frame of data, and under the condition that the speed of the laser radar is high, the acquired point cloud data has motion distortion and needs to be corrected through motion compensation. And performing motion compensation on the point cloud data acquired by the laser radar based on the IMU data to obtain local positioning data, and obtaining a local positioning result of the carrier according to the local positioning data.
Further, step 200 may further include:
step 201, creating a local coordinate system corresponding to the inertial positioning data, and determining a state matrix of the positioning target in the local coordinate system based on the inertial positioning data;
202, filtering and integrating the inertial positioning data according to the state matrix to obtain motion parameters of the positioning target in the local coordinate system, wherein the motion parameters comprise a rotation angle, an acceleration and an angular velocity;
and 203, calculating relative pose parameters of the positioning target according to the motion parameters, wherein the relative pose parameters comprise relative speed and relative displacement.
The relative pose parameters of the carrier comprise relative speed and relative displacement, and when the relative pose parameters of the IMU data are calculated, firstly, the IMU is taken as a reference to establish a local coordinate system C corresponding to the IMU data I And defining a state matrix X of the carrier at a certain time t t ：
X t ＝[R t ,T t ,V t ,b t ] (1)
Wherein R is t Rotation matrix of 3 x 3, T t Translation matrix 1 x 3, V t Speed matrix of 1 x 3, b t Bayesian error matrix 1 x 3.
Filtering and integrating IMU data to obtain the carrier in IMU coordinate system C I The motion parameters comprise the rotation angle R, the acceleration a and the angular speed omega of the carrier. The filtering process for the IMU data may be kalman filtering, which is not limited herein, and taking kalman filtering as an example, after the kalman filtering process is performed on the IMU data, at the time t + Δ t by solving the IMU data integral, the carrier is in the IMU coordinate system C I Lower corresponding translation parameter T t+Δt And a speed parameter V t+Δt ：
V t+Δt ＝V t +R t (a t -b t -n t )Δt (2)
R t+Δt ＝R t exp((ω t -b t -n t )Δt) (4)
Wherein n is t The motion trail of the carrier can be predicted for white noise at the time t through the calculated motion parameters, and the motion parameters of the carrier at the time t can be obtained according to the acquired data, so that the motion parameters of the carrier at the future time t + delta t can be obtained, and the position of the carrier can be predicted. Since the integration process is time-consuming, and if the value of the IMU needs to be corrected in the subsequent processing process, the integration needs to be performed again, in this embodiment, the IMU data is pre-integrated, repeated calculation is avoided, and the carrier is used at any t i Time t and j all relative velocities av between moments ij And relative displacement Δ T ij Is represented as follows:
the acquired IMU data are integrated, so that the motion parameters of the carrier at a certain moment (reference moment) corresponding to the IMU data can be determined, and the relative pose parameters of the carrier can be obtained based on the defined relative speed and relative displacement of the carrier and the calculated motion parameters of the carrier. Based on the relative pose parameter, the relative pose of the carrier at a future time of the reference time can be predicted. The relative pose is the pose of the carrier at a future time relative to the pose at the reference time. After the pose of the carrier at a certain moment is determined, the pose of the carrier at a future moment can be predicted based on the relative pose parameters of the carrier.
Further, step 200 may further include:
step 210, converting the point cloud data into the local coordinate system, and determining the corresponding local point cloud data of the point cloud data in the local coordinate system;
220, performing inter-frame interpolation processing on a corresponding pose result of the positioning target in the local point cloud data based on the relative pose parameter in the local coordinate system, and determining pose information of the positioning target at each point in the local point cloud data at the corresponding moment;
and 230, converting each frame of data point in the local point cloud data into a local coordinate system at the frame scanning end moment according to the pose information to correct the motion distortion of the point cloud data to obtain local positioning data.
When the motion distortion of the point cloud data collected by the laser radar is corrected according to the calculated relative pose parameters, the collected point cloud data is converted into a local coordinate system established based on an IMU (inertial measurement unit), and the corresponding points of each data point in the collected point cloud data in the local coordinate system are determined. When multi-source data is collected, data from different sources are represented based on respective coordinate systems, that is, positioning of a carrier by data collected through different channels or manners can be represented based on different coordinate systems, for example, an IMU is represented by an established local coordinate system, and GPS data can be represented by a longitude and latitude coordinate system (or a global coordinate system), etc. The method comprises the steps of performing motion compensation on point cloud data acquired by a laser radar, and therefore when motion distortion of the point cloud data is corrected, firstly, converting the point cloud data into a local coordinate system for representation.
And after the point cloud data is converted into a local coordinate system, the pose, namely the motion state of the carrier at each moment corresponding to the IMU data is obtained by integrating the IMU data. Because the frequency when the IMU data is acquired is generally greater than the frequency when the laser radar acquires the point cloud data, the point cloud data is interpolated based on the acquired IMU data, and the state at the moment corresponding to the IMU data is used as the state at the moment corresponding to the point cloud data, so that the acquired point cloud data is subjected to motion compensation. Therefore, in each frame of laser radar data, interpolation is carried out on the time corresponding to the IMU data between the point cloud data frames, and the position and posture result of the carrier at the time corresponding to the IMU data is inserted to obtain the posture information of the point cloud data at the time corresponding to each data point. And converting the data points in each frame into a local coordinate system at the frame scanning end time of the data points according to the pose information of the carrier at the time corresponding to each data point in the point cloud data, and correcting the motion distortion of each data point in the frame of point cloud data through the pose information of the carrier at the frame scanning end time of each frame of point cloud data, so as to realize the correction of the motion distortion of the collected point cloud data and obtain corresponding local positioning data.
Before step 210, the method may further include:
and step 211, rejecting outlier data points in the GPS data.
Since the GPS data can have outliers, the outliers need to be removed from the collected GPS data so as to avoid influencing the positioning result. And when the outliers are eliminated, identifying the outliers generated due to the GPS drift according to the uniform motion model and eliminating the outliers.
And step 300, fusing the local positioning data and the GPS data to determine a global positioning result of the positioning target.
And fusing the local positioning data obtained by the motion distortion correction with the GPS data to obtain a global positioning result of the carrier, wherein the fusing of the local positioning data and the GPS data comprises converting the local positioning data and the GPS data into the same coordinate system. It can be understood that GPS and lidar's positioning accuracy is different, and the location of meter level precision can be realized to general GPS, and lidar can realize the location of centimetre level precision, fuses lidar and GPS's positioning result, can realize the location of centimetre level precision that does not rely on satellite signal to the improvement is to the positioning accuracy of carrier, realizes the autonomic accurate positioning to the carrier.
In this embodiment, through fusing the multisource location data of gathering, carry out the motion distortion correction to the high accuracy location data of positioning object, obtain the local positioning data to the positioning object, fix a position the positioning object through fusing local positioning data and GPS data, can fuse the positioning result of different precisions, under the condition that does not rely on satellite signal, improve the positioning accuracy to the positioning object to the realization is to the autonomic accurate positioning of positioning object.
In one embodiment, step 300 may further include:
step 301, performing point cloud matching on the local positioning data, and determining a local positioning result of the positioning target;
step 302, converting the GPS data into the local coordinate system, and fusing the local positioning result and the positioning result of the GPS data to obtain a fused positioning result of the positioning target;
step 303, converting the fusion positioning result to a global coordinate system corresponding to the GPS data to obtain a global positioning result for the positioning target, where the positioning accuracy of the local positioning result is higher than that of the GPS data.
After motion compensation is carried out on the collected point cloud data and motion distortion of the point cloud data is corrected, each frame of point cloud data is converted into a created local coordinate system, point cloud matching is carried out on the obtained local positioning data under the local coordinate system, and therefore a local positioning result of the carrier is determined. And converting the GPS data into a local coordinate system, fusing the positioning result of the GPS data with the local positioning result, and converting the fused positioning result into a global coordinate system corresponding to the GPS data, thereby obtaining a global positioning result of the carrier.
Further, step 301 may further include:
step 311, determining a relative pose relationship of the point cloud between frames according to the local positioning data;
step 321, performing voxel segmentation on each data point in the local positioning data, and calculating a mean value and a covariance matrix of each voxel obtained by segmentation;
step 331, taking the relative pose relationship as an initial parameter, and iteratively optimizing the mean value and the covariance matrix to obtain an optimal parameter;
step 341, superposing each frame of point cloud data in the local positioning data according to the optimal parameters to match each frame of point cloud data in the local positioning data, so as to obtain a local positioning result of the positioning target in the local coordinate system.
After the point cloud data collected by the laser radar is subjected to motion distortion correction, at the moment, each frame of the laser radar is converted to a local coordinate system, a relative translation matrix delta T and a relative rotation matrix delta R between two frames of point clouds can be obtained, however, certain errors exist in the integral result of IMU data, and the integral error is eliminated through point cloud matching, so that the local positioning result of the carrier can be obtained.
Specifically, a NDT (non-destructive Testing) method is adopted for point cloud matching, and the relative pose relation of the carrier among different frames of point cloud data in the point cloud data is determined according to local positioning data. The motion distortion of the point cloud data is corrected, after the point cloud data is converted into a local coordinate system, data points in each frame of point cloud data are converted into the local coordinate system corresponding to the scanning end time of each frame of point cloud, and therefore the relative pose relation of the carrier in the inter-frame point cloud can be calculated, and the relative pose relation can be represented by a translation matrix and a rotation matrix.
Determining the relative pose relationship of the carrier under the currently acquired point cloud data according to the local positioning data, and taking the relative pose relationship as an initial parameter [ R, T]Performing voxel segmentation on each point of the point cloud data in the local coordinate system space, and calculating the mean value of each voxel obtained by segmentation
And covariance matrix Σ:
wherein
Is a mean value, x i For each point in the voxel, n is the number of points, and Σ is the covariance matrix. Averaging two frames of point clouds according to initial parameters
Iterative optimization of the sum covariance matrix sigma to find out the optimal parameter R o ,T o ]And converting the next frame of point cloud into a corresponding coordinate system according to the optimal parameters, and superposing the point clouds to complete the matching of the frame of point cloud. By matching the point cloud of each frame, the translation and rotation of the carrier relative to the local coordinate system at the moment corresponding to the point cloud data of each frame can be obtained and recorded as [ R ] i ,T i ]Namely, the local positioning result of the carrier under the local coordinate system is obtained.
Further, step 302 may further include:
step 312, creating a geodetic coordinate system, and converting each data point in the GPS data into the geodetic coordinate system;
step 322, determining an initial data point from each data point corresponding to the GPS data in the geodetic coordinate system according to a time corresponding to a first frame of point cloud data in the point cloud data;
step 332, converting each data point in the GPS data from the geodetic coordinate system to the local coordinate system by differencing with the initial data point.
In converting GPS data into a local coordinate system, a geodetic coordinate system is first created and aligned with a GPS coordinate system, which generally uses the center of gravity coordinate system (ENU coordinate system) and is required when converting points in the center of gravity coordinate system into a local coordinate systemThe coordinate system alignment is performed by the geodetic coordinate system. After the geodetic coordinate system is created, each data point in the GPS data is converted into the geodetic coordinate system. Specifically, based on a station center coordinate system used by the GPS, an included angle between an ellipsoid normal line passing through a ground point and an ellipsoid equatorial plane is taken as a geodetic latitude B, an included angle between an ellipsoid meridian plane passing through the ground point and a starting meridian plane is taken as a geodetic longitude L, a distance from the ground point to the ellipsoid along the ellipsoid normal line is taken as a geodetic height H, a GPS data point with the time closest to the first frame of IMU data is converted into a geodetic coordinate system as an initial data point and is recorded as G 0 ＝[B o ,L o ,H o ]Wherein, taking the reference center 0 as the origin of coordinates, the Z axis coincides with the minor axis (rotating axis) of the reference ellipsoid, the X axis coincides with the intersection line of the initial meridian plane and the equator, and the Y axis is vertical to the X axis on the equator plane, so as to form a right-hand rectangular coordinate system 0-XYZ:
X o ＝(N+H o )cosB o cosL o (9)
Y o ＝(N+H o )cosB o sinL o (10)
Z o ＝(N(1-e 2 )+H o )sinB o (11)
wherein e is the first eccentricity of an ellipsoid in the standing center coordinate system, and is obtained according to the major and minor semiaxes:
e＝sqrt(a 2 -b 2 )/a (12)
n is the curvature radius of the unitary-and-mortise circle of the ellipsoid in the station center coordinate system, and can be obtained according to the following formula:
N＝a/sqrt(1-e 2 sin 2 B o ) (13)
after the initial data points are converted into a geodetic coordinate system, each GPS data point subjected to outlier filtering is subjected to the same conversion, so that the GPS data point is converted into the geodetic coordinate system, the GPS data point is converted into a local coordinate system by making a difference with the initial data point, the GPS data is corrected by utilizing IMU data subjected to Kalman filtering and integration processing, and the positioning result and the local positioning result of the GPS data are fused to realize the global positioning of the carrier.
Referring to fig. 2, fig. 2 is another schematic flow chart of a multi-source data fusion positioning method according to an embodiment of the present invention, in fig. 2, a carrier can be subjected to position prediction by using processed IMU data and corrected GPS data, so as to position the carrier, when the carrier is only rotated, pose prediction is performed based on IMU data integration, and when the carrier is translated, pose prediction is performed by using GPS data and IMU data fusion. And according to the pose prediction of the carrier, updating the pose of the carrier based on a prediction result after the carrier is translated and/or rotated, and applying the updated pose to subsequent prediction through pre-integration processing.
Further, after the local positioning of the point cloud data based on the laser radar and the coordinate conversion based on the GPS are completed, in a local coordinate system, a centimeter-level accurate positioning result and a meter-level GPS positioning result based on the laser radar are fused, and the generated fusion positioning result can be obtained according to an initial data point G of the GPS 0 ＝[X o ,Y o ,Z o ]Converting the initial data point of the GPS into a geodetic coordinate system by taking the longitude and latitude coordinate system as an example and recording the initial data point as G t ＝[X t ,Y t ,Z t ]And finally, converting the carrier into a longitude and latitude coordinate system according to the following formula to realize centimeter-level high-precision global positioning of the carrier independent of GPS precision:
L t ＝tan -1 (Y t /X t ) (14)
in the embodiment, the optimal parameters are determined through voxel segmentation to perform point cloud matching, so that the integral error of IMU data is eliminated, meanwhile, the GPS data is subjected to coordinate alignment, the fusion of a local positioning result and a GPS positioning result is realized, and the positioning precision of a positioning target is improved.
The multi-source data fusion positioning device provided by the invention is described below, and the multi-source data fusion positioning device described below and the multi-source data fusion positioning method described above can be referred to correspondingly.
Referring to fig. 3, the multi-source data fusion positioning apparatus provided by the embodiment of the present invention includes:
the multi-source data acquisition module 10 is used for acquiring multi-source positioning data of a positioning target, wherein the multi-source data acquisition module comprises a laser radar and an inertial sensor, and the multi-source positioning data comprises point cloud data acquired by the laser radar, inertial positioning data acquired by the inertial sensor and GPS data;
the data correction module 20 is configured to calculate a relative pose parameter of the positioning target based on the inertial positioning data, and correct motion distortion of the point cloud data according to the relative pose parameter to obtain local positioning data;
and the fusion positioning module 30 is configured to fuse the local positioning data and the GPS data to determine a global positioning result of the positioning target.
In one embodiment, the data correction module 20 is further configured to:
creating a local coordinate system corresponding to the inertial positioning data, and determining a state matrix of the positioning target in the local coordinate system based on the inertial positioning data;
filtering and integrating the inertial positioning data according to the state matrix to obtain motion parameters of the positioning target under the local coordinate system, wherein the motion parameters comprise a rotation angle, an acceleration and an angular velocity;
and calculating relative pose parameters of the positioning target according to the motion parameters, wherein the relative pose parameters comprise relative speed and relative displacement.
In one embodiment, the data correction module 20 is further configured to:
converting the point cloud data into the local coordinate system, and determining the corresponding local point cloud data of the point cloud data in the local coordinate system;
in the local coordinate system, based on the relative pose parameters, performing interframe interpolation processing on a corresponding pose result of the positioning target in the local point cloud data, and determining pose information of the positioning target at each point in the local point cloud data at the corresponding moment;
and converting each frame of data point in the local point cloud data into a local coordinate system at the frame scanning end moment according to the pose information so as to correct the motion distortion of the point cloud data and obtain local positioning data.
In one embodiment, the fusion localization module 30 is further configured to:
performing point cloud matching on the local positioning data to determine a local positioning result of the positioning target;
converting the GPS data into the local coordinate system, and fusing the local positioning result and the positioning result of the GPS data to obtain a fused positioning result of the positioning target;
and converting the fusion positioning result into a global coordinate system corresponding to the GPS data to obtain a global positioning result of the positioning target.
In one embodiment, the fusion localization module 30 is further configured to:
determining a relative pose relationship of the interframe point cloud according to the local positioning data;
performing voxel segmentation on each data point in the local positioning data, and calculating a mean value and a covariance matrix of each voxel obtained by segmentation;
taking the relative pose relationship as an initial parameter, and iteratively optimizing the mean value and the covariance matrix to obtain an optimal parameter;
and superposing each frame of point cloud data in the local positioning data according to the optimal parameters so as to match each frame of point cloud data in the local positioning data, thereby obtaining a local positioning result of the positioning target in the local coordinate system.
In one embodiment, the fusion localization module 30 is further configured to:
creating a geodetic coordinate system and converting each data point in the GPS data into the geodetic coordinate system;
determining initial data points from each corresponding data point of the GPS data in the geodetic coordinate system according to the time corresponding to the first frame of point cloud data in the point cloud data;
and converting each data point in the GPS data from the geodetic coordinate system to the local coordinate system by making a difference with the initial data point.
In one embodiment, the multi-source data fusion positioning apparatus further comprises an outlier data filtering module configured to:
and rejecting outlier data points in the GPS data.
Fig. 4 illustrates a physical structure diagram of an electronic device, which may include, as shown in fig. 4: a processor (processor) 410, a communication Interface (Communications Interface) 420, a memory (memory) 430 and a communication bus 440, wherein the processor 410, the communication Interface 420 and the memory 430 are in communication with each other via the communication bus 440. Processor 410 may invoke logic instructions in memory 430 to perform a multi-source data fusion localization method, the method comprising:
acquiring multi-source positioning data of a positioning target, wherein the multi-source positioning data comprises point cloud data acquired by using a laser radar, inertial positioning data acquired by using an inertial sensor and GPS data;
calculating relative pose parameters of the positioning target based on the inertial positioning data, and correcting motion distortion of the point cloud data according to the relative pose parameters to obtain local positioning data;
and fusing the local positioning data and the GPS data to determine a global positioning result of the positioning target.
In addition, the logic instructions in the memory 430 may be implemented in the form of software functional units and stored in a computer readable storage medium when the software functional units are sold or used as independent products. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and various media capable of storing program codes.
In another aspect, the present invention also provides a computer program product, where the computer program product includes a computer program, the computer program can be stored on a non-transitory computer readable storage medium, and when the computer program is executed by a processor, a computer can execute the multi-source data fusion positioning method provided by the above methods, where the method includes:
acquiring multi-source positioning data of a positioning target, wherein the multi-source positioning data comprises point cloud data acquired by using a laser radar, inertial positioning data acquired by using an inertial sensor and GPS data;
calculating relative pose parameters of the positioning target based on the inertial positioning data, and correcting motion distortion of the point cloud data according to the relative pose parameters to obtain local positioning data;
and fusing the local positioning data and the GPS data to determine a global positioning result of the positioning target.
In yet another aspect, the present invention also provides a non-transitory computer-readable storage medium, on which a computer program is stored, the computer program being implemented by a processor to perform the multi-source data fusion positioning method provided by the above methods, the method including:
acquiring multi-source positioning data of a positioning target, wherein the multi-source positioning data comprises point cloud data acquired by using a laser radar, inertial positioning data acquired by using an inertial sensor and GPS data;
calculating relative pose parameters of the positioning target based on the inertial positioning data, and correcting motion distortion of the point cloud data according to the relative pose parameters to obtain local positioning data;
and fusing the local positioning data and the GPS data to determine a global positioning result of the positioning target.
The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one position, or may be distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
Through the above description of the embodiments, those skilled in the art will clearly understand that each embodiment may be implemented by software plus a necessary general hardware platform, and may also be implemented by hardware. With this understanding, the above technical solutions may be embodied in the form of a software product, which may be stored in a computer-readable storage medium, such as ROM/RAM, a magnetic disk, an optical disk, etc., and includes several instructions for enabling a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (10)
1. A multi-source data fusion positioning method is characterized by comprising the following steps:
acquiring multi-source positioning data of a positioning target, wherein the multi-source positioning data comprises point cloud data acquired by using a laser radar, inertial positioning data acquired by using an inertial sensor and GPS data;
calculating relative pose parameters of the positioning target based on the inertial positioning data, and correcting motion distortion of the point cloud data according to the relative pose parameters to obtain local positioning data;
and fusing the local positioning data and the GPS data to determine a global positioning result of the positioning target.
2. The multi-source data fusion positioning method according to claim 1, wherein the step of calculating the relative pose parameters of the positioning targets based on the inertial positioning data comprises:
creating a local coordinate system corresponding to the inertial positioning data, and determining a state matrix of the positioning target in the local coordinate system based on the inertial positioning data;
filtering and integrating the inertial positioning data according to the state matrix to obtain motion parameters of the positioning target under the local coordinate system, wherein the motion parameters comprise a rotation angle, an acceleration and an angular velocity;
and calculating relative pose parameters of the positioning target according to the motion parameters, wherein the relative pose parameters comprise relative speed and relative displacement.
3. The multi-source data fusion positioning method according to claim 2, wherein the step of correcting the motion distortion of the point cloud data according to the relative pose parameter to obtain local positioning data comprises:
converting the point cloud data into the local coordinate system, and determining the corresponding local point cloud data of the point cloud data in the local coordinate system;
in the local coordinate system, based on the relative pose parameters, performing interframe interpolation processing on a corresponding pose result of the positioning target in the local point cloud data, and determining pose information of the positioning target at each point in the local point cloud data at the corresponding moment;
and converting each frame of data point in the local point cloud data into a local coordinate system at the frame scanning end moment according to the pose information so as to correct the motion distortion of the point cloud data and obtain local positioning data.
4. The multi-source data fusion positioning method according to claim 2, wherein the step of fusing the local positioning data and the GPS data to determine a global positioning result of the positioning target comprises:
performing point cloud matching on the local positioning data to determine a local positioning result of the positioning target;
converting the GPS data into the local coordinate system, and fusing the local positioning result and the positioning result of the GPS data to obtain a fused positioning result of the positioning target;
and converting the fusion positioning result into a global coordinate system corresponding to the GPS data to obtain a global positioning result of the positioning target.
5. The multi-source data fusion positioning method according to claim 4, wherein the step of performing point cloud matching on the local positioning data to determine the local positioning result of the positioning target includes:
determining a relative pose relationship of the interframe point cloud according to the local positioning data;
performing voxel segmentation on each data point in the local positioning data, and calculating a mean value and a covariance matrix of each voxel obtained by segmentation;
taking the relative pose relationship as an initial parameter, and iteratively optimizing the mean value and the covariance matrix to obtain an optimal parameter;
and superposing each frame of point cloud data in the local positioning data according to the optimal parameters so as to match each frame of point cloud data in the local positioning data, thereby obtaining a local positioning result of the positioning target in the local coordinate system.
6. The multi-source data fusion positioning method according to claim 4, wherein the step of converting the GPS data into the local coordinate system comprises:
creating a geodetic coordinate system and converting each data point in the GPS data into the geodetic coordinate system;
determining initial data points from each corresponding data point of the GPS data in the geodetic coordinate system according to the time corresponding to the first frame of point cloud data in the point cloud data;
each data point in the GPS data is transformed from the geodetic coordinate system into the local coordinate system by making a difference with the initial data point.
7. The multi-source data fusion positioning method according to claim 3, wherein the step of converting the GPS data into the local coordinate system is preceded by:
and rejecting outlier data points in the GPS data.
8. A multi-source data fusion positioning device, comprising:
the multi-source data acquisition module is used for acquiring multi-source positioning data of a positioning target, wherein the multi-source data acquisition module comprises a laser radar and an inertial sensor, and the multi-source positioning data comprises point cloud data acquired by the laser radar, inertial positioning data acquired by the inertial sensor and GPS data;
the data correction module is used for calculating relative pose parameters of the positioning target based on the inertial positioning data and correcting motion distortion of the point cloud data according to the relative pose parameters to obtain local positioning data;
and the fusion positioning module is used for fusing the local positioning data and the GPS data to determine a global positioning result of the positioning target.
9. 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 multi-source data fusion localization method of any of claims 1 to 7 when executing the program.
10. A non-transitory computer readable storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the multi-source data fusion localization method of any of claims 1 to 7.
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| CN118091693A (en) * | 2024-04-25 | 2024-05-28 | 武汉汉宁轨道交通技术有限公司 | Adaptive feature matching line structured light and Lidar track multi-scale point cloud fusion method |
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| CN109116397B (en) * | 2018-07-25 | 2022-12-30 | 吉林大学 | Vehicle-mounted multi-camera visual positioning method, device, equipment and storage medium |
| WO2021007716A1 (en) * | 2019-07-12 | 2021-01-21 | Beijing Voyager Technology Co., Ltd. | Systems and methods for positioning |
| 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 |
| CN111077907A (en) * | 2019-12-30 | 2020-04-28 | 哈尔滨理工大学 | Autonomous positioning method of outdoor unmanned aerial vehicle |
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| CN113466890B (en) * | 2021-05-28 | 2024-04-09 | 中国科学院计算技术研究所 | Light laser radar inertial combination positioning method and system based on key feature extraction |
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