CN116068563A - Positioning method and positioning equipment for mobile device and mobile device - Google Patents
Positioning method and positioning equipment for mobile device and mobile device Download PDFInfo
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Abstract
The invention discloses a positioning method and positioning equipment for a mobile device and the mobile device. The cloud map of the target point is obtained; acquiring a point cloud to be registered from a laser radar arranged on the mobile device; gridding the target point cloud map to obtain a plurality of grid sub-maps; and carrying out parallelization matching processing on the point cloud to be registered and the grid maps so as to determine the position of the mobile device in the target point cloud map. By adopting the point cloud map and carrying out parallelization matching operation on the point cloud to be registered and the grid sub-maps, the operation speed is effectively improved, the matching time and the resource occupancy rate are reduced, and the real-time performance of mobile device positioning is ensured.
Description
Technical Field
The invention relates to the technical field of automatic driving, in particular to a positioning method and positioning equipment for a mobile device and the mobile device.
Background
In the process of automatic driving, inertial navigation and real-time dynamic measurement (Real Time Kinematic, RTK) systems are generally used to achieve accurate positioning in most scenes, but in some scenes in cities, such as in the case of high-rise forests or tree blinding, GPS signals are poor and accurate positioning is difficult to perform.
In order to solve the problem, a common scheme in the industry is to build a navigation map first, then load the navigation map into a memory, and match point cloud data scanned by a laser radar in a mobile device with the navigation map when the vehicle is automatically driven, so as to obtain the position of automatic driving equipment in the navigation map.
Because the navigation map is particularly large in volume in the automatic driving scene, the matching of the point cloud data and the navigation map takes a long time, and the real-time performance of the positioning of the mobile device is poor.
Disclosure of Invention
The invention mainly aims to provide a positioning method, positioning equipment and a mobile device for the mobile device, and aims to solve the technical problem that the real-time performance of positioning the mobile device in the prior art is poor.
In order to achieve the above object, a first aspect of the present invention provides a positioning method for a mobile device, comprising the steps of:
acquiring a target point cloud map;
acquiring a point cloud to be registered from a laser radar arranged on a mobile device;
gridding the target point cloud map to obtain a plurality of grid sub-maps;
and carrying out parallelization matching processing on the point cloud to be registered and the grid sub-maps so as to determine the position of the mobile device in the target point cloud map.
In the embodiment of the invention, parallelization matching processing is performed on the point cloud to be registered and a plurality of grid sub-maps, and the parallelization matching processing comprises the following steps:
acquiring an initial value of the current position of the mobile device;
determining a first grid sub-map within a preset range of a distance initial value from the plurality of grid sub-maps;
voxel processing is carried out on the first grid sub-map so as to obtain a plurality of voxels;
deleting voxels with the number of points smaller than a preset value from the first grid sub-map to obtain a screened grid sub-map;
and carrying out parallelization matching processing on the point cloud to be registered and the screened grid sub-map.
In the embodiment of the invention, parallelization matching processing is carried out on the point cloud to be registered and the screened grid sub-map, and the parallelization matching processing comprises the following steps:
distributing a plurality of threads for the screened grid sub-map;
and matching the point cloud to be aligned with the screened grid sub-map through a plurality of threads.
In the embodiment of the invention, matching processing is carried out on the point cloud to be aligned and the screened grid sub-map, and the matching processing comprises the following steps:
obtaining a current transformation parameter according to the initial value;
coordinate transformation is carried out on the point cloud to be aligned according to the current transformation parameters so as to obtain transformed points;
determining the matching degree of the transformed points and the screened grid sub-map;
and under the condition that the matching degree reaches a preset condition, determining the position according to the transformed points.
In the embodiment of the invention, the transformed points are obtained according to the following formula:
P i '＝R×P i +D；
wherein P is i ' is the transformed point, P i For the ith point in the point cloud to be registered, R is a rotation parameter vector in the current transformation parameter, and D is a translation parameter vector in the current transformation parameter.
In the embodiment of the invention, determining the matching degree of the transformed points and the screened grid sub-map comprises the following steps:
calculating the mean and variance of the normal distribution of the voxels for each voxel of the screened grid sub-map;
and obtaining the matching degree according to the transformed points, the mean value and the variance.
In the embodiment of the invention, the matching degree is obtained by the following formula:
wherein Fitness is the degree of matching, mu i For the mean value, P, of voxels in which the ith point in the point cloud to be registered is located i ' is the transformed point, Σ is the variance of the voxel where the ith point is located, and n is the number of points in the point cloud to be registered.
In the embodiment of the invention, the positioning method further comprises the following steps:
acquiring a global point cloud map;
gridding the global point cloud map to obtain a plurality of grid maps;
acquiring the current position of the mobile device;
and determining a grid map containing the current position in the plurality of grid maps as a target point cloud map.
In the embodiment of the invention, the positioning method further comprises the following steps:
acquiring three-dimensional coordinates and resolution of each grid map in a plurality of grid maps;
and naming each grid map according to the three-dimensional coordinates and the resolution.
A second aspect of the present invention provides a positioning apparatus for a mobile device, comprising:
a memory configured to store a positioning program for the mobile device;
and a processor configured to invoke the program from the memory to enable the processor to perform the positioning method for the mobile device described above when the program is run.
A third aspect of the present invention provides a mobile device comprising a positioning apparatus as described above for a mobile device.
Through the technical scheme, the cloud map of the target point is obtained; acquiring a point cloud to be registered from a laser radar arranged on a mobile device; gridding the target point cloud map to obtain a plurality of grid sub-maps; and carrying out parallelization matching processing on the point cloud to be registered and the grid sub-maps so as to determine the position of the mobile device in the target point cloud map. By adopting the point cloud map and carrying out parallelization matching operation on the point cloud to be registered and the grid sub-maps, the operation speed is effectively improved, the matching time and the resource occupancy rate are reduced, and the real-time performance of mobile device positioning is ensured.
Additional features and advantages of embodiments of the invention will be set forth in the detailed description which follows.
Drawings
The accompanying drawings are included to provide a further understanding of embodiments of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain, without limitation, the embodiments of the invention. In the drawings:
FIG. 1 is a schematic diagram of a positioning apparatus for a mobile device in a hardware operating environment according to an embodiment of the present invention;
FIG. 2 is a flowchart of a positioning method for a mobile device according to an embodiment of the invention;
fig. 3 is a detailed flowchart of an embodiment of step S40 in fig. 2.
Detailed Description
The following describes the detailed implementation of the embodiments of the present invention with reference to the drawings. It should be understood that the detailed description and specific examples, while indicating and illustrating the invention, are not intended to limit the invention.
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
Referring to fig. 1, fig. 1 is a schematic structural diagram of a positioning device for a mobile apparatus in a hardware running environment according to an embodiment of the present invention.
As shown in fig. 1, the positioning apparatus for a mobile device may include: a processor 1001, such as a CPU, a communication bus 1002, a user interface 1003, a network interface 1004, and a memory 1005. Wherein the communication bus 1002 is used to enable connected communication between these components. The user interface 1003 may include a display, an input unit such as a keyboard, and the optional user interface 1003 may also include a standard wired interface, a wireless interface. The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface). The memory 1005 may be a high-speed RAM memory or a stable memory such as a disk memory. The memory 1005 may also optionally be a storage device separate from the processor 1001 described above.
It will be appreciated by those skilled in the art that the structure shown in fig. 1 does not constitute a limitation of the positioning apparatus for a mobile device, and may include more or fewer components than shown, or may combine certain components, or a different arrangement of components.
As shown in fig. 1, an operating system, a network communication module, a user interface module, and a positioning program for a mobile device may be included in a memory 1005, which is a type of computer storage medium.
In the positioning apparatus for a mobile device shown in fig. 1, the network interface 1004 is mainly used for data communication with an external network; the user interface 1003 is mainly used for receiving an input instruction of a user; the positioning apparatus for a mobile device calls a positioning program for a mobile device stored in the memory 1005 through the processor 1001, and performs the following operations:
acquiring a target point cloud map;
acquiring a point cloud to be registered from a laser radar arranged on a mobile device;
gridding the target point cloud map to obtain a plurality of grid sub-maps;
and carrying out parallelization matching processing on the point cloud to be registered and the grid sub-maps so as to determine the position of the mobile device in the target point cloud map.
Further, the processor 1001 may call a positioning program for a mobile device stored in the memory 1005, and further perform the following operations:
acquiring an initial value of the current position of the mobile device;
determining a first grid sub-map within a preset range of a distance initial value from the plurality of grid sub-maps;
voxel processing is carried out on the first grid sub-map so as to obtain a plurality of voxels;
deleting voxels with the number of points smaller than a preset value from the first grid sub-map to obtain a screened grid sub-map;
and carrying out parallelization matching processing on the point cloud to be registered and the screened grid sub-map.
Further, the processor 1001 may call a positioning program for a mobile device stored in the memory 1005, and further perform the following operations:
distributing a plurality of threads for the screened grid sub-map;
and matching the point cloud to be aligned with the screened grid sub-map through a plurality of threads.
Further, the processor 1001 may call a positioning program for a mobile device stored in the memory 1005, and further perform the following operations:
obtaining a current transformation parameter according to the initial value;
coordinate transformation is carried out on the point cloud to be aligned according to the current transformation parameters so as to obtain transformed points;
determining the matching degree of the transformed points and the screened grid sub-map;
and under the condition that the matching degree reaches a preset condition, determining the position according to the transformed points.
Further, the processor 1001 may call a positioning program for a mobile device stored in the memory 1005, and further perform the following operations:
the transformed points are obtained according to the following formula:
P i '＝R×P i +D；
wherein P is i ' is the transformed point, P i For the ith point in the point cloud to be registered, R is a rotation parameter vector in the current transformation parameter, and D is a translation parameter vector in the current transformation parameter.
Further, the processor 1001 may call a positioning program for a mobile device stored in the memory 1005, and further perform the following operations:
calculating the mean and variance of the normal distribution of the voxels for each voxel of the screened grid sub-map;
and obtaining the matching degree according to the transformed points, the mean value and the variance.
In one embodiment, the degree of matching is obtained by the following formula:
wherein,,fitness is the degree of matching, mu i For the mean value, P, of voxels in which the ith point in the point cloud to be registered is located i ' is the transformed point, Σ is the variance of the voxel where the ith point is located, and n is the number of points in the point cloud to be registered.
Further, the processor 1001 may call a positioning program for a mobile device stored in the memory 1005, and further perform the following operations:
acquiring a global point cloud map;
gridding the global point cloud map to obtain a plurality of grid maps;
acquiring the current position of the mobile device;
and determining a grid map containing the current position in the plurality of grid maps as a target point cloud map.
Further, the processor 1001 may call a positioning program for a mobile device stored in the memory 1005, and further perform the following operations:
acquiring three-dimensional coordinates and resolution of each grid map in a plurality of grid maps;
and naming each grid map according to the three-dimensional coordinates and the resolution.
The embodiment obtains the cloud map of the target point; acquiring a point cloud to be registered from a laser radar arranged on a mobile device; gridding the target point cloud map to obtain a plurality of grid sub-maps; and carrying out parallelization matching processing on the point cloud to be registered and the grid sub-maps so as to determine the position of the mobile device in the target point cloud map. By adopting the point cloud map and carrying out parallelization matching operation on the point cloud to be registered and the grid sub-maps, the operation speed is effectively improved, the matching time and the resource occupancy rate are reduced, and the real-time performance of mobile device positioning is ensured.
Based on the above hardware structure, the embodiment of the positioning method for the mobile device is provided.
Referring to fig. 2, fig. 2 is a flowchart illustrating an embodiment of a positioning method for a mobile device according to the present invention.
In this embodiment, the positioning method for a mobile device includes the steps of:
s10: and acquiring a target point cloud map.
It should be understood that the point cloud map is different from the navigation map, and the point cloud map is a map formed by accumulating point clouds. After the global point cloud map is acquired, the global point cloud map may be subjected to meshing processing to obtain a plurality of grid maps, a current position of the mobile device is acquired, and a grid map including the current position in the plurality of grid maps is determined as a target point cloud map.
In a specific implementation, the global point cloud map can be segmented in a grid mode according to a certain resolution, and the map in each grid after segmentation is a small map and is stored as an independent map file.
In order to cooperate with dynamic loading, the three-dimensional coordinates and resolution of each grid map in the plurality of grid maps can be obtained; and naming each grid map according to the three-dimensional coordinates and the resolution.
Because each grid map is named by three-dimensional coordinates, when the target point cloud map needs to be loaded, the grid map which is close to the mobile device does not need to be searched from the global point cloud map, and the grid map where the mobile device is located can be quickly positioned only according to the three-dimensional coordinates of the mobile device.
It should be appreciated that positioning with memory limitations may be achieved by dynamically loading the target point cloud map at the time of positioning, rather than loading the global point cloud map or the navigation map. In order to achieve both accuracy and real-time positioning, the distance between the mobile device and the grid boundary can be obtained according to the current position of the mobile device and the resolution of the grid division. And under the condition that the distance reaches a preset value, loading of the next group of grid maps is required to be completed. Each time a grid map is loaded, the grid map where the current position is located and the grid map adjacent to the grid map need to be loaded as a target point cloud map. After the mobile device runs for a certain distance, whether a new target point cloud map needs to be loaded can be dynamically detected, if so, the new target point cloud map needs to be loaded timely, and therefore matching of the point cloud and the target point cloud map can be achieved even if the mobile device is on the boundary of the grid map.
S20: a point cloud to be registered is obtained from a lidar disposed on a mobile device.
It should be understood that, the mobile device refers to a device that can move such as a vehicle, an airplane, etc., and in an automatic driving process, the mobile device generally needs to use a laser radar to acquire point cloud data to achieve matching between the point cloud and a navigation map, so as to obtain a position of the mobile device in the navigation map. In this embodiment, the point cloud obtained by scanning the laser radar is used as the point cloud to be registered.
S30: and carrying out gridding processing on the target point cloud map to obtain a plurality of grid sub-maps.
In a specific implementation, the target point cloud map can be divided into a plurality of grids with the same size according to a certain resolution, and the point cloud to be registered is matched with the obtained grid sub-maps.
S40: and carrying out parallelization matching processing on the point cloud to be registered and the grid sub-maps so as to determine the position of the mobile device in the target point cloud map.
In the process of matching the point cloud to be registered with the grid sub-maps, the parallelization chip can be divided into a plurality of blocks, each block is provided with a plurality of threads, and the grid sub-maps are put into the parallelization calculation chip to finish the matched calculation, so that the calculation speed is effectively improved, and the real-time performance of positioning is ensured.
In one example, when the matching degree between the points in the point cloud to be registered and the grid sub-map reaches the maximum value after the point is transformed, matching is completed, corresponding optimized matching parameters are obtained, three-dimensional coordinate transformation is carried out on the point cloud to be registered according to the optimized matching parameters, transformed points are obtained, and the position of the mobile device in the target point cloud map can be determined according to the transformed points.
The embodiment obtains the cloud map of the target point; acquiring a point cloud to be registered from a laser radar arranged on a mobile device; gridding the target point cloud map to obtain a plurality of grid sub-maps; and carrying out parallelization matching processing on the point cloud to be registered and the grid sub-maps so as to determine the position of the mobile device in the target point cloud map. By adopting the point cloud map and carrying out parallelization matching operation on the point cloud to be registered and the grid sub-maps, the operation speed is effectively improved, the matching time and the resource occupancy rate are reduced, and the real-time performance of mobile device positioning is ensured.
As shown in fig. 3, fig. 3 is a schematic diagram of a refinement flow chart of step S40 in fig. 2, and in this embodiment, step S40 includes the following steps:
s41: an initial value of a current location of the mobile device is obtained.
It should be noted that, since the acquisition of the position of the mobile device is a continuous process, that is, the position of the mobile device is continuously acquired during a period of time during the operation of the mobile device, the current position is understood as the position of the mobile device at the current time, and the initial value is understood as pose information of the position of the mobile device at the current time, which can be obtained by the global positioning system (Global Positioning System, GPS).
S42: and determining a first grid sub-map within a preset range of the initial value of the distance from the plurality of grid sub-maps.
S43: and voxelizing the first grid sub-map to obtain a plurality of voxels.
S44: and deleting voxels with the number of points smaller than a preset value from the first grid sub-map to obtain the screened grid sub-map.
The preset value may be set according to actual needs, for example, 3 or 5, which is not limited in this embodiment.
It should be appreciated that, to ensure the validity of the first grid sub-map participating in the matching, the filtering and cleaning may be performed according to the number of points in the voxels of the first grid sub-map, with relatively small numbers of points removed. Through screening voxels in the first grid sub-map, an effective grid sub-map can be obtained, and positioning accuracy is improved.
S45: and carrying out parallelization matching processing on the point cloud to be registered and the screened grid sub-map.
In a specific implementation, a plurality of threads can be allocated to the screened grid sub-map; and matching the point cloud to be aligned with the screened grid sub-map through a plurality of threads.
Further, the matching calculation process is as follows: obtaining a current transformation parameter according to the initial value; coordinate transformation is carried out on the point cloud to be aligned according to the current transformation parameters so as to obtain transformed points; determining the matching degree of the transformed points and the screened grid sub-map; and under the condition that the matching degree reaches a preset condition, determining the position according to the transformed points.
It should be appreciated that after the initial values are obtained by the GPS, the initial values may be converted to a transformation matrix to obtain the pose of the GPS, and the current transformation parameters may be obtained by converting the pose to the lidar coordinate system. The matching calculation needs to define some transformation parameters, and three-dimensional coordinate transformation can be carried out on the point cloud to be registered through the transformation parameters to obtain transformed points. There are two sets of transformation parameters, namely a rotation parameter vector R (alpha, theta, gamma) and a translation parameter vector D (x, y, z), wherein the rotation parameter vector represents the rotation amount of a transformed point relative to a point before transformation in an attitude angle, and the translation parameter vector represents the translation amount of the transformed point relative to the point before transformation in three directions.
The transformed points can be obtained according to the following formula:
P i '＝R×P i +D；
wherein P is i ' is the transformed point, P i For the ith point in the point cloud to be registered, R is a rotation parameter vector in the current transformation parameter, and D is a translation parameter vector in the current transformation parameter.
It should be noted that, since the acquisition of the position of the mobile device is a continuous process, that is, the position of the mobile device is continuously acquired for a period of time during the operation of the mobile device, the current transformation parameter is a transformation parameter obtained according to the position of the mobile device at the current time.
In the matching process, firstly, the point cloud to be registered is transformed according to the current transformation parameters, and then the matching degree of the points before transformation and the points after transformation under the current transformation parameters is calculated. Specifically, after the three-dimensional coordinate transformation of the point cloud to be registered is completed, selecting corresponding Gaussian distribution to calculate probability density according to the voxel corresponding relation, and accumulating the probability density of all points to obtain the matching degree.
In one example, the mean and variance of the normal distribution of voxels may be calculated for each voxel of the screened grid sub-map; and obtaining the matching degree according to the transformed points, the mean value and the variance. The matching degree can be calculated according to the following formula:
wherein Fitness is the degree of matching, mu i For the mean value, P, of voxels in which the ith point in the point cloud to be registered is located i ' is the transformed point, Σ is the variance of the voxel where the ith point is located, and n is the number of points in the point cloud to be registered.
The mean value of voxels where the ith point in the point cloud to be registered is located can be obtained according to the following formula:
wherein P is ki Mu for the i-th point in the kth voxel k For the mean value of the kth voxel, m is the number of points in each voxel.
The variance of the voxel in which the i-th point cloud is located can be obtained according to the following formula:
wherein P is ki Mu for the i-th point in the kth voxel k For the mean value of the kth voxel, Σ k For the variance of the kth voxel, m is the number of points in each voxel.
It should be understood that, since the computation between voxels is not related to each other, the process of computing the mean and variance of each voxel can be accelerated by parallel computation, and the process of computing the matching degree can be completed in a parallelization computation chip, so that the real-time performance of matching is ensured by parallelization computation.
After the matching degree under the current transformation parameters is calculated, under the condition that the matching degree reaches the preset condition, the algorithm is considered to be converged, the current transformation parameters are the optimal transformation parameters, transformed points are obtained by transforming the point cloud to be registered according to the current transformation parameters, and the position of the mobile device can be determined according to the transformed point cloud.
Under the condition that the matching degree does not reach the preset condition, new transformation can be obtained by searching the maximum value of the matching degree, then the current transformation parameters are obtained again, coordinate transformation is carried out on the point cloud to be aligned according to the current transformation parameters, so that transformed points are obtained, and new matching degree is obtained according to the transformed points and the screened grid sub-map until the new matching degree reaches the preset condition.
According to the embodiment, the mean value and the variance of the voxels of the grid sub-map are subjected to parallelization calculation, and the matching degree is subjected to parallelization calculation, so that the calculation speed is improved, and the real-time performance of matching is ensured.
The embodiment of the invention also provides a mobile device, which comprises the positioning equipment of the mobile device.
In embodiments of the present invention, examples of mobile devices may include, but are not limited to, drones, and the like.
The specific embodiments of the mobile device of the present invention are substantially the same as the embodiments of the positioning method described above, and will not be described herein.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or system that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or system. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or system that comprises the element.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
From the above description of the embodiments, it will be clear to those skilled in the art that the above-described embodiment method may be implemented by means of software plus a necessary general hardware platform, but of course may also be implemented by means of hardware, but in many cases the former is a preferred embodiment. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product stored in a storage medium (e.g. ROM/RAM, magnetic disk, optical disk) as described above, comprising instructions for causing a terminal device (which may be a mobile phone, a computer, a server, an air conditioner, or a network device, etc.) to perform the method according to the embodiments of the present invention.
The foregoing description is only of the preferred embodiments of the present invention, and is not intended to limit the scope of the invention, but rather is intended to cover any equivalents of the structures or equivalent processes disclosed herein or in the alternative, which may be employed directly or indirectly in other related arts.
Claims (11)
1. A positioning method for a mobile device, comprising:
acquiring a target point cloud map;
acquiring a point cloud to be registered from a laser radar arranged on the mobile device;
gridding the target point cloud map to obtain a plurality of grid sub-maps;
and carrying out parallelization matching processing on the point cloud to be registered and the grid maps so as to determine the position of the mobile device in the target point cloud map.
2. The positioning method according to claim 1, wherein the parallelized matching processing of the point cloud to be registered and the plurality of grid maps includes:
acquiring an initial value of a current position of the mobile device;
determining a first grid sub-map within a preset range from the initial value from the grid sub-maps;
voxelization is carried out on the first grid sub-map so as to obtain a plurality of voxels;
deleting voxels with the number of points smaller than a preset value from the first grid sub-map to obtain a screened grid sub-map;
and carrying out parallelization matching processing on the point cloud to be registered and the screened grid sub-map.
3. The positioning method according to claim 2, wherein the parallelization matching processing of the point cloud to be registered and the screened grid sub-map includes:
distributing a plurality of threads for the screened grid sub-map;
and matching the point cloud to be registered with the screened grid sub-map through the threads.
4. The positioning method according to claim 3, wherein the matching the point cloud to be registered with the screened grid sub-map includes:
obtaining a current transformation parameter according to the initial value;
carrying out coordinate transformation on the point cloud to be registered according to the current transformation parameters so as to obtain transformed points;
determining the matching degree of the transformed points and the screened grid sub-map;
and under the condition that the matching degree reaches a preset condition, determining the position according to the transformed point.
5. The positioning method of claim 4 wherein the transformed points are derived according to the following formula:
P i '＝R×P i +D；
wherein P is i ' is the transformed point, P i For the ith point in the point cloud to be registered, R is a rotation parameter vector in the current transformation parameter, and D is a translation parameter vector in the current transformation parameter.
6. The positioning method of claim 4, wherein the determining the degree of matching of the transformed points to the screened grid sub-map comprises:
calculating the mean and variance of the normal distribution of the voxels for each voxel of the screened grid sub-map;
and obtaining the matching degree according to the transformed points, the mean value and the variance.
7. The positioning method of claim 6, wherein the degree of matching is obtained by the following formula:
wherein Fitness is the degree of matching, mu i For the mean value, P, of voxels in which the ith point in the point cloud to be registered is located i ' is the transformed point, Σ is the variance of the voxel where the ith point is located, and n is the number of points in the point cloud to be registered.
8. The positioning method as set forth in claim 1, further comprising:
acquiring a global point cloud map;
gridding the global point cloud map to obtain a plurality of grid maps;
acquiring the current position of the mobile device;
and determining a grid map containing the current position in the grid maps as the target point cloud map.
9. The positioning method as set forth in claim 8, further comprising:
acquiring three-dimensional coordinates and resolution of each grid map in the plurality of grid maps;
and naming each grid map according to the three-dimensional coordinates and the resolution.
10. A positioning apparatus for a mobile device, comprising:
a memory configured to store a positioning program for the mobile device;
a processor configured to call the program from the memory to enable the processor to perform the positioning method for a mobile device according to any one of claims 1 to 9.
11. A mobile device comprising a positioning apparatus for a mobile device according to claim 10.
Priority Applications (1)
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