阅读说明：本技术 手操器的定位方法、装置、存储介质及电子设备 (Positioning method and device of manual operator, storage medium and electronic equipment ) 是由 周志勇 朱虹 宋明岑 于 2019-10-23 设计创作，主要内容包括：本发明提供了一种手操器的定位方法、装置、存储介质及电子设备,所述方法包括：获取手操器所在区域的3D点云数据；从所述3D点云数据中提取所述手操器的点云数据；将所述手操器的点云数据与预设的模板点云数据进行配准,以确定所述手操器的位姿数据；根据所述位姿数据进行手操器的定位。本发明解决了由于手操器底部不平整无法保持其正面放置水平,进而导致机器人通过2D视觉定位的方法进行精准定位的问题,通过3D点云数据进行视觉定位,实现了机器人抓取过程中手操器的精准定位,使得生产线全自动化运行,提高生产效率。(The invention provides a positioning method and device of a manual operator, a storage medium and electronic equipment, wherein the method comprises the following steps: acquiring 3D point cloud data of an area where a manual operator is located; extracting point cloud data of the manual operator from the 3D point cloud data; registering the point cloud data of the manual operator with preset template point cloud data to determine pose data of the manual operator; and positioning the hand operator according to the pose data. The invention solves the problem that the robot can be accurately positioned by a 2D vision positioning method because the bottom of the manual operator is uneven and the front of the manual operator can not be kept horizontal, and realizes the accurate positioning of the manual operator in the robot grabbing process by performing the vision positioning through the 3D point cloud data, so that the production line runs automatically, and the production efficiency is improved.)

1. A method of positioning a hand-held device, the method comprising:

acquiring 3D point cloud data of an area where a manual operator is located;

extracting point cloud data of the manual operator from the 3D point cloud data;

registering the point cloud data of the manual operator with preset template point cloud data to determine pose data of the manual operator;

and positioning the hand operator according to the pose data.

2. The method of claim 1, wherein the extracting point cloud data of the manipulator from the 3D point cloud data comprises:

filtering the 3D point cloud data;

performing boundary estimation on the filtered 3D point cloud data to obtain the boundary of the manual operator;

clustering the point cloud data in the boundary of the manual operator through a region growing algorithm so as to segment the point cloud data of the manual operator from the 3D point cloud data.

3. The method of claim 2, wherein the filtering the 3D point cloud data comprises:

and filtering the 3D point cloud data by adopting a voxel grid filtering algorithm.

4. The method according to any one of claims 1 to 3, wherein the registering the point cloud data of the hand operator with preset template point cloud data to determine pose data of the hand operator comprises:

matching the point cloud data of the hand operator with preset template point cloud data by adopting an iterative nearest neighbor algorithm to determine pose data of the hand operator;

the matching of the point cloud data of the manual operator and the preset template point cloud data by adopting the iterative nearest neighbor algorithm specifically comprises the following steps:

sampling point cloud data of the manual operator to obtain a sampling data set;

extracting key point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data;

calculating a rotation matrix and a translation vector for converting the point cloud data in the sampling data set into corresponding key point cloud data;

and determining the pose data of the hand operator according to the rotation matrix and the translation vector.

5. The method of claim 4, wherein extracting key point cloud data corresponding to point cloud data in the sample dataset from the template point cloud data comprises:

extracting target point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data;

and extracting point cloud data which meets the similarity conditions of features and positions with the corresponding point cloud data in the sampling data set from the target point cloud data.

6. A positioning device for a hand-held device, the device comprising:

the acquisition module is used for acquiring 3D point cloud data of an area where the manual operator is located;

the extraction module is used for extracting the point cloud data of the manual operator from the 3D point cloud data;

the registration module is used for registering the point cloud data of the hand operator with preset template point cloud data so as to determine pose data of the hand operator;

and the positioning module is used for positioning the manual operator according to the pose data.

7. The apparatus of claim 6, wherein the extraction module comprises:

the filtering unit is used for filtering the 3D point cloud data;

the boundary estimation unit is used for carrying out boundary estimation on the filtered 3D point cloud data to obtain the boundary of the manual operator;

and the clustering unit is used for clustering the point cloud data in the boundary of the manual operator through a region growing algorithm so as to partition the point cloud data of the manual operator from the 3D point cloud data.

8. The device according to claim 6 or 7, wherein the registration module is specifically configured to match the point cloud data of the hand operator with preset template point cloud data by using an iterative nearest neighbor algorithm to determine pose data of the hand operator;

the registration module specifically includes:

the sampling unit is used for sampling the point cloud data of the manual operator to obtain a sampling data set;

an extraction unit, configured to extract key point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data;

the first calculation unit is used for calculating a rotation matrix and a translation vector which are used for converting the point cloud data in the sampling data set into corresponding key point cloud data;

and the second calculation unit is used for calculating the pose data of the hand operator according to the rotation matrix and the translation vector.

9. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 5.

10. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the steps of the method according to any of claims 1-5 are implemented when the processor executes the program.

Technical Field

The invention relates to the technical field of computers, in particular to a positioning method and device of a manual operator, a storage medium and electronic equipment.

Background

Along with automated production's demand, production facility is more and more, and in order to raise efficiency and output, automatic unloading becomes essential production line corollary equipment, and manual operator automation vision check out test set is the indispensable part of realization automated production.

At present, the manual operator automatic visual detection equipment needs a belt line to convey the manual operator to a fixed position, and a robot grabs the manual operator to a fixed tray through visual positioning to carry out next detection. However, the bottom of the hand manipulator is not flat, the postures on the belt line are different, and accurate grabbing cannot be performed by a 2D visual positioning method.

Disclosure of Invention

The present invention is directed to overcome the above-mentioned problems, and provides a method and an apparatus for positioning a hand-operated device, a storage medium, and an electronic device.

In one aspect of the present invention, there is provided a method for positioning a hand-held device, the method comprising:

acquiring 3D point cloud data of an area where a manual operator is located;

extracting point cloud data of the manual operator from the 3D point cloud data;

registering the point cloud data of the manual operator with preset template point cloud data to determine pose data of the manual operator;

and positioning the hand operator according to the pose data.

Optionally, the extracting point cloud data of the manipulator from the 3D point cloud data includes:

filtering the 3D point cloud data;

performing boundary estimation on the filtered 3D point cloud data to obtain the boundary of the manual operator;

clustering the point cloud data in the boundary of the manual operator through a region growing algorithm so as to segment the point cloud data of the manual operator from the 3D point cloud data.

Optionally, the filtering the 3D point cloud data includes:

and filtering the 3D point cloud data by adopting a voxel grid filtering algorithm.

Optionally, the registering the point cloud data of the manipulator with preset template point cloud data to determine the pose data of the manipulator includes:

and matching the point cloud data of the hand operator with preset template point cloud data by adopting an iterative nearest neighbor algorithm so as to determine the pose data of the hand operator.

Optionally, the matching the point cloud data of the hand operator with the preset template point cloud data by using an iterative nearest neighbor algorithm includes:

sampling point cloud data of the manual operator to obtain a sampling data set;

extracting key point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data;

calculating a rotation matrix and a translation vector for converting the point cloud data in the sampling data set into corresponding key point cloud data;

and determining the pose data of the hand operator according to the rotation matrix and the translation vector.

Optionally, the extracting, from the template point cloud data, key point cloud data corresponding to the point cloud data in the sampling data set includes:

extracting target point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data;

and extracting point cloud data which meets the similarity conditions of features and positions with the corresponding point cloud data in the sampling data set from the target point cloud data.

In another aspect of the present invention, there is provided a positioning device for a hand-held device, the device comprising:

the acquisition module is used for acquiring 3D point cloud data of an area where the manual operator is located;

the extraction module is used for extracting the point cloud data of the manual operator from the 3D point cloud data;

the registration module is used for registering the point cloud data of the hand operator with preset template point cloud data so as to determine pose data of the hand operator;

and the positioning module is used for positioning the manual operator according to the pose data.

Optionally, the extraction module includes:

the filtering unit is used for filtering the 3D point cloud data;

the boundary estimation unit is used for carrying out boundary estimation on the filtered 3D point cloud data to obtain the boundary of the manual operator;

and the clustering unit is used for clustering the point cloud data in the boundary of the manual operator through a region growing algorithm so as to partition the point cloud data of the manual operator from the 3D point cloud data.

Optionally, the registration module is specifically configured to match the point cloud data of the hand operator with preset template point cloud data by using an iterative nearest neighbor algorithm, so as to determine pose data of the hand operator.

Optionally, the registration module comprises:

the sampling unit is used for sampling the point cloud data of the manual operator to obtain a sampling data set;

an extraction unit, configured to extract key point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data;

the first calculation unit is used for calculating a rotation matrix and a translation vector which are used for converting the point cloud data in the sampling data set into corresponding key point cloud data;

and the second calculation unit is used for calculating the pose data of the hand operator according to the rotation matrix and the translation vector.

Optionally, the extracting unit is specifically configured to extract target point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data; and extracting point cloud data which meets the similarity conditions of features and positions with the corresponding point cloud data in the sampling data set from the target point cloud data.

Furthermore, the invention also provides a computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method as described above.

Furthermore, the present invention also provides 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 steps of the method as described above when executing the program.

The positioning method, the positioning device, the storage medium and the electronic equipment of the manual operator provided by the embodiment of the invention solve the problem that the robot can not be accurately positioned by a 2D visual positioning method because the bottom of the manual operator is uneven and the front face of the manual operator can not be kept horizontal, and realize the accurate positioning of the manual operator in the robot grabbing process by carrying out the visual positioning through the 3D point cloud data, so that the production line runs automatically, and the production efficiency is improved.

The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.

Drawings

Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:

fig. 1 is a schematic flow chart of a positioning method of a hand operator according to an embodiment of the present invention;

fig. 2 is a schematic internal flowchart of step S12 in the positioning method of the hand operator according to the embodiment of the present invention;

fig. 3 is a schematic structural diagram of a positioning device of a hand operator according to an embodiment of the present invention.

Detailed Description

Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.

It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.

Fig. 1 schematically shows a flowchart of a positioning method of a hand operator according to an embodiment of the present invention. Referring to fig. 1, the positioning method of the manual operator provided by the embodiment of the present invention specifically includes steps S11 to S14, as follows:

and S11, acquiring the 3D point cloud data of the area where the hand operator is located.

The 3D point cloud data of the area where the hand operator is located in the embodiment of the invention is point cloud data obtained by triangulation in a mode of using a monocular camera and structured light.

And S12, extracting the point cloud data of the manipulator from the 3D point cloud data.

And S13, registering the point cloud data of the hand operator with preset template point cloud data to determine the pose data of the hand operator.

The template point cloud data is point cloud data acquired by a manual operator which is put according to a standard that the front surface of the template point cloud data is horizontally placed.

And S14, positioning the hand operator according to the pose data.

The positioning method of the manual operator provided by the embodiment of the invention solves the problem that the robot can be accurately positioned by a 2D visual positioning method because the bottom of the manual operator is uneven and the front face of the manual operator cannot be kept horizontal, and the robot can be accurately positioned by 3D point cloud data, so that the accurate positioning of the manual operator in the grabbing process of the robot is realized, the production line is operated automatically, and the production efficiency is improved.

In an embodiment of the present invention, as shown in fig. 2, the extracting point cloud data of the manipulator from the 3D point cloud data in step S12 specifically includes the following steps:

and S121, filtering the 3D point cloud data.

And filtering the 3D point cloud data, specifically, filtering the 3D point cloud data by adopting a voxel grid filtering algorithm.

In the embodiment of the invention, the disordered point cloud data is filtered by using the voxel grid filtering algorithm to realize the down-sampling, and the down-sampling is realized by using the voxel grid method, so that the number of points can be reduced, the point cloud data can be reduced, the shape characteristics of the point cloud can be kept, and the registration precision can be improved. In a specific example, the algorithm can be implemented by using a PCL point cloud library, where the VoxelGrid class implemented by PCL creates a three-dimensional voxel grid from the input point cloud data, and then, in each voxel, the center of gravity of all the points in the voxel is used to approximately display other points in the voxel, so that all the points in the voxel are finally represented by a center of gravity point, and a filtered point cloud is obtained after all the voxels are processed. According to the method and the device, the points far away from the measured object in the point cloud data, namely the outliers, can be effectively removed, the follow-up point cloud data processing is not interfered by environmental noise, and the processing speed of the program is further improved.

And S122, carrying out boundary estimation on the filtered 3D point cloud data to obtain the boundary of the manual operator.

In this embodiment, a normal is estimated from the filtered point cloud data, and then a boundary is estimated from the normal and the point cloud data. Specifically, the input point cloud data is determined, and because the boundary estimation needs to depend on the normal line, the normal line of the boundary estimation needs to be preset, in addition, the radius needed by the boundary estimation and the angle threshold value during the boundary estimation need to be preset, and finally, the boundary estimation is stored according to the set search mode kdtree.

S123, clustering the point cloud data in the boundary of the manual operator through a region growing algorithm to segment the point cloud data of the manual operator from the 3D point cloud data.

In this embodiment, after the boundaries of the manual operators are extracted, the manual operators are clustered and segmented in a region growing manner, so as to remove the background clutter feature. The algorithm is based on the comparison of angles between point normals, and combines adjacent points satisfying the smoothness constraint together and outputs the combined points in a cluster of point sets, wherein each cluster of point sets is considered to belong to the same plane. Region growing starts from the point with the smallest curvature value, so all curvature values have to be calculated and sorted. This is because the point of least curvature is located in the flat region, and growing from the flattest region may reduce the total number of regions. In a specific example, the implementation of clustering by a region growing algorithm specifically includes the following steps: (1) the point cloud has unmarked points, the points are sorted according to the curvature values of the points, the point with the minimum curvature value is found, and the point with the minimum curvature value is added into the seed point set; (2) for each seed point, the algorithm finds all the neighboring points of the perimeter. (3) Calculating the normal angle difference between each neighboring point and the current seed point, and if the difference value is smaller than a set threshold value, the neighboring point is considered in a key manner, and the second step of test is carried out; (4) the neighbor point passes the normal angle difference test and if its curvature is smaller than the threshold we set, this point is added to the set of seed points, i.e. belongs to the current plane. (5) The points that pass the two checks are removed from the original point cloud. (6) Setting the number min of the minimum point cluster and the max of the maximum point cluster; repeating the steps 1-3, generating all planes with the points at min and max by the algorithm, and marking different colors on different planes for distinguishing. (7) And stopping the algorithm until the point clusters generated by the algorithm in the rest points cannot meet the min.

In an embodiment of the present invention, the registering the point cloud data of the hand operator with the preset template point cloud data to determine the pose data of the hand operator includes: and matching the point cloud data of the hand operator with preset template point cloud data by adopting an iterative nearest neighbor algorithm so as to determine the pose data of the hand operator.

The specific implementation includes the following steps that are not shown in the drawings:

s131, sampling point cloud data of the manual operator to obtain a sampling data set.

And S132, extracting key point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data. Extracting key point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data, wherein the extracting specifically comprises the following steps: extracting target point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data; and extracting point cloud data which meets the similarity conditions of features and positions with the corresponding point cloud data in the sampling data set from the target point cloud data.

And S133, calculating a rotation matrix and a translation vector for converting the point cloud data in the sampling data set into corresponding key point cloud data.

And S134, determining pose data of the hand operator according to the rotation matrix and the translation vector.

In the embodiment, the matching method of the point cloud data of the hand operator is carried out by using an iterative nearest neighbor point registration mode, and comprises the following implementation steps of firstly sampling the point cloud data of the hand operator which is segmented out, reducing the data volume, secondly determining an initial corresponding point set, namely a point set which is extracted by template point cloud data and hand operator point cloud data which needs to be registered with a template according to the same point selection standard, respectively calculating characteristic descriptors of all the selected point cloud data, estimating the corresponding relation of the point cloud data and the template point cloud data by combining the coordinate positions of the characteristic descriptors in two data sets and taking the similarity of the characteristics and the positions between the two data sets as the basis, and preliminarily estimating corresponding point pairs. The pairs of corresponding points of the error that have an effect on the registration are then removed. And finally, solving the coordinate transformation to obtain the pose of the manual operator. And (3) iteratively calculating the optimal coordinate transformation, namely a rotation matrix and a translation vector, of the manual operator point set by a least square method, so that the error function is minimum. The rotation matrix and the translation vector can enable the manual operator point set to rotate to the template point set, the pose of the manual operator point set relative to the template is solved, accurate positioning is conducted according to the pose data, and positioning information is sent to the robot to achieve grabbing.

According to the positioning method of the manual operator, provided by the embodiment of the invention, three-dimensional characteristic processing is carried out by using 3D point cloud data of the manual operator, redundant point cloud data is removed by carrying out down-sampling through voxel filtering, the manual operator is extracted from a complex background through a region growing algorithm, and finally the coordinate position and the posture of the manual operator are determined through an iterative nearest neighbor point registration method. The invention can solve the problem that the robot cannot be positioned and grabbed through plane characteristics because the front of the hand-operated device is placed horizontally due to uneven bottom, can replace manual work, solves the semi-automatic state of the production process of the hand-operated device, leads the production line to be fully automatic, and improves the production efficiency.

For simplicity of explanation, the method embodiments are described as a series of acts or combinations, but those skilled in the art will appreciate that the embodiments are not limited by the order of acts described, as some steps may occur in other orders or concurrently with other steps in accordance with the embodiments of the invention. Further, those skilled in the art will appreciate that the embodiments described in the specification are presently preferred and that no particular act is required to implement the invention.

Fig. 3 schematically shows a structural diagram of a positioning device of a hand operator according to an embodiment of the invention. Referring to fig. 3, the positioning apparatus of the manual operator in the embodiment of the present invention specifically includes an obtaining module 201, an extracting module 202, a registering module 203, and a positioning module 204, where:

the acquisition module 201 is used for acquiring 3D point cloud data of an area where a hand operator is located;

an extraction module 202, configured to extract point cloud data of the manipulator from the 3D point cloud data;

the registration module 203 is configured to register the point cloud data of the hand operator with preset template point cloud data to determine pose data of the hand operator;

and the positioning module 204 is used for positioning the hand operator according to the pose data.

In the embodiment of the present invention, the extracting module 202 specifically includes a filtering unit, a boundary estimating unit, and a clustering unit, where:

the filtering unit is used for filtering the 3D point cloud data;

the boundary estimation unit is used for carrying out boundary estimation on the filtered 3D point cloud data to obtain the boundary of the manual operator;

and the clustering unit is used for clustering the point cloud data in the boundary of the manual operator through a region growing algorithm so as to partition the point cloud data of the manual operator from the 3D point cloud data.

The filtering unit is specifically configured to perform filtering processing on the 3D point cloud data by using a voxel grid filtering algorithm.

In an embodiment of the present invention, the registration module 203 is specifically configured to match the point cloud data of the hand operator with preset template point cloud data by using an iterative nearest neighbor algorithm, so as to determine the pose data of the hand operator.

Further, the registration module 203 specifically includes a sampling unit, an extraction unit, a first calculation unit, and a second calculation unit, where:

the sampling unit is used for sampling the point cloud data of the manual operator to obtain a sampling data set;

an extraction unit, configured to extract key point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data;

the first calculation unit is used for calculating a rotation matrix and a translation vector which are used for converting the point cloud data in the sampling data set into corresponding key point cloud data;

and the second calculation unit is used for calculating the pose data of the hand operator according to the rotation matrix and the translation vector.

Further, the extracting unit is specifically configured to extract target point cloud data corresponding to the point cloud data in the sampling data set from the template point cloud data; and extracting point cloud data which meets the similarity conditions of features and positions with the corresponding point cloud data in the sampling data set from the target point cloud data.

For the device embodiment, since it is basically similar to the method embodiment, the description is simple, and for the relevant points, refer to the partial description of the method embodiment.

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 place, or may be distributed on a plurality of 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.

The positioning method and the positioning device for the manual operator provided by the embodiment of the invention solve the problem that the robot can not be accurately positioned by a 2D visual positioning method because the bottom of the manual operator is uneven and the front face of the manual operator can not be kept horizontal, and realize the accurate positioning of the manual operator in the grabbing process of the robot by carrying out the visual positioning through the 3D point cloud data, so that the production line runs automatically, and the production efficiency is improved.

Furthermore, an embodiment of the present invention also provides a computer-readable storage medium, on which a computer program is stored, which when executed by a processor implements the steps of the method as described above.

In this embodiment, the module/unit integrated with the positioning device of the hand operator may be stored in a computer readable storage medium if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, all or part of the flow of the method according to the embodiments of the present invention may also be implemented by a computer program, which may be stored in a computer-readable storage medium, and when the computer program is executed by a processor, the steps of the method embodiments may be implemented. Wherein the computer program comprises computer program code, which may be in the form of source code, object code, an executable file or some intermediate form, etc. The computer-readable medium may include: any entity or device capable of carrying the computer program code, recording medium, usb disk, removable hard disk, magnetic disk, optical disk, computer Memory, Read-only Memory (ROM), Random Access Memory (RAM), electrical carrier wave signals, telecommunications signals, software distribution medium, etc. It should be noted that the computer readable medium may contain content that is subject to appropriate increase or decrease as required by legislation and patent practice in jurisdictions, for example, in some jurisdictions, computer readable media does not include electrical carrier signals and telecommunications signals as is required by legislation and patent practice.

The electronic device provided by the embodiment of the invention 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 computer program to realize the steps in the positioning method embodiments of each hand operator, such as S11 shown in FIG. 1, and 3D point cloud data of the area where the hand operator is located are acquired; s12, extracting the point cloud data of the manipulator from the 3D point cloud data; s13, registering the point cloud data of the hand manipulator with preset template point cloud data to determine pose data of the hand manipulator; and S14, positioning the hand operator according to the pose data. Alternatively, the processor, when executing the computer program, implements the functions of the modules/units in the positioning apparatus embodiments of the handmanipulators, such as the obtaining module 201, the extracting module 202, the registering module 203 and the positioning module 204 shown in fig. 3.

Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to implement the invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used for describing the execution process of the computer program in the positioning device of the hand operator. For example, the computer program may be segmented into an acquisition module 201, an extraction module 202, a registration module 203 and a localization module 204.

The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like that is central to and connects the various parts of the overall electronic device using various interfaces and lines.

The memory may be used to store the computer programs and/or modules, and the processor may implement various functions of the electronic device by running or executing the computer programs and/or modules stored in the memory and calling data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required by at least one function (such as a sound playing function, an image playing function, etc.), and the like; the storage data area may store data (such as audio data, a phonebook, etc.) created according to the use of the cellular phone, and the like. In addition, the memory may include high speed random access memory, and may also include non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), at least one magnetic disk storage device, a Flash memory device, or other volatile solid state storage device.

Those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than others, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.

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 will 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.