Patent Description:
Visual localization technology refers to accomplishment of localization tasks through machine vision, which is a research hotspot in the fields of AR technology and mobile robots in recent years. On the one hand, many mobile phone manufacturers realize AR (Augmented Reality) functions in some mobile phones by using cameras of the mobile phones and visual localization algorithms, but due to the limited accuracy of the existing localization technologies, AR applications in the mobile phones are restricted, and thus mobile phone manufacturers are committed to the research of visual localization. On the other hand, due to advantages of machine vision with respect to traditional laser sensors, some mobile robot companies are also investing in the research and development of visual localization in order to solve existing problems.

Document entitled "A Simultaneous Localization and Mapping (SLAM) Framework for <NUM> Map Building Based on Low-Cost LiDAR and Vision Fusion" provides a new graph optimization-based SLAM framework through the combination of low-cost LiDAR sensor and vision sensor.

The present disclosure provides a method and a device for obtaining localization information and a corresponding computer-readable storage medium, to solve the existing problems in the related technologies and to improve robustness of visual localization in the actual environment.

This summary is provided to introduce a selection of aspects of the present disclosure in a simplified form that are further described below in the detailed description.

According to a first aspect of the present disclosure, a method for obtaining localization information is provided according to claim <NUM>. Optional features are set out in dependent claims <NUM> to <NUM>.

According to a second aspect of the present disclosure, a device for obtaining localization information is provided according to claim <NUM>. Optional features are set out in dependent claims <NUM> to <NUM>.

According to a third aspect of the embodiments of the present disclosure, a device for obtaining localization information is provided according to claim <NUM>.

According to a fourth aspect of the embodiments of the present disclosure, a non-transitory computer-readable storage medium is provided according to claim <NUM>.

According to technical solutions provided in the embodiments of the present disclosure, three-dimensional coordinates of spatial obstacle points are obtained based on the depth map, environmental three-dimensional coordinates corresponding to each of the estimated target postures are obtained based on the relocation postures, the relocation variance and the point cloud map, and the three-dimensional coordinates of the spatial obstacle points are scanned and matched with the environmental three-dimensional coordinates corresponding to each of the estimated target postures to determine whether the relocation postures are available, and then localization information is obtained, all of which are implemented by using the existing localization device, without the need of additional hardware sensing devices or changing the main structure of the visual localization system. Through the above methods, the problem of weak localization robustness caused by the high dependence of the relocation module on the visual algorithm in the existing visual localization system is solved, and the localization robustness is improved.

It is to be noted that if additional sensors are added to the visual localization system, it is also possible to solve the above problems, such as adding laser sensors for algorithm fusion, or using a coded disc installed on the robot body for algorithm fusion in the field of use of a ground mobile robot. However, these solutions for adding external sensors have no advantages in terms of cost, power consumption and size. The methods provided in the disclosure do not need to add additional hardware sensor devices, but to add parallel modules, such that the problem of localization errors of the relocation module in the actual operation of the visual localization system is solved, thereby improving the robustness of the visual localization system in the actual environment.

It is to be understood that the above general description and the following detailed description below are merely exemplary and explanatory and not intended to limit the present disclosure.

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the present disclosure.

The specific aspects of the present disclosure, which have been illustrated by the accompanying drawings described above, will be described in detail below. These accompanying drawings and description are not intended to limit the scope of the present disclosure in any manner, but to explain the concept of the present disclosure to those skilled in the art via referencing specific aspects.

At present, there are several visual localization technical solutions. For the convenience of description, the technical solutions of the present disclosure will be described by taking visual SLAM (simultaneous localization and mapping) technology as an example.

From the perspective of visual sensors, visual SLAM mainly includes monocular + IMU SLAM, binocular SLAM and RGBD-SLAM. These three types of visual SLAM have different three-dimensional visual calculation methods, but due to requirements of the visual SLAM, the framework components of the whole visual SLAM are basically the same, including front-end optimization and back-end optimization, which are divided into four main modules: a localization module, a mapping module, a relocation module and a closed-loop module. These four modules are used to accomplish the tasks of SLAM. As a method for correcting localization errors in the visual system, the relocation module is very important to improve the robustness of the visual localization system. However, in the navigation and localization of many actual scenes, the relocation algorithm may fail due to the similar distribution of feature points in the visual system, which not only cannot correct the wrong localization, but also easily leads to the wrong localization. Once the wrong localization occurs, the entire existing visual SLAM system will fail.

<FIG> illustrates a schematic diagram of a relocation process in the existing visual localization, in which a relocation module takes image features as an input, outputs postures after relocation and optimizes posture estimation of the system. The existing visual localization has been discussed in the documents https://en. org/wiki/Simultaneous_localization_and_mapping or http://webdiis. es/~raulmur/orbslam/.

The relocation module is introduced in order to solve the problem of cumulative error of posture estimation. However, due to the complex scenes in reality, the algorithm (such as Bag Of Words) and the heuristic selection rule for key frames adopted by the relocation module are difficult to ensure that the key frames have a good distribution in space while all the key-frame feature vectors have strong discrimination. This results in a probability that the relocation module gives a wrong posture in practice, which will lead to the localization error, and further, this error cannot be eliminated by the visual SLAM system itself until the next correct relocation, which leads to the localization error of the visual SLAM.

The present disclosure provides a method for obtaining localization information. On the basis of the existing visual localization system, a processing module, parallel with the relocation module, is added to determine whether an output posture of the relocation module is correct, so as to improve the robustness of the visual localization.

The present disclosure provides a method for obtaining localization information. As illustrated in <FIG>, the method includes the following operations.

In <NUM>, image information and related information of the image information are obtained, wherein the related information includes a depth map, a point cloud map, relocation postures and a relocation variance after relocation.

In <NUM>, three-dimensional coordinates of spatial obstacle points are obtained based on the depth map.

In <NUM>, target postures and environmental three-dimensional coordinates corresponding to each of the target postures are obtained based on the relocation postures, the relocation variance and the point cloud map.

In <NUM>, the three-dimensional coordinates of the spatial obstacle points are scanned and matched with the environmental three-dimensional coordinates to obtain matching result information.

In <NUM>, localization information is obtained based on the relocation postures and the relocation variance when the matching result information satisfies a predetermined condition.

In <NUM>, the image information during localization illustrated in <FIG> is obtained. The image information may be a frame of image. The point cloud map is obtained by processing the frame of image, and relocation postures and relocation variance corresponding to the relocation postures are obtained based on relocation of the frame of image. The point cloud map, the relocation postures and the relocation variance are illustrated in <FIG>. In addition, the depth map obtained corresponds to the frame of image, that is, the frame of image and its corresponding depth map are both taken at the same time for the same scene.

The depth map in <NUM> refers to a dense depth map. The binocular visual device and the RGBD visual device can directly output the dense depth map information. The monocular + IMU visual device can process a sparse depth map to obtain the dense depth map, but due to the limited image quality, the method of the present disclosure is not suitable for the monocular + IMU visual device.

In <NUM>, the three-dimensional coordinates of spatial obstacle points obtained based on the depth map may also be calculated by the camera formula. This calculation process is known to those skilled in the art, which will not be elaborated herein.

In <NUM>, the operation of obtaining target postures and environmental three-dimensional coordinates corresponding to each of the target postures based on the relocation postures, the relocation variance and the point cloud map may include: obtaining the target postures based on the relocation postures and the relocation variance, wherein the target postures are represented by particles, and obtaining the environmental three-dimensional coordinates corresponding to the target postures through the particles and the point cloud map. The specific process will be described in the following specific embodiments.

In <NUM> and <NUM>, the environmental three-dimensional coordinates corresponding to each of the target postures can be matched with the three-dimensional coordinates of spatial obstacle points by a manner of scan matching, and a matching score can be calculated. The highest matching score might be determined from the matching scores of these target postures. In this case, the matching result information may be a matching score of each target posture, and the predetermined condition may be the condition whether the highest matching score exceeds a predetermined threshold. The predetermined threshold may be preset by a user or obtained in advance through offline experiments according to a specific application scene, which will not be limited in the disclosure. If the highest matching score meets the requirement of exceeding the predetermined threshold, it might be determined that the relocation posture is correct. If the highest matching score does not meet the threshold requirement, it might be determined that the relocation posture is wrong, and the result of the relocation is not be used.

The above method can guarantee accuracy of output postures of the relocation, such that the problem of the wrong posture result given by the relocation module is solved, thereby improving the robustness of the visual localization.

As a refinement and extension of the embodiment illustrated in <FIG>, one embodiment of the disclosure discloses another localization method. Referring to <FIG> is a flowchart illustrating the operations of obtaining target postures and environmental three-dimensional coordinates corresponding to each of the target postures based on the relocation postures, the relocation variance and the point cloud map. As illustrated in <FIG>, the operation in <NUM> of <FIG> may further include the following actions.

In <NUM>, a particle set is obtained based on the relocation postures and the relocation variance, wherein each particle in the particle set corresponds to one of the target postures.

In <NUM>, environmental three-dimensional coordinates of each particle are obtained based on the point cloud map, wherein the environmental three-dimensional coordinates corresponding to each of the target postures are environmental three-dimensional coordinates of the particle corresponding to the target posture.

In <NUM>, the operation of obtaining the particle set based on the relocation postures and the relocation variance may specifically use the method of constructing Gaussian probability distribution, Kalman filter or Bayesian estimation. The calculation process is known to those skilled in the art, which will not be elaborated herein.

In <NUM>, the environmental three-dimensional coordinates of each particle are coordinates of the point cloud map projected into the coordinate system corresponding to each target posture (particle).

As a refinement and extension of the embodiment illustrated in <FIG>, the embodiment of the disclosure discloses another localization method. Referring to <FIG> is a flowchart illustrating the operations of obtaining target postures and environmental three-dimensional coordinates corresponding to each of the target postures based on the relocation postures, the relocation variance and the point cloud map. As illustrated in <FIG>, the operation of obtaining the particle set based on the relocation postures and the relocation variance in <NUM> of <FIG> may further include the following actions.

In <NUM>, a probability density of Gaussian probability distribution is obtained based on the relocation postures and the relocation variance.

In <NUM>, the relocation postures are sampled according to the probability density of Gaussian probability distribution to obtain the particle set.

The operation of obtaining the environmental three-dimensional coordinates of each particle based on the point cloud map in <NUM> of <FIG> may further include the following action.

In <NUM>, the environmental three-dimensional coordinates of each particle are obtained by a ray casting algorithm based on the point cloud map.

In <NUM> and <NUM>, the target postures are obtained through the probability density of Gaussian probability distribution, i.e., the particle set is obtained. The use of the Gaussian probability distribution is due to the case that the Gaussian distribution has a faster calculation speed without dealing with complex Jacobian matrix operations and is also easy to model.

In <NUM>, the point cloud map and each particle are used to calculate the environmental three-dimensional coordinates of the corresponding particle by the ray casting algorithm. The method adopted by the ray casting algorithm is known to those skilled in the art, which will not be elaborated herein.

As a refinement and extension of the embodiment illustrated in <FIG>, the embodiment of the disclosure discloses another localization method. Referring to <FIG> is a flowchart illustrating the operations of scanning and matching the three-dimensional coordinates of the spatial obstacle points with the environmental three-dimensional coordinates to obtain matching result information and obtaining localization information based on the relocation postures and the relocation variance when the matching result information satisfies a predetermined condition. As illustrated in <FIG>, the operations in <NUM> and <NUM> of <FIG> may further include the following actions.

In <NUM>, a matching score of each particle is obtained by scanning and matching the three-dimensional coordinates of the spatial obstacle points with the environmental three-dimensional coordinates of each particle.

In <NUM>, when the highest matching score is greater than a predetermined threshold, the relocation postures are determined as a localization result.

In this embodiment, the environmental three-dimensional coordinates of each particle are environmental three-dimensional coordinates of the target posture corresponding to the particle obtained based on the point cloud map, and the matching score of each particle may be obtained by scanning and matching these two kinds of three-dimensional coordinates. If the matching score of any particle is greater than a predetermined threshold, it is determined that the relocation posture is correct. Therefore, the highest matching score is selected to determine whether the highest matching score is greater than a predetermined threshold. The predetermined threshold may be obtained in advance through offline experiments according to a specific application scene. In another example, the predetermined threshold may be preset by a user.

As a refinement and extension of the embodiment illustrated in <FIG>, the embodiment of the disclosure discloses another localization method. Referring to <FIG> is a flowchart illustrating the operations of obtaining a matching score of each particle by scanning and matching the three-dimensional coordinates of the spatial obstacle points with the environmental three-dimensional coordinates of each particle. As illustrated in <FIG>, the operation in <NUM> of <FIG> may further include the following action.

In <NUM>, the three-dimensional coordinates of the spatial obstacle points are scanned and matched with the environmental three-dimensional coordinates of each particle by using a likelihood field model, and the matching score of each particle is obtained.

When the scan matching algorithm is performed, the matching scores of the particles are calculated by using a likelihood field model. The matching algorithm and the likelihood field model may be in a manner known to those skilled in the art, which will not be elaborated herein.

<FIG> illustrates a specific embodiment according to the present disclosure. In the embodiment, the localization information is obtained based on the result of SLAM relocation. The method of this specific embodiment includes the following operations.

In <NUM>, a frame of image for which this SLAM relocation is applied, a depth map for the same scene obtained at the same time as the frame of image, a point cloud map based on the frame of image, as well as relocation postures and corresponding relocation variance obtained by the relocation based on the frame of image are obtained.

In <NUM>, a probability density of Gaussian probability distribution is obtained based on the relocation postures and the relocation variance, and the relocation postures are sampled to obtain the particle set according to the probability density of Gaussian probability distribution.

In <NUM>, when the highest matching score is greater than a predetermined threshold, the relocation postures are determined as a localization result. When the highest matching score is less than or equal to the predetermined threshold, the relocation postures are not used.

It can be seen from the above specific embodiments, three-dimensional coordinates of spatial obstacle points are obtained based on the depth map, environmental three-dimensional coordinates corresponding to each of the estimated target postures are obtained based on the relocation postures, the relocation variance and the point cloud map, and the three-dimensional coordinates of the spatial obstacle points are scanned and matched with the environmental three-dimensional coordinates corresponding to each of the estimated target postures to determine whether the relocation postures are available, and then localization information is obtained, all of which are implemented by using the existing localization device, without the need of additional hardware sensing devices or changing the main structure of the visual localization system. Through the above methods, the problem of weak localization robustness caused by the high dependence of the relocation module on the visual algorithm in the existing visual localization system is solved, and the localization robustness is improved.

It is to be noted that if additional sensors are added to the visual localization system, it is also possible to solve the above problems, such as adding laser sensors for algorithm fusion, or using a coded disc installed on the robot body for algorithm fusion in the field of use of a ground mobile robot. However, these solutions for adding external sensors have no advantages in terms of cost, power consumption and size. The methods provided in the disclosure are not required to add additional hardware sensor devices, but to add parallel modules, such that the problem of localization errors of the relocation module in the actual operation of the visual localization system is solved, thereby improving the robustness of the visual localization system in the actual environment.

The present disclosure also provides a device for obtaining localization information. As illustrated as <FIG>, the device includes an obtaining module <NUM>, an obstacle point coordinate calculation module <NUM>, an environmental coordinate calculation module <NUM> and a scan matching module <NUM>.

The obtaining module <NUM> is configured to obtain image information and related information of the image information. The related information includes a depth map, a point cloud map, relocation postures and a relocation variance after the relocation.

The obstacle point coordinate calculation module <NUM> is configured to obtain three-dimensional coordinates of spatial obstacle points based on the depth map.

An environmental coordinate calculation module <NUM> is configured to obtain target postures and environmental three-dimensional coordinates corresponding to each of the target postures based on the relocation postures, the relocation variance and the point cloud map.

The scan matching module <NUM> is configured to scan and match the three-dimensional coordinates of the spatial obstacle points with the environmental three-dimensional coordinates to obtain matching result information, and obtain localization information based on the relocation postures and the relocation variance when the matching result information satisfies a predetermined condition.

In an optional embodiment, the environmental coordinate calculation module <NUM> is further configured to:.

In an optional embodiment, the scan matching module <NUM> is further configured to:.

In an optional embodiment, the scan matching module <NUM> is further configured to:
scan and match the three-dimensional coordinates of the spatial obstacle points with the environmental three-dimensional coordinates of each particle by using a likelihood field model, and obtain the matching score of each particle.

With respect to the device in the above embodiments, the specific manners in which the modules perform operations have been described in detail in the method embodiments, which will not be elaborated herein.

The present disclosure also provides a device for obtaining localization information, which includes a processor and a memory for storing instructions executable by the processor. The processor is configured to implement operations of any method for obtaining localization information in the above-mentioned embodiments. In an implementation, the processor may implement the functions of the obtaining module <NUM>, the obstacle point coordinate calculation module <NUM>, the environmental coordinate calculation module <NUM> and the scan matching module <NUM>.

<FIG> is a block diagram illustrating a device <NUM> for obtaining localization information according to an exemplary embodiment of the disclosure. For example, the device <NUM> may be a mobile phone, a computer, a digital broadcasting terminal, a messaging device, a gaming console, a tablet, a medical device, exercise equipment, a personal digital assistant or the like.

The processing component <NUM> typically controls overall operations of the device <NUM>, such as the operations associated with display, telephone calls, data communications, camera operations and recording operations. The processing component <NUM> may include one or more processors <NUM> to execute instructions to perform all or part of the steps in the abovementioned methods.

Examples of such data include instructions for any application or method operated on the device <NUM>, contact data, phonebook data, messages, pictures, videos, etc. The memory <NUM> may be implemented by any type of volatile or non-volatile memory devices, or a combination thereof, such as a static random access memory (SRAM), an electrically erasable programmable read-only memory (EEPROM), an erasable programmable read-only memory (EPROM), a programmable read-only memory (PROM), a read-only memory (ROM) , a magnetic memory, a flash memory, a magnetic or optical disk.

The power component <NUM> may include a power management system, one or more power sources, and any other components associated with generation, management and distribution of power for the device <NUM>.

The multimedia component <NUM> includes a screen providing an output interface between the device <NUM> and a user. In some embodiments of the disclosure, the screen may include a liquid crystal display (LCD) and a touch panel (TP). The touch sensors may not only sense a boundary of a touch or swipe action, but also sense a period of time and pressure associated with the touch or swipe action. In some embodiments of the disclosure, the multimedia component <NUM> includes a front camera and/or a rear camera. The front camera and/or the rear camera may receive external multimedia data when the device <NUM> is in an operation mode, such as a photographing mode or a video mode. Each of the front camera and the rear camera may be a fixed optical lens system or have focusing and optical zooming capability.

The audio component <NUM> is configured to output and/or input audio signals. For example, the audio component <NUM> includes a microphone (MIC) configured to receive an external audio signal when the device <NUM> is in an operation mode, such as a call mode, a recording mode, and a voice recognition mode. The received audio signal may be further stored in the memory <NUM> or transmitted via the communication component <NUM>. In some embodiments of the disclosure, the audio component <NUM> further includes a speaker to output audio signals.

The I/O interface <NUM> provides an interface between the processing component <NUM> and peripheral interface modules, and the peripheral interface module may be a keyboard, a click wheel, buttons, and the like. The button may include, but are not limited to, a home button, a volume button, a starting button, and a locking button.

The sensor component <NUM> may include a P-sensor configured to detect presence of an object nearby without any physical contact.

The communication component <NUM> is configured to facilitate wired or wireless communication between the device <NUM> and other equipment. The device <NUM> may access a communication-standard-based wireless network, such as a Wireless Fidelity (Wi-Fi) network, a 2nd-Generation (<NUM>) or 3rd-Generation (<NUM>) network or a combination thereof. In an exemplary embodiment, the communication component <NUM> receives a broadcast signal or broadcast associated information from an external broadcast management system through a broadcast channel. In an exemplary embodiment, the communication component <NUM> further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on a Radio Frequency Identification (RFID) technology, an Infrared Data Association (IrDA) technology, an Ultra-WideBand (UWB) technology, a Bluetooth (BT) technology or another technology.

In exemplary embodiments, the device <NUM> may be implemented by one or more Application Specific Integrated Circuits (ASICs), Digital Signal Processors (DSPs), Digital Signal Processing Devices (DSPDs), Programmable Logic Devices (PLDs), Field Programmable Gate Arrays (FPGAs), controllers, micro-controllers, microprocessors or other electronic components, and is configured to execute the abovementioned methods.

In an exemplary embodiment of the disclosure, there is also provided a non-transitory computer-readable storage medium including an instruction, such as the memory <NUM> including an instruction, and the instruction may be executed by the processor <NUM> of the device <NUM> to implement the abovementioned methods. For example, the non-transitory computer-readable storage medium may be a ROM, Random Access Memory (RAM), a Compact Disc Read-Only Memory (CD-ROM), a magnetic tape, a floppy disc, an optical data storage device and the like.

It is noted that the various modules, sub-modules, units, and components in the present disclosure can be implemented using any suitable technology. For example, a module may be implemented using circuitry, such as an integrated circuit (IC). As another example, a module may be implemented as a processing circuit executing software instructions.

The present disclosure also provides a non-transitory computer-readable storage medium. An instruction in the storage medium may be executed by a processor of a terminal to enable the terminal to implement a method for obtaining localization information. The method includes: obtaining image information and related information of the image information, wherein the related information includes a depth map, a point cloud map, relocation postures and a relocation variance after relocation; obtaining three-dimensional coordinates of spatial obstacle points based on the depth map; obtaining target postures and environmental three-dimensional coordinates corresponding to each of the target postures based on the relocation postures, the relocation variance and the point cloud map; scanning and matching the three-dimensional coordinates of the spatial obstacle points with the environmental three-dimensional coordinates to obtain matching result information; and obtaining localization information based on the relocation postures and the relocation variance when the matching result information satisfies a predetermined condition.

<FIG> is a block diagram illustrating a device <NUM> for obtaining localization information according to an exemplary embodiment of the disclosure. For example, the device <NUM> may be a server. Referring to <FIG>, the device <NUM> includes a processing component <NUM>, which further includes one or more processor and memory resource represented by a memory <NUM> for storing instructions executable by the processing component <NUM>, such as an application program. The application program stored in the memory <NUM> may include one or more modules, and each of those modules corresponds to a set of instructions. In addition, the processing component <NUM> is configured to execute the instructions to implement the above method, which includes: obtaining image information and related information of the image information, wherein the related information includes a depth map, a point cloud map, relocation postures and a relocation variance after relocation; obtaining three-dimensional coordinates of spatial obstacle points based on the depth map; obtaining target postures and environmental three-dimensional coordinates corresponding to each of the target postures based on the relocation postures, the relocation variance and the point cloud map; scanning and matching the three-dimensional coordinates of the spatial obstacle points with the environmental three-dimensional coordinates to obtain matching result information; and obtaining localization information based on the relocation postures and the relocation variance when the matching result information satisfies a predetermined condition.

The device <NUM> may also include a power component <NUM> configured to perform power management of the device <NUM>, a wired or wireless network interface <NUM> configured to connect the device <NUM> to a network, and an input/output (I/O) interface <NUM>. The device <NUM> may operate based on an operating system stored in the memory <NUM>, such as Windows ServerTM, Mac OS XTM, UnixTM, LinuxTM, FreeBSDTM or the like.

Other embodiments of the present disclosure will be apparent to those skilled in the art from consideration of the specification and practice of the present disclosure. This present disclosure is intended to cover any variations, uses, or adaptations of the present disclosure following the general principles thereof and including such departures from the present disclosure as come within known or customary practice in the art.

Claim 1:
A method for obtaining localization information, applied to a visual localization system including a relocation module, the method comprising:
obtaining (<NUM>) image information of an image and related information of the image information from the relocation module, wherein the related information comprises: a depth map, a point cloud map, relocation postures and a relocation variance after relocation;
obtaining (<NUM>) three-dimensional coordinates of spatial obstacle points based on the depth map;
obtaining (<NUM>) target postures and environmental three-dimensional coordinates corresponding to each of the target postures based on the relocation postures, the relocation variance and the point cloud map;
scanning and matching (<NUM>) the three-dimensional coordinates of the spatial obstacle points with the environmental three-dimensional coordinates to obtain matching result information;
obtaining (<NUM>), if the matching result information satisfies a predetermined condition, localization information based on the relocation postures and the relocation variance; and
if the matching result information does not satisfy the predetermined condition, determining that the relocation postures are wrong;
characterized by obtaining (<NUM>) target postures and environmental three-dimensional coordinates corresponding to each of the target postures based on the relocation postures, the relocation variance and the point cloud map comprises:
obtaining (<NUM>) a probability density of Gaussian probability distribution based on the relocation postures and the relocation variance;
sampling (<NUM>) the relocation postures to obtain a particle set according to the probability density of Gaussian probability distribution, wherein each particle in the particle set corresponds to one of the target postures; and
obtaining (<NUM>) environmental three-dimensional coordinates of each particle by a ray casting algorithm based on the point cloud map, wherein the environmental three-dimensional coordinates corresponding to each of the target postures are environmental three-dimensional coordinates of the particle corresponding to the target posture.