Method for Relocating a Mobile Vehicle in a SLAM Map and Mobile Vehicle

Abstract

The present disclosure provides a method and a mobile vehicle for relocating a mobile vehicle in a simultaneous localization and mapping (SLAM) map. The method is used in a mobile vehicle moving in an area and includes: establishing a SLAM map of the corresponding area at an initial time point; detecting a first position trajectory and a first orientation trajectory of the mobile vehicle on the SLAM map through a non-SLAM positioning device; detecting a probability of getting lost of the mobile vehicle between a first timestamp and a second timestamp through a SLAM positioning device; determining whether a condition is satisfied; and when the above condition is satisfied at a current time point, updating the SLAM map to a new SLAM map corresponding to the current time point and updating the positioning information of the mobile vehicle in the new SLAM map.

Description

Technical Field

[0001] The present disclosure relates to a method and a mobile vehicle for repositioning, and particularly to a method and a mobile vehicle for repositioning a mobile vehicle in a map of Simultaneous Localization and Mapping (SLAM). Background Art

[0002] As a system that enables a mobile robot to map its environment and maintain the working data of its position in the map, Simultaneous Localization and Mapping (SLAM) is both accurate and versatile. Its reliability and adaptability to various applications make it a useful component for endowing robots with a certain degree of autonomy.

[0003] Currently, the SLAM technology uses a method of calculating probabilities to locate the position of a mobile robot and map the environment. Since this technology requires relatively accurate positions or orientations, once the mobile robot encounters some complex environments or environments with large changes, the mobile robot will not be able to reposition itself in the currently mapped SLAM map (i.e., the mobile robot gets lost). And the previously established map and positioning information by the mobile robot will all become invalid.

[0004] Therefore, a method and a mobile vehicle for repositioning a mobile vehicle in a SLAM map are needed to improve the above problems. Summary of the Invention

[0005] The following disclosure is only exemplary and is not intended to be limiting in any way. Other aspects, embodiments, and features will be apparent by reference to the drawings and the following detailed description. That is, the following disclosure is provided to introduce concepts, highlights, benefits, and novel and non-obvious technical advantages described herein. The selected, not all, embodiments will be described in further detail below. Therefore, the following disclosure is not intended to be a necessary feature of the claimed subject matter, nor is it intended to be used in determining the scope of the claimed subject matter.

[0006] Therefore, the main object of the present disclosure is to provide a method and a mobile vehicle for repositioning a mobile vehicle in a SLAM map to improve the above disadvantages.

[0007] The present disclosure provides a method for repositioning a moving vehicle in a real-time positioning and mapping (RTLS) map, which is used for the moving vehicle moving in an area, and includes: establishing, at an initial time point, the SLAM map corresponding to the area by using SLAM; detecting, by a non-SLAM positioning device, a first position trajectory and a first orientation trajectory of the moving vehicle on the SLAM map; detecting, by a SLAM positioning device, a probability of getting lost of the moving vehicle between a first timestamp and a second timestamp; determining whether a condition is satisfied; and when the condition is satisfied at a current time point, updating the SLAM map to a new SLAM map corresponding to the current time point and updating the positioning information of the moving vehicle in the new SLAM map; wherein the condition is one of the following: the position trajectory or the orientation trajectory is not between a first range and the probability of getting lost is not between a second range; and the probability of getting lost is not between the second range.

[0008] In some embodiments, the step of updating the SLAM map to the new SLAM map and updating the positioning information further includes: using the position and orientation of the moving vehicle detected by the non-SLAM positioning device at the current time point to update the SLAM map to the new SLAM map and update the positioning information.

[0009] In some embodiments, before updating the SLAM map to the new SLAM map and updating the positioning information, the method further includes: calculating, by the non-SLAM positioning device, a first trust value of the moving vehicle at the current time point; calculating, by the SLAM positioning device, a second trust value of the moving vehicle at the current time point; and when the first trust value or the second trust value is higher than a threshold, updating the SLAM map to the new SLAM map corresponding to the current time point and updating the positioning information of the moving vehicle in the new SLAM map.

[0010] In some embodiments, the first trust value and the second trust value are a mean function.

[0011] In some embodiments, the probability of getting lost is a second position trajectory, a second orientation trajectory of the moving vehicle moving between the first timestamp and the second timestamp, or a superposition difference of the SLAM map between the first timestamp and the second timestamp.

[0012] In some embodiments, the probability of getting lost is a mean function.

[0013] In some embodiments, the above SLAM positioning device updates the above SLAM map to the above new SLAM map in real time at the above current time point.

[0014] In some embodiments, the above non-SLAM positioning device updates the above SLAM map to the above new SLAM map at irregular intervals.

[0015] In some embodiments, the above first timestamp and the above second timestamp are consecutive timestamps.

[0016] In some embodiments, the above first timestamp and the above second timestamp are non-consecutive timestamps.

[0017] The present disclosure provides a mobile vehicle for moving in an area, including: a computing device that uses SLAM to establish the above SLAM map corresponding to the above area at an initial time point; a non-SLAM positioning device connected to the above computing device to detect a first position trajectory and a first orientation trajectory of the above mobile vehicle on the above SLAM map; and a SLAM positioning device connected to the above computing device to detect a lost probability of the above mobile vehicle between a first timestamp and a second timestamp: wherein the above computing device determines whether a condition is satisfied; and when the above condition is satisfied at a current time point, updates the above SLAM map to a new SLAM map corresponding to the above current time point and updates the positioning information of the above mobile vehicle in the above new SLAM map; wherein the above condition is one of the following: the above position trajectory or the above orientation trajectory is not between a first range and the above lost probability is not between a second range; and the above lost probability is not between the above second range. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 Schematically shows a mobile vehicle in the form of an unmanned vehicle equipped with a SLAM positioning device and a non-SLAM positioning device.

[0019] Figure 2 Is a SLAM map generated based on the data obtained by the SLAM positioning device of the mobile vehicle according to an embodiment of the present disclosure.

[0020] Figure 3 Is a flowchart showing a method for repositioning a mobile vehicle in a simultaneous localization and mapping (SLAM) map according to an embodiment of the present disclosure.

[0021] Figure 4 Is a schematic diagram showing the superposition of SLAM maps according to an embodiment of the present disclosure.

[0022] Figure 5 Is a diagram showing an exemplary operating environment for implementing the embodiments of the present disclosure.

[0023] The descriptions of the reference numerals are as follows:

[0024] 100: Mobile vehicle

[0025] 102: SLAM positioning device

[0026] 104: Non-SLAM positioning device

[0027] 110: Computing device

[0028] 112: Processor

[0029] 114: Memory

[0030] 1142: Program

[0031] 200: SLAM map

[0032] 300: Method flow chart

[0033] S305, S310, S315, S320, S325, S330: Steps

[0034] 410: SLAM map

[0035] 420: SLAM map

[0036] 500: Computing device

[0037] 510: Bus

[0038] 512: Memory

[0039] 514: Processor

[0040] 516: Display element

[0041] 518: I / O port

[0042] 520: I / O element

[0043] 522: Power supply Detailed implementation manners

[0044] Aspects of the present disclosure will be described more fully hereinafter with reference to the accompanying drawings. However, the present disclosure may be embodied in many different forms and should not be construed as limited to any specific structure or function presented throughout this disclosure. On the contrary, these aspects 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. Based on the teachings herein, those skilled in the art should appreciate that the scope of the present disclosure is intended to cover any aspect disclosed herein, whether implemented alone or in combination with any other aspect of the present disclosure. For example, any number of the devices or methods presented herein may be used to implement it. Additionally, beyond the aspects of the present disclosure presented herein, the scope of the present disclosure is more intended to cover devices or methods implemented using other structures, functions, or combinations of structures and functions. It should be understood that any aspect disclosed herein may be embodied by one or more elements of the claims.

[0045] The term "exemplary" is used herein to mean "serving as an example, instance, or illustration". Any aspect of the present disclosure or a design described herein as "exemplary" is not necessarily to be construed as preferred or superior to other aspects of the present disclosure or designs. Additionally, the same numerals indicate the same elements in all of the several figures, and unless otherwise specified in the description, the articles "a" and "the" include plural references.

[0046] It will be understood that when an element is referred to as being "connected" or "coupled" to another element, the element can be directly connected or coupled to the other element or intervening elements may be present. In contrast, when the element is referred to as being "directly connected" or "directly coupled" to another element, there are no intervening elements. Other words used to describe the relationship between elements should be interpreted in a like manner (e.g., "between" versus "directly between", "adjacent" versus "directly adjacent", etc.).

[0047] Embodiments of the present disclosure provide a method and a mobile vehicle for relocating a mobile vehicle in a Simultaneous Localization and Mapping (SLAM) map, by adding another accurate non-SLAM positioning device to further solve the problem of positioning failure using a SLAM positioning device.

[0048] Figure 1Schematically shown is a mobile vehicle 100 in the form of an unmanned vehicle device equipped with a SLAM positioning device 102 and a non-SLAM positioning device 104. In the illustrated embodiment, the SLAM positioning device 102 can be any suitable ranging sensor capable of generating an accurate map (such as a laser (LiDAR), a laser scanner, or a sonar device). The non-SLAM positioning device 104 can be any radio frequency (RF)-based positioning device (such as an ultra-wideband (UWB), Bluetooth, LoRa, WiFi, etc. device). The mobile vehicle 100 further includes a computing device 110 having a processor 112 and a memory 114 that can store a program 1142.

[0049] The computing device 110 is a device that can support various wireless access technologies, such as a mobile phone, a laptop computer, a smartphone, or a tablet computer, etc. The computing device 110 communicates with the SLAM positioning device 102 and the non-SLAM positioning device 104 (e.g., via a wireless communication interface), and is capable of storing and processing data related to the use of signals received by the SLAM positioning device 102 and the non-SLAM positioning device 104.

[0050] The memory 114 can store a SLAM map corresponding to an area established by the processor 112 using SLAM. The computing device 110 can further include other conventional features, such as a user interface and a communication interface that allow it to exchange data with a remote device. In an alternative embodiment, signals from at least one sensor can be transmitted to a remote computing device for processing instead of being processed by the computing device 110.

[0051] In use, the mobile vehicle 100 travels along a road surface in an area, and data related to the scene around the mobile vehicle 100 is captured by the SLAM positioning device 102 and the non-SLAM positioning device 104. Although the exemplary mobile vehicle 100 is an unmanned vehicle traveling along a road / ground, it should be understood that in an alternative embodiment, the mobile vehicle 100 can be any type of device that can travel above a scanable ground (and does not have to be in contact with the ground). Additionally, in other embodiments, the SLAM positioning device 102, the non-SLAM positioning device 104, and the computing device 110 do not need to be installed on the mobile vehicle 100, but can be included in, for example, a handheld navigation device.

[0052] The processor 112 is configured to process data received from the SLAM positioning device 102 and the non-SLAM positioning device 104 to attempt to locate the mobile vehicle 100 from the existing map data. The processor 112 may use the data obtained from the SLAM positioning device 102 to generate a SLAM map when the mobile vehicle 100 is running, as Figure 2 shown. Figure 2 FIG. Figure 2 is a SLAM map 200 showing the mobile vehicle 100 generating according to the data obtained by the SLAM positioning device 102 according to an embodiment of the present disclosure. As shown, the SLAM map 200 is at least composed of three data -1, 0, 1, where -1 represents an unknown area (e.g., Figure 2 the gray area in FIG. Figure 2 ), 0 represents a known area (e.g., Figure 2 the black area, wall or obstacle in FIG. Figure 2 ), and 1 represents a blank area (e.g., Figure 2 the white area or open space in FIG. Figure 2 ).

[0053] It should be understood that Figure 1 the computing device 110 shown can be implemented by any type of computing device, such as the computing device 500 described with reference to Figure 5 FIG. Figure 5 , as Figure 5 shown.

[0054] Figure 3 FIG. Figure 3 is a flowchart 300 of a method for relocating a mobile vehicle in a simultaneous localization and mapping (SLAM) map according to an embodiment of the present disclosure. This method can be executed in the mobile vehicle 100 moving in an area as Figure 1 shown.

[0055] In step S305, the mobile vehicle uses SLAM to establish the above SLAM map corresponding to the above area at an initial time point.

[0056] In step S310, the mobile vehicle detects a first position trajectory and a first orientation trajectory of the mobile vehicle on the SLAM map through a non-SLAM positioning device. More specifically, the non-SLAM positioning device can record the position and orientation of the mobile vehicle on the SLAM map in real time. All positions can be represented as a position set rf.p = { {timestamp1, position1}, {timestamp2, position2},...}, and all orientations can be represented as an orientation set rf.a = { {timestamp1, azimuth1}, {timestamp2, azimuth2},...}. The first position trajectory is a trajectory formed by two or more consecutive positions in the position set, and the first orientation trajectory is a trajectory formed by two or more consecutive orientations in the orientation set.

[0057] In step S315, the mobile vehicle detects a probability of getting lost between a first timestamp and a second timestamp through a SLAM positioning device, where the probability of getting lost is a second position trajectory, a second orientation trajectory that the mobile vehicle moves between the first timestamp and the second timestamp, or a superposition difference of the SLAM map between the first timestamp and the second timestamp.

[0058] More specifically, the SLAM positioning device can record the position and orientation of the mobile vehicle on the SLAM map in real time. All positions can be represented as a position set opt.p = { {timestamp1, position1}, {timestamp2, position2},...}, and all orientations can be represented as an orientation set opt.a = { {timestamp1, azimuth1}, {timestamp2, azimuth2},...}. The second position trajectory is a trajectory formed by two or any two consecutive positions in the position set, and the first orientation trajectory is a trajectory formed by two or any two consecutive orientations in the orientation set. In addition, a superposition difference of the SLAM map between the first timestamp and the second timestamp is a difference probability value of the SLAM map between the first timestamp and the second timestamp. For example, Figure 4 FIG. is a schematic diagram showing the superposition of the SLAM map according to an embodiment of the present disclosure. The SLAM map 410 is the SLAM map generated at the timestamp t0, and the SLAM map 420 is the SLAM map generated at the timestamp ti. The SLAM positioning device can calculate the probability value of the SLAM map 410 and the SLAM map 420 as a probability of getting lost of the mobile vehicle between the timestamp t0 and the timestamp ti.

[0059] In Figure 3In S315, the process of the mobile vehicle detecting the probability of getting lost between the first timestamp and the second timestamp through the SLAM positioning device can be represented by the following Python code. The input parameters include at least the SLAM map map_t-1 at the previous time t-1, the ranging sensor LiDAR input (i.e., the distance values between the mobile vehicle and the surrounding environment), the position opt_p_t(x_t, y_t) and orientation opt_a_t(a_t) of the mobile vehicle at time t. The text to the right of "#" represents the annotation in the program.

[0060]

[0061]

[0062] In another embodiment, the above probability of getting lost is a mean function, such as mean(p).

[0063] In addition, it should be noted that steps S310 and S315 are respectively and simultaneously executed by the non-SLAM positioning device and the SLAM positioning device.

[0064] Next, in step S320, the processor of the mobile vehicle determines whether a condition is satisfied. The above condition is one of the following: (1) the above position trajectory or the above orientation trajectory is not between a first range and the above probability of getting lost is not between a second range; and (2) the above probability of getting lost is not between the above second range.

[0065] When the above conditions are met at a current time point (i.e., "Yes" in step S320), in step S325, the processor of this mobile vehicle updates the above SLAM map to a new SLAM map corresponding to the current time point and updates the positioning information (e.g., position and orientation) of the mobile vehicle in the new SLAM map. More specifically, the processor of this mobile vehicle uses the position and orientation of the mobile vehicle detected by the non-SLAM positioning device at the current time point to update the SLAM map to the new SLAM map and update the positioning information. For example, the SLAM positioning device of the mobile vehicle detects that the position and orientation of the mobile vehicle in the SLAM map at the current time point i are opt_p_i(x_i, y_i) and opt_a_i(a_i) respectively, while the non-SLAM positioning device of the mobile vehicle detects that the position and orientation of the mobile vehicle in the SLAM map at the current time point i are rf_p_i(x_i, y_i) and rf_a_i(a_i) respectively. When the above conditions are met at the current time point i, the processor of the mobile vehicle updates the position and orientation of the mobile vehicle detected by the SLAM positioning device in the SLAM map from opt_p_i(x_i, y_i) and opt_a_i(a_i) to rf_p_i(x_i, y_i) and rf_a_i(a_i). The SLAM positioning device then updates the SLAM map to a new SLAM map corresponding to the current time point i with the position and orientation of the mobile vehicle in the SLAM map being rf_p_i(x_i, y_i) and rf_a_i(a_i). After the SLAM positioning device updates the old SLAM map to a new SLAM map, the non-SLAM positioning device will update the old SLAM map to a new SLAM map irregularly.

[0066] In another embodiment, in step S325, before updating the SLAM map to the new SLAM map and updating the positioning information, the mobile vehicle can also calculate a first trust value of the mobile vehicle at the current time point through the non-SLAM positioning device, and calculate a second trust value of the mobile vehicle at the current time point through the SLAM positioning device. The trust value indicates whether the result of repositioning is trustworthy.

[0067] When the first trust value or the second trust value is higher than a threshold, the mobile vehicle updates the SLAM map to the new SLAM map corresponding to the current time point and updates the positioning information of the mobile vehicle in the new SLAM map. When the first trust value or the second trust value is not higher than the threshold, the mobile vehicle abandons updating the SLAM map and the positioning information of the mobile vehicle.

[0068] More specifically, the above-mentioned first trust value and the above-mentioned second trust value can be represented by the following Python code, where the input parameters include at least the position opt_p_t(x_t, y_t) and orientation opt_a_t(a_t) of the mobile vehicle obtained by the SLAM positioning device at time t on the SLAM map, or the position rf_p_t(x_t, y_t) and orientation rf_a_t(a_t) of the mobile vehicle obtained by the non-SLAM positioning device at time t on the SLAM map. The text to the right of "#" represents the annotation in the program.

[0069]

[0070] In another embodiment, the above-mentioned first trust value and second trust value are mean functions, such as mean(p).

[0071] In addition, the processor 112 in the mobile vehicle 100 can also be integrated with the SLAM positioning device 102 and the non-SLAM positioning device 104. The processor 112 in the mobile vehicle 100 can also execute the program 1142 in the memory 114 to present the actions and steps described in the above embodiments, or other descriptions in the specification.

[0072] Therefore, through the method and mobile vehicle for relocating a mobile vehicle in a SLAM map according to the present disclosure, the SLAM positioning device of the mobile vehicle can re-position itself on the currently mapped SLAM map by using the position and orientation provided by another accurate non-SLAM positioning device, so as to improve the positioning accuracy in the SLAM map.

[0073] For the embodiments of the present invention that have been described, the following describes an exemplary operating environment in which the embodiments of the present invention can be implemented. Specifically refer to Figure 5 , Figure 5 which shows an exemplary operating environment for implementing the embodiments of the present invention, generally regarded as a computing device 500. The computing device 500 is only an example of a suitable computing environment and does not intend to imply any limitation on the use or functional scope of the present invention. The computing device 500 should not be construed as having any dependency or requirement related to any one or combination of the elements shown.

[0074] The present invention can be implemented using computer program code or machine-usable instructions. The instructions can be computer-executable instructions for program modules, which are executed by a computer or other machine, such as a personal digital assistant or other portable device. Generally, program modules include routines, programs, objects, components, data structures, etc. Program modules refer to program code that performs specific tasks or implements specific abstract data types. The present invention can be implemented in various system configurations, including portable devices, consumer electronics, general-purpose computers, more specialized computing devices, etc. The present invention can also be implemented in a distributed computing environment, processing devices connected by a communication network.

[0075] Reference Figure 5 。The computing device 500 includes a bus 510 that directly or indirectly couples the following devices: a memory 512, one or more processors 514, one or more display elements 516, an input / output (I / O) port 518, an input / output (I / O) element 520, and an illustrative power supply 522. The bus 510 represents an element (e.g., an address bus, a data bus, or a combination thereof) that can be one or more buses. Although Figure 5 each block of is shown briefly as a line for simplicity, in reality, the demarcation of each element is not specific. For example, the rendering element of a display device can be regarded as an I / O element; a processor can have a memory.

[0076] The computing device 500 generally includes various computer-readable media. The computer-readable media can be any available media accessible by the computing device 500, which includes both volatile and non-volatile media, removable and non-removable media. By way of example and not limitation, the computer-readable media can include computer storage media and communication media. The computer-readable media also includes volatile and non-volatile media, removable and non-removable media implemented in any method or technology for storing information such as computer-readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, random access memory (RAM), read-only memory (ROM), electrically-erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical disc storage devices, magnetic disks, magnetic tapes, magnetic disk storage devices or other magnetic storage devices, or any other media that can be used to store the required information and accessible by the computing device 500. Computer storage media does not include signals per se.

[0077] Communication media generally includes computer-readable instructions, data structures, program modules, or other data in the form of a modulated data signal such as a carrier wave or other transmission mechanism, and includes any information delivery media. The term "modulated data signal" refers to a signal having one or more characteristics sets or a signal that has been altered in a manner that encodes information in the signal. By way of example and not limitation, communication media includes wired media such as a wired network or direct wired connection, and wireless media such as audio, radio frequency, infrared, and other wireless media. Combinations of the above media are included within the scope of computer-readable media.

[0078] The memory 512 includes computer storage media in the form of volatile and non-volatile memory. The memory can be removable, non-removable, or a combination of both. Exemplary hardware devices include solid-state memory, hard disk drives, optical disk drives, etc. The computing device 500 includes one or more processors that read data from entities such as the memory 512 or the I / O element 520. The display element 516 displays data indications to the user or other devices. Exemplary display elements include display devices, speakers, printing elements, vibrating elements, etc.

[0079] The I / O port 518 allows the computing device 500 to logically connect to other devices including I / O components 520, some of which are built-in devices. Exemplary components include microphones, rockers, game tables, satellite dish signal receivers, scanners, printers, wireless devices, etc. The I / O component 520 can provide a natural user interface for processing user-generated gestures, sounds, or other physiological inputs. In some examples, these inputs can be transmitted to a suitable network component for further processing. The computing device 500 can be equipped with a depth camera, such as a stereo camera system, an infrared camera system, an RGB camera system, and combinations of these systems, to detect and identify objects. In addition, the computing device 500 can be equipped with sensors (e.g., radar, lidar) to periodically sense the surrounding environment within a sensing range, generating sensor information representing its association with the surrounding environment. Furthermore, the computing device 500 can be equipped with an accelerometer or gyroscope for detecting motion. The output of the accelerometer or gyroscope can be provided to the display of the computing device 500.

[0080] In addition, the processor 514 in the computing device 500 can also execute the programs and instructions in the memory 512 to perform the actions and steps described in the above embodiments, or other descriptions in the content of the specification.

[0081] Any specific order or hierarchical steps of the programs disclosed herein are purely for illustrative purposes. Based on design preferences, it must be understood that any specific order or hierarchical steps of the programs can be rearranged within the scope disclosed in this document. The accompanying method claims present the elements of various steps in an exemplary order and should not, therefore, be limited by the specific order or hierarchy shown.

[0082] The use of ordinal numbers such as "first", "second", "third", etc. to modify elements in the claims does not itself imply any priority, precedence, order among the elements, or order of the steps performed by the method, but is only used as a label to distinguish different elements with the same name (with different ordinal numbers).

[0083] Although the present disclosure has been disclosed above by way of embodiments, it is not intended to limit the present disclosure. Any person skilled in the art can make some changes and modifications without departing from the concept and scope of the present disclosure. Therefore, the protection scope of the present disclosure shall be determined by the scope defined by the claims.

Claims

1. A method for relocating a moving vehicle in a map of real-time positioning and mapping, for the moving vehicle moving in an area, comprising: At an initial time point, use real-time positioning and mapping technology to establish a map of the real-time positioning and mapping corresponding to the area; Detect a first position trajectory and a first orientation trajectory of the moving vehicle on the map of the real-time positioning and mapping through a positioning device of non-real-time positioning and mapping; Detect a lost probability of the moving vehicle between a first time stamp and a second time stamp through a positioning device of real-time positioning and mapping, where the lost probability is a second position trajectory, a second orientation trajectory that the moving vehicle moves between the first time stamp and the second time stamp, or a superposition difference of the map of the real-time positioning and mapping between the first time stamp and the second time stamp; Judge whether a condition is satisfied; And When the above condition is satisfied at a current time point, update the map of the real-time positioning and mapping to a new map of the real-time positioning and mapping and update the positioning information with a position and an orientation of the moving vehicle detected by the positioning device of non-real-time positioning and mapping at the current time point; Where the above condition is that the first position trajectory or the first orientation trajectory is not between a first range and the lost probability is not between a second range.

2. The method for relocating a moving vehicle in a map of real-time positioning and mapping according to claim 1, wherein before updating the map of real-time positioning and mapping to the new map of real-time positioning and mapping and updating the positioning information, the method further comprises: Calculate a first trust value of the moving vehicle at the current time point through the positioning device of non-real-time positioning and mapping; Calculate a second trust value of the moving vehicle at the current time point through the positioning device of real-time positioning and mapping; And When the first trust value or the second trust value is higher than a threshold, update the map of the real-time positioning and mapping to the new map of the real-time positioning and mapping corresponding to the current time point and update the positioning information of the moving vehicle in the new map of the real-time positioning and mapping.

3. The method for relocating a moving vehicle in a map of real-time positioning and mapping according to claim 2, wherein the first trust value and the second trust value are a mean function.

4. The method for relocating a moving vehicle in a map of real-time positioning and mapping according to claim 1, wherein the probability of being lost is a mean function.

5. The method for relocating a moving vehicle in a map of real-time positioning and mapping according to claim 1, wherein the positioning device of real-time positioning and mapping updates the map of real-time positioning and mapping to the new map of real-time positioning and mapping in real time at the current time point.

6. The method for relocating a moving vehicle in a map of real-time positioning and mapping according to claim 1, wherein the positioning device of non-real-time positioning and mapping updates the map of real-time positioning and mapping to the new map of real-time positioning and mapping irregularly.

7. The method for relocating a moving vehicle in a map of real-time positioning and mapping according to claim 1, wherein the first timestamp and the second timestamp are consecutive timestamps.

8. The method for relocating a moving vehicle in a map of real-time positioning and mapping according to claim 1, wherein the first timestamp and the second timestamp are non-consecutive timestamps.

9. A moving vehicle for moving in an area, comprising: A computing device uses real-time positioning and mapping technology to establish a map of the real-time positioning and mapping corresponding to the area at an initial time point; A positioning device of non-real-time positioning and mapping, connected to the computing device, detects a first position trajectory and a first orientation trajectory of the moving vehicle on the map of the real-time positioning and mapping; And A positioning device of real-time positioning and mapping, connected to the computing device, detects a lost probability of the moving vehicle between a first time stamp and a second time stamp, where the lost probability is a second position trajectory, a second orientation trajectory that the moving vehicle moves between the first time stamp and the second time stamp, or a superposition difference of the map of the real-time positioning and mapping between the first time stamp and the second time stamp: Where the computing device judges whether a condition is satisfied; and when the above condition is satisfied at a current time point, update the map of the real-time positioning and mapping to a new map of the real-time positioning and mapping and update the positioning information with a position and an orientation of the moving vehicle detected by the positioning device of non-real-time positioning and mapping at the current time point; Wherein the above conditions are that the first position locus or the first orientation locus is not between a first range and the probability of loss is not between a second range.

10. The mobile vehicle according to claim 9, wherein before updating the map of the real-time positioning and mapping to the map of the new real-time positioning and mapping and updating the positioning information, the mobile vehicle further performs: The positioning device of the non-real-time positioning and mapping calculates a first trust value of the mobile vehicle at the current time point; The positioning device of the real-time positioning and mapping calculates a second trust value of the mobile vehicle at the current time point; and When the first trust value or the second trust value is higher than a threshold, the computing device updates the map of the real-time positioning and mapping to the map of the new real-time positioning and mapping corresponding to the current time point and updates the positioning information of the mobile vehicle in the map of the new real-time positioning and mapping.

11. The mobile vehicle according to claim 10, wherein the first trust value and the second trust value are a mean function.

12. The mobile vehicle according to claim 9, wherein the probability of getting lost is a mean function.

13. The mobile vehicle according to claim 9, wherein the positioning device of the real-time positioning and mapping updates the map of the real-time positioning and mapping to the map of the new real-time positioning and mapping in real time at the current time point.

14. The mobile vehicle according to claim 9, wherein the positioning device of the non-real-time positioning and mapping updates the map of the real-time positioning and mapping to the map of the new real-time positioning and mapping at irregular intervals.

15. The mobile vehicle according to claim 9, wherein the first time stamp and the second time stamp are consecutive time stamps.

16. The mobile vehicle according to claim 9, wherein the first time stamp and the second time stamp are non-consecutive time stamps.

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