A robot scanning method, device, equipment and storage medium

Abstract

The embodiments of the present application provide a robot scanning method, device, equipment and storage medium. In the robot scanning method, the map established by the robot in the target space at the current moment can be obtained, and the map can be compared with the map established at the historical moment to obtain the similarity comparison result between the two maps. If the similarity comparison result does not meet the set similarity condition, a distinguishing mark is added to the target space so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the map established at the historical moment meets the similarity condition. Through this implementation method, the difference between the map established by the robot in the target space and the map established at the historical moment is improved, thereby reducing the possibility of positioning loss during the scanning process.

Description

Technical Field

[0001] The embodiments of the present application relate to the field of robotics technology, and in particular to a robot scanning method, device, equipment and storage medium. Background Art

[0002] As the intelligence of robots continues to improve, robot scanning technology is also developing. In the prior art, robots can usually achieve intelligent scanning based on visual perception, radar ranging and other technologies. In the robot's scanning process, there are usually areas to be scanned with similar features. The mapping results of the areas to be scanned with similar features are similar, which can easily cause the robot to lose its position during the subsequent walking process. Therefore, a scanning method is urgently needed. Summary of the invention

[0003] The embodiments of the present application provide a robot scanning method, device, equipment and storage medium to improve the difference of scanning results of different areas and reduce the possibility of positioning loss.

[0004] An embodiment of the present application provides a robot scanning method, which is applied to a robot, and the method includes: obtaining a first map established by the robot in a target space at a current moment; comparing the first map with a second map established at a historical moment to obtain a similarity comparison result between the first map and the second map; if the similarity comparison result does not meet a set similarity condition, adding a distinguishing mark in the target space so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the second map meets the similarity condition.

[0005] Further optionally, the first map is compared with a second map established at a historical moment to obtain a similarity comparison result between the first map and the second map, including: obtaining first data corresponding to the first sensor in the first map and second data corresponding to the first sensor in the second map; if the similarity between the first data and the second data is less than a preset first threshold, using the second sensor to obtain third data in the target space; the second sensor is of a different type from the first sensor; and calculating the similarity between the third data and fourth data corresponding to the second sensor in the second map as the similarity comparison result between the first map and the second map.

[0006] Further optionally, the first sensor includes: a lidar sensor; the second sensor includes: at least one of a visual sensor, a magnetic sensor, an ultrasonic sensor, a network signal sensor, and a sound signal sensor.

[0007] Further optionally, adding a distinguishing mark to the target space so that a similarity comparison result between the map of the target space after the distinguishing mark is added and the second map meets the similarity condition includes: adding a distinguishing mark to the target space; performing a mapping operation on the target space after the distinguishing mark is added to obtain a map of the target space after the distinguishing mark is added; calculating the similarity between the map of the target space after the distinguishing mark is added and the second map; if the similarity between the map of the target space after the distinguishing mark is added and the second map does not meet the similarity condition, repeating the operation of adding the distinguishing mark.

[0008] Further optionally, adding a distinguishing mark in the target space includes: inserting a magnetic marker into the building plane of the target space so that the similarity of magnetic induction data between the marked map of the target space and the second map is greater than a corresponding magnetic induction similarity threshold.

[0009] Further optionally, adding a distinguishing mark in the target space includes: drawing an identification pattern on a building plane of the target space so that the similarity of visual perception data between the marked map of the target space and the second map is greater than a corresponding visual similarity threshold.

[0010] Further optionally, adding a distinguishing mark in the target space includes: placing a reference object in the target space so that the similarity of radar point cloud data between the marked map of the target space and the second map is greater than a corresponding point cloud similarity threshold.

[0011] The embodiment of the present application also provides a robot scanning device, including: an acquisition module, used to: acquire a first map established by the robot in the target space at the current moment; a comparison module, used to: compare the first map with a second map established at a historical moment to obtain a similarity comparison result between the first map and the second map; a marking module: if the similarity comparison result does not meet the set similarity condition, then add a distinguishing mark in the target space, so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the second map meets the similarity condition.

[0012] An embodiment of the present application also provides a robot device, comprising: a memory and a processor; wherein the memory is used to: store one or more computer instructions; the processor is used to execute the one or more computer instructions, so as to: execute the steps in the robot scanning method.

[0013] The embodiment of the present application also provides a computer-readable storage medium storing a computer program. When the computer program is executed by a processor, the processor implements the steps in the robot image scanning method described in the embodiment of the present application.

[0014] The embodiments of the present application provide a robot scanning method, device, equipment and storage medium, which can obtain the map established by the robot in the target space at the current moment, and compare the map with the map established at the historical moment to obtain the similarity comparison result between the two maps. If the similarity comparison result does not meet the set similarity condition, a distinguishing mark is added to the target space so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the map established at the historical moment meets the similarity condition. Through this implementation method, the difference between the map established by the robot in the target space and the map established at the historical moment is improved, thereby reducing the possibility of positioning loss during the scanning process. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0016] Figure 1 A schematic diagram of a flow chart of a robot scanning method provided by an exemplary embodiment of the present application;

[0017] Figure 2 A schematic diagram of a robot image scanning device provided by an exemplary embodiment of the present application;

[0018] Figure 3 A schematic structural diagram of a robot device provided as an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] In the prior art, during the scanning process of the robot, there are usually areas to be scanned with relatively similar features. The mapping results of the areas to be scanned with relatively similar features are similar, which can easily cause the robot to lose its positioning during the subsequent walking process. In response to the above technical problems, a solution is provided in some embodiments of the present application. The technical solutions provided in each embodiment of the present application will be described in detail below in conjunction with the accompanying drawings.

[0021] Figure 1 A schematic diagram of a flow chart of a robot scanning method provided by an exemplary embodiment of the present application, such as Figure 1 As shown, the method includes:

[0022] Step 11: Obtain the first map established by the robot in the target space at the current moment.

[0023] Step 12: Compare the first map with the second map established at a historical moment to obtain a similarity comparison result between the first map and the second map.

[0024] Step 13: If the similarity comparison result does not meet the set similarity condition, a distinguishing mark is added to the target space so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the second map meets the similarity condition.

[0025] The execution subject of this embodiment is a robot device. The robot device is equipped with a variety of sensors, and the robot device can scan the mapping data based on the various sensors and generate a map according to the mapping data.

[0026] In this embodiment, the first map established by the robot in the target space at the current moment can be obtained, and the first map can be compared with the second map established at the historical moment to obtain the similarity comparison result between the first map and the second map. The target space can be any space in the real world, such as a library, restaurant, hotel, residence, etc. The first map refers to the map established by the robot in the target space at the current moment. The second map refers to the map established at the historical moment. The historical moment can include any moment before the current moment. For example, at time T -2 , T -1 and time T 0 In, T 0 is the current moment, T -2 and T -1 For historical moments.

[0027] After obtaining the similarity comparison result, if the similarity comparison result does not meet the set similarity condition, a distinguishing mark is added in the target space so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the second map meets the similarity condition. For example, if the set similarity condition is less than 20%, and the similarity comparison result is 90%, which does not meet the similarity condition, it means that the first map established by the robot in the target space at the current moment is closer to the second map at the historical moment. At this time, a distinguishing mark can be added in the target space to reduce the similarity between the first map and the second map, so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the second map meets the similarity condition.

[0028] In this embodiment, the map created by the robot in the target space at the current moment can be obtained, and the map can be compared with the map created at the historical moment to obtain the similarity comparison result between the two maps. If the similarity comparison result does not meet the set similarity condition, a distinguishing mark is added to the target space so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the map created at the historical moment meets the similarity condition. Through this implementation method, the difference between the map created by the robot in the target space and the map created at the historical moment is improved, thereby reducing the possibility of the robot losing its position in the process of moving using the map.

[0029] In some optional embodiments, comparing the first map with the second map established at a historical moment to obtain a similarity comparison result between the first map and the second map can be achieved based on the following steps:

[0030] Step S1, obtaining first data corresponding to a first sensor in a first map and second data corresponding to a first sensor in a second map.

[0031] The first sensor includes: a laser radar sensor. The first data refers to the data corresponding to the first sensor in the first map, that is, the laser radar data corresponding to the first map; the second data refers to the data corresponding to the first sensor in the second map, that is, the laser radar data corresponding to the second map.

[0032] Step S2: If the similarity between the first data and the second data is greater than a preset first threshold, a second sensor is used to acquire third data in the target space; wherein the second sensor is of a different type from the first sensor.

[0033] Among them, when the similarity between the first data and the second data is greater than the first threshold, it means that the lidar data in the first map and the second map are relatively similar, which may easily lead to the loss of robot positioning. At this time, the second sensor can be used to obtain the third data in the target space. The second sensor includes: one or more of a visual sensor, a magnetic sensor, an ultrasonic sensor, a network signal sensor, and a sound signal sensor. Among them, the third data refers to the data corresponding to the second sensor in the first map, such as the visual data, magnetic data, etc. corresponding to the first map. Among them, the first threshold can be 80%, 85% or 90%, etc., which is not limited in this embodiment.

[0034] Step S3: Calculate the similarity between the third data and the fourth data corresponding to the second sensor in the second map as the similarity comparison result between the first map and the second map. The fourth data refers to the data corresponding to the second sensor in the second map.

[0035] Taking the second sensor as a visual sensor as an example, the similarity between the visual data corresponding to the visual sensor in the first map and the visual data corresponding to the visual sensor in the second map established at a historical moment can be calculated as the similarity comparison result between the first map and the second map. In this way, the robot can perceive the visual difference between the map at the current moment and the map at the historical moment through the visual sensor.

[0036] Taking the second sensor as a magnetic sensor as an example, the similarity between the magnetic data corresponding to the magnetic sensor in the first map and the magnetic data corresponding to the magnetic sensor in the second map established at a historical moment can be calculated as the similarity comparison result between the first map and the second map. In this way, the robot can perceive the difference in magnetism between the map at the current moment and the map at the historical moment through the magnetic sensor.

[0037] Taking the second sensor as an ultrasonic sensor as an example, the similarity between the ultrasonic data corresponding to the ultrasonic sensor in the first map and the ultrasonic data corresponding to the ultrasonic sensor in the second map established at a historical moment can be calculated as the similarity comparison result between the first map and the second map. In this way, the robot can perceive the difference in ultrasonic ranging between the current map and the historical map through the ultrasonic sensor.

[0038] Taking the second sensor as a network signal sensor as an example, the similarity between the network data corresponding to the network signal sensor in the first map and the network data corresponding to the network signal sensor in the second map established by the network signal sensor at a historical moment can be calculated as the similarity comparison result between the first map and the second map. In this way, the robot can perceive the difference in network signals between the current map and the historical map through the network signal sensor.

[0039] Taking the second sensor as a sound signal sensor as an example, the similarity between the sound sensing data corresponding to the sound signal sensor in the first map and the sound sensing data corresponding to the sound signal sensor in the second map established by the sound signal sensor at a historical moment can be calculated as the similarity comparison result between the first map and the second map. In this way, the robot can perceive the difference in sound signals between the current map and the map at a historical moment through the sound signal sensor.

[0040] In this embodiment, based on the data corresponding to the first sensor in the first map and the second map, the similarity between the first map and the second map is calculated. If the similarity is greater than a threshold, the second sensor is used to obtain the third data in the target space. In this way, it is not necessary to turn on all sensors during the scanning process, but to turn on the sensors according to the actual needs of the robot. Through this implementation, the power consumption of the robot is further reduced.

[0041] In some optional embodiments, adding a distinguishing mark to the target space so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the second map satisfies the similarity condition can be achieved based on the following steps:

[0042] S1. Add distinguishing marks in the target space.

[0043] Based on this step, the similarity between the map of the target space and the second map can be reduced. For example, when the robot passes through a long tunnel, it moves along the preset track in the tunnel. At this time, the scenes in various places in the tunnel are very close, and the robot cannot determine its position in the tunnel. To avoid this situation, when the robot is mapping the tunnel, the robot can add distinguishing marks in different areas of the tunnel to enhance the differences between various places in the tunnel, thereby avoiding positioning loss.

[0044] S2. After adding the distinguishing mark, a mapping operation may be performed on the target space after adding the distinguishing mark to obtain a map of the target space after adding the distinguishing mark.

[0045] S3. Calculate the similarity between the map of the target space after adding the distinguishing mark and the second map.

[0046] S4. Determine whether the similarity between the map of the target space after adding the distinguishing mark and the second map meets the similarity condition; if the similarity condition is met, continue to perform the map scanning task. If the similarity between the map of the target space after adding the distinguishing mark and the second map does not meet the similarity condition, it means that after adding the distinguishing mark, the difference between the two maps is still small, and the robot still has a greater possibility of positioning loss. Therefore, the robot can repeat the operation of adding the distinguishing mark in step S1 to continue to improve the difference between the two maps.

[0047] For example, after adding distinguishing mark 1 to the target space, if the similarity between the map of the target space and the second map after adding the distinguishing mark still does not meet the similarity condition, distinguishing mark 2 can be added to continue to reduce the similarity between the map of the target space and the second map. If the similarity condition is still not met, distinguishing mark 3 can be added to the target space. If the similarity between the map of the target space and the second map meets the similarity condition after adding distinguishing mark 3, adding distinguishing marks can be stopped.

[0048] Through this implementation, distinguishing marks are continuously added until the similarity between the two maps meets the conditions, thereby avoiding the situation of excessively adding distinguishing marks at one time and reducing the changes to the original scene of the target space.

[0049] Optionally, the robot may add a distinguishing mark in the target space based on the following implementation to increase the difference between the map of the target space and the second map.

[0050] Embodiment 1: inserting a magnetic marker into the building plane of the target space. Then, the similarity of the magnetic induction data between the marked map of the target space and the second map can be greater than the corresponding magnetic induction similarity threshold.

[0051] The building plane may include: a wall, a ground or a roof of the target space, etc. The magnetic marker may include a magnetic object, such as a magnet or a magnetic tile, etc. The magnetic sensing data is collected by a magnetic sensor on the robot. By inserting the magnetic marker in this way, the difference in magnetic field between the map of the target space and the second map can be increased.

[0052] Embodiment 2: Draw a marking pattern on the building plane of the target space. Then, the similarity of visual perception data between the marked map of the target space and the second map can be greater than the corresponding visual similarity threshold.

[0053] The identification pattern may include a two-dimensional code, a three-dimensional code or other patterns, etc. The visual perception data is collected by a visual sensor. By drawing the identification pattern in this way, the visual difference between the map of the target space and the second map can be increased.

[0054] Embodiment 3: Placing a reference object in the target space. Then, the similarity of the laser radar point cloud data and / or the ranging data between the marked map of the target space and the second map can be greater than a corresponding similarity threshold.

[0055] The laser radar point cloud data is collected by a laser radar sensor. The ranging data can be obtained by an ultrasonic sensor or an infrared sensor, which is not limited in this embodiment. By placing a reference object in this way, the difference in object perception data between the map of the target space and the second map can be increased.

[0056] It should be noted that the above-mentioned implementation modes can be executed in combination or individually, which is not limited in this embodiment.

[0057] The following will further explain the robot scanning method in combination with actual application scenarios.

[0058] In actual application scenarios, the robot can be equipped with multiple sensors such as lidar sensors, visual sensors, force sensors, ultrasonic sensors, and network signal sensors. The robot can first turn on sensors such as lidar to map the target space. The robot builds map 001 from the starting position and continues to build map 002. Based on this, the robot can compare the differences between map 001 and map 002. The differences include but are not limited to the following aspects: color, distance between objects, network signals, magnetic fields, and surrounding sounds. If the differences in all aspects exceed multiple preset thresholds, the scanning and mapping will continue.

[0059] If the difference in any aspect does not exceed the threshold, the robot turns on all sensors and repeats the above difference comparison process.

[0060] After all sensors are turned on, map 001 and map 002 are compared. If the difference in any aspect still does not exceed the threshold, then at the position of map 002, the robot can insert a magnet into the ground or draw a color mark on the wall until the difference between map 001 and map 002 exceeds the threshold.

[0061] Through this implementation, the difference between the map established by the robot in the target space and the map established at the historical moment is improved, thereby reducing the possibility of positioning loss during the scanning process.

[0062] It should be noted that the execution subject of each step of the method provided in the above embodiment can be the same device, or the method can be executed by different devices. For example, the execution subject of steps 11 to 13 can be device A; for another example, the execution subject of steps 11 to 12 can be device A, and the execution subject of step 13 can be device B; and so on.

[0063] In addition, in some of the processes described in the above embodiments and the accompanying drawings, multiple operations appearing in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this document or may be executed in parallel, and the sequence numbers of the operations, such as 11, 12, etc., are only used to distinguish between different operations, and the sequence numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel.

[0064] It should be noted that the descriptions such as “first” and “second” in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit “first” and “second” to different types.

[0065] The present application embodiment provides a robot scanning device, such as Figure 2As shown, the robot image scanning device includes: an acquisition module 201, a comparison module 202 and a marking module 203.

[0066] Among them, the acquisition module 201 is used to: acquire the first map established by the robot in the target space at the current moment; the comparison module 202 is used to: compare the first map with the second map established at the historical moment to obtain the similarity comparison result between the first map and the second map; the marking module 203: if the similarity comparison result does not meet the set similarity condition, then add a distinguishing mark in the target space, so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the second map meets the similarity condition.

[0067] Further optionally, when the comparison module 202 compares the first map with the second map established at a historical moment to obtain a similarity comparison result between the first map and the second map, it is specifically used to: obtain first data corresponding to the first sensor in the first map and second data corresponding to the first sensor in the second map; if the similarity between the first data and the second data is less than a preset first threshold, use the second sensor to obtain third data in the target space; the second sensor is of a different type from the first sensor; calculate the similarity between the third data and the fourth data corresponding to the second sensor in the second map as the similarity comparison result between the first map and the second map; the first sensor includes: a lidar sensor; the second sensor includes: at least one of a visual sensor, a magnetic sensor, an ultrasonic sensor, a network signal sensor and a sound signal sensor.

[0068] Further optionally, the marking module 203 adds a distinguishing mark to the target space so that when the similarity comparison result between the map of the target space after the distinguishing mark is added and the second map meets the similarity condition, it is specifically used to: add a distinguishing mark to the target space; perform a mapping operation on the target space after the distinguishing mark is added to obtain the map of the target space after the distinguishing mark is added; calculate the similarity between the map of the target space after the distinguishing mark is added and the second map; if the similarity between the map of the target space after the distinguishing mark is added and the second map does not meet the similarity condition, repeat the operation of adding the distinguishing mark.

[0069] Further optionally, when the marking module 203 adds a distinguishing mark in the target space, it is specifically used to: insert a magnetic marker into the building plane of the target space so that the similarity of the magnetic induction data between the map of the marked target space and the second map is greater than the corresponding magnetic induction similarity threshold.

[0070] Further optionally, when the marking module 203 adds a distinguishing mark in the target space, it is specifically used to: draw an identification pattern on the building plane of the target space so that the similarity of visual perception data between the map of the marked target space and the second map is greater than the corresponding visual similarity threshold.

[0071] Further optionally, when the marking module 203 adds a distinguishing mark in the target space, it is specifically used to: place a reference object in the target space so that the similarity of radar point cloud data between the map of the marked target space and the second map is greater than the corresponding point cloud similarity threshold.

[0072] In this embodiment, the map created by the robot in the target space at the current moment can be obtained, and the map can be compared with the map created at the historical moment to obtain the similarity comparison result between the two maps. If the similarity comparison result does not meet the set similarity condition, a distinguishing mark is added to the target space so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the map created at the historical moment meets the similarity condition. Through this implementation method, the difference between the map created by the robot in the target space and the map created at the historical moment is improved, thereby reducing the possibility of positioning loss during the scanning process.

[0073] Figure 3 is a schematic diagram of the structure of a robot device provided by an exemplary embodiment of the present application. The robot device is applicable to the robot scanning method provided by the aforementioned embodiment, such as Figure 3 As shown, the robot device includes: a memory 301 , a processor 302 and a communication component 303 .

[0074] The memory 301 is used to store computer programs and can be configured to store various other data to support operations on the terminal device. Examples of such data include instructions for any application or method operating on the terminal device, contact data, phone book data, messages, pictures, videos, etc.

[0075] The processor 302 is coupled to the memory 301 and is used to execute the computer program in the memory 301, so as to: obtain a first map established by the robot in the target space at the current moment; compare the first map with a second map established at a historical moment to obtain a similarity comparison result between the first map and the second map; if the similarity comparison result does not meet the set similarity condition, add a distinguishing mark in the target space, so that the similarity comparison result between the map of the target space after adding the distinguishing mark and the second map meets the similarity condition.

[0076] Further optionally, when the processor 302 compares the first map with the second map established at a historical moment to obtain a similarity comparison result between the first map and the second map, it is specifically used to: obtain first data corresponding to the first sensor in the first map and second data corresponding to the first sensor in the second map; if the similarity between the first data and the second data is less than a preset first threshold, use the second sensor to obtain third data in the target space; the second sensor is of a different type from the first sensor; and calculate the similarity between the third data and fourth data corresponding to the second sensor in the second map as the similarity comparison result between the first map and the second map.

[0077] Further optionally, when the processor 302 adds a distinguishing mark to the target space so that a similarity comparison result between the map of the target space after the distinguishing mark is added and the second map meets the similarity condition, the processor 302 is specifically used to: add a distinguishing mark to the target space; perform a mapping operation on the target space after the distinguishing mark is added to obtain a map of the target space after the distinguishing mark is added; calculate the similarity between the map of the target space after the distinguishing mark is added and the second map; if the similarity between the map of the target space after the distinguishing mark is added and the second map does not meet the similarity condition, repeat the operation of adding the distinguishing mark.

[0078] Further optionally, when the processor 302 adds a distinguishing mark in the target space, it is specifically used to: insert a magnetic marker into the building plane of the target space so that the similarity of the magnetic induction data between the map of the marked target space and the second map is greater than the corresponding magnetic induction similarity threshold.

[0079] Further optionally, when the processor 302 adds a distinguishing mark in the target space, it is specifically used to: draw an identification pattern on the building plane of the target space so that the similarity of visual perception data between the map of the marked target space and the second map is greater than the corresponding visual similarity threshold.

[0080] Further optionally, when the processor 302 adds a distinguishing mark in the target space, it is specifically used to: place a reference object in the target space so that the similarity of radar point cloud data between the map of the marked target space and the second map is greater than the corresponding point cloud similarity threshold.

[0081] Further, if Figure 3 As shown, the robot device also includes: a power supply component 304, a first sensor 305, a second sensor 306 and other components. Figure 3 Only some components are shown schematically, which does not mean that the robot device only includes Figure 3 Components shown.

[0082] Accordingly, an embodiment of the present application also provides a computer-readable storage medium storing a computer program, which, when executed, can implement the steps that can be performed by the robot device in the above method embodiment.

[0083] Above Figure 3 The memory 301 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0084] Above Figure 3 The communication component 303 in is configured to facilitate wired or wireless communication between the device where the communication component is located and other devices. The device where the communication component is located can access a wireless network based on a communication standard, such as WiFi, 2G, 3G, 4G or 5G, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component can be implemented based on near field communication (NFC) technology, radio frequency identification (RFID) technology, infrared data association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0085] Above Figure 3 The power supply component 304 in the embodiment provides power to various components of the device where the power supply component is located. The power supply component may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to the device where the power supply component is located.

[0086] Above Figure 3 The first sensor 305 comprises: a laser radar sensor. Figure 3 The second sensor 306 includes one or more of a visual sensor, a magnetic sensor, an ultrasonic sensor, a network signal sensor, and a sound signal sensor.

[0087] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0088] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0089] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0090] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0091] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0092] The memory may include non-permanent storage in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0093] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0094] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0095] The above is only an embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various changes and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.

Claims

1. A robot scanning method, applied to a robot, characterized in that: The method comprises: Obtain the first map established by the robot in the target space at the current moment; Comparing the first map with a second map established at a historical moment to obtain a similarity comparison result between the first map and the second map; If the similarity comparison result does not meet the set similarity condition, a distinguishing mark is added to the target space; a mapping operation is performed on the target space after the distinguishing mark is added to obtain a map of the target space after the distinguishing mark is added; the similarity between the map of the target space after the distinguishing mark is added and the second map is calculated; if the similarity between the map of the target space after the distinguishing mark is added and the second map does not meet the similarity condition, the operation of adding the distinguishing mark is repeated.

2. The method according to claim 1, characterized in that Comparing the first map with a second map established at a historical moment to obtain a similarity comparison result between the first map and the second map includes: Acquire first data corresponding to the first sensor in the first map and second data corresponding to the first sensor in the second map; If the similarity between the first data and the second data is greater than a preset first threshold, a second sensor is used to acquire third data in the target space; the second sensor is of a different type from the first sensor; The similarity between the third data and fourth data corresponding to the second sensor in the second map is calculated as a similarity comparison result between the first map and the second map.

3. The method according to claim 2, characterized in that The first sensor includes: a laser radar sensor; the second sensor includes: at least one of a visual sensor, a magnetic sensor, an ultrasonic sensor, a network signal sensor and a sound signal sensor.

4. The method according to claim 1, characterized in that: Adding distinguishing marks in the target space includes: A magnetic marker is inserted into the building plane of the target space so that the similarity of magnetic induction data between the marked map of the target space and the second map is greater than a corresponding magnetic induction similarity threshold.

5. The method according to claim 1, characterized in that Adding distinguishing marks in the target space includes: A marking pattern is drawn on the building plane of the target space so that the similarity of visual perception data between the marked map of the target space and the second map is greater than a corresponding visual similarity threshold.

6. The method according to claim 1, characterized in that Adding distinguishing marks in the target space includes: A reference object is placed in the target space so that the similarity of radar point cloud data between the marked map of the target space and the second map is greater than a corresponding point cloud similarity threshold.

7. A robot image scanning device, characterized in that: include: An acquisition module is used to: acquire a first map established by the robot in the target space at the current moment; A comparison module, used to compare the first map with a second map established at a historical moment, and obtain a similarity comparison result between the first map and the second map; Marking module: if the similarity comparison result does not meet the set similarity condition, then add a distinguishing mark in the target space; Performing a mapping operation on the target space after adding the distinguishing mark to obtain a map of the target space after adding the distinguishing mark; Calculating the similarity between the map of the target space after adding the distinguishing mark and the second map; If the similarity between the map of the target space after adding the distinguishing mark and the second map does not meet the similarity condition, the operation of adding the distinguishing mark is repeated.

8. A robot device, characterized in that: include: Memory and processor; Wherein, the memory is used to: store one or more computer instructions; The processor is used to execute the one or more computer instructions to: execute the steps in the method according to any one of claims 1-6.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the processor is caused to implement the steps in the method according to any one of claims 1 to 6.

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