Door opening and closing detection method and mobile robot

Abstract

The invention provides a door opening and closing detection method, a mobile robot and a computer readable storage medium. The door opening and closing detection method comprises the following steps: acquiring a distance between a specified position point of a target room and the target room as a door detection distance of the target room; acquiring a room door detection width of the target room; determining a point cloud limit angle of the target room according to the door detection distance and the door detection width; when the mobile robot navigates to the designated position point, obtaining each point cloud of interest of the target room according to the point cloud limit angle; and detecting the door opening and closing state of the target room based on the point cloud of interest and the robot navigation pose of the specified position point. The detection precision of the opening and closing state of the room door can be improved.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to a door opening and closing detection method, a mobile robot and a computer-readable storage medium. Background Art

[0002] As robotics technology matures and consumers’ demand for automated services increases, robots are being applied to more and more scenarios, such as hotels, retail, warehousing and logistics, etc.

[0003] In the related art, in order to improve the efficiency of the mobile robot, the mobile robot needs to determine whether the door is open in order to proceed to the next operation. For example, when the mobile robot delivers goods to the designated room, it automatically opens the door when it detects that the door is open; for another example, when the mobile robot passes through the fire escape door, it needs to detect that the fire escape door is open before continuing to move forward. However, the inventor of this application found in the actual research and development process that in the related art, the detection accuracy of the open and closed state of the room door is low. Summary of the invention

[0004] The present application provides a door opening and closing detection method, a mobile robot and a computer-readable storage medium, which can improve the detection accuracy of the opening and closing status of a room door.

[0005] In a first aspect, the present application provides a door opening and closing detection method, the method comprising:

[0006] Obtaining the distance between the designated position point of the target room and the target room as the door detection distance of the target room;

[0007] Obtaining the door detection width of the target room;

[0008] Determining the point cloud limit angle of the target room according to the door detection distance and the door detection width;

[0009] After the mobile robot navigates to the designated location, each point cloud of interest in the target room is obtained according to the point cloud limit angle;

[0010] Based on the cloud of points of interest and the robot navigation posture of the designated position point, the door opening and closing status of the target room is detected.

[0011] In a second aspect, the present application further provides a mobile robot, comprising a processor and a memory, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, it executes any one of the door opening and closing detection methods provided in the present application.

[0012] In a third aspect, the present application also provides a computer-readable storage medium on which a computer program is stored, and the computer program is loaded by a processor to execute the door opening and closing detection method.

[0013] In the present application, on the one hand, by determining the point cloud limit angle of the target room according to the door detection distance and door detection width of the target room, the point cloud data of interest can be dynamically determined for judging the open and closed state of the room door, so that the point cloud detection range can be dynamically adjusted according to the actual delivery conditions of different rooms (such as room width, distance between the mobile robot and the door), thereby improving the detection accuracy of the open and closed state of the room door. On the second hand, by dynamically determining the point cloud data of interest in combination with the door detection width, it is avoided that different rooms use a unified point cloud detection range for detecting the open and closed state of the room door, thereby avoiding the problem of low detection accuracy of the open and closed state of the room door caused by the room being too large or too small. On the third hand, by dynamically determining the point cloud data of interest in combination with the door detection distance, it is avoided that different rooms use a unified point cloud detection range for detecting the open and closed state of the room door, thereby avoiding the problem of low detection accuracy of the open and closed state of the room door caused by the door detection distance being too large or too small. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0015] Figure 1 is a schematic block diagram of the structure of a mobile robot provided in an embodiment of the present application;

[0016] Figure 2 It is a flow chart of a door opening and closing detection method provided in an embodiment of the present application;

[0017] Figure 3 is a schematic diagram for explaining the limit angle of the point cloud in the embodiment of the present application;

[0018] Figure 4 is a schematic diagram for explaining the navigation coordinate system and the robot coordinate system in the embodiment of the present application;

[0019] Figure 5 is a schematic diagram for explaining how to determine the opening and closing indication information of the point of interest cloud in an embodiment of the present application;

[0020] Figure 6 It is a schematic diagram of an embodiment of the door opening and closing detection process provided in the embodiments of the present application. DETAILED DESCRIPTION

[0021] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of this application.

[0022] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may also be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0023] In the description of the embodiments of the present application, it should be understood that the terms "first" and "second" are used for descriptive purposes only and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features. In the description of the embodiments of the present application, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined.

[0024] In order to enable any person skilled in the art to implement and use the present application, the following description is provided. In the following description, details are listed for the purpose of explanation. It should be understood that those of ordinary skill in the art can recognize that the present application can also be implemented without using these specific details. In other examples, the known process will not be elaborated in detail to avoid unnecessary details that make the description of the present application embodiment obscure. Therefore, the present application is not intended to be limited to the embodiments shown, but is consistent with the widest range of principles and features disclosed in accordance with the embodiments of the present application.

[0025] The embodiment of the present application provides a door opening and closing detection method, a mobile robot and a computer-readable storage medium. The door opening and closing detection method can be applied to a mobile robot, which can be a cleaning robot, a food delivery robot, etc.

[0026] Some embodiments of the present application are described in detail below in conjunction with the accompanying drawings. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.

[0027] Figure 1 It is a structural schematic block diagram of a mobile robot provided in an embodiment of the present application.

[0028] like Figure 1As shown, the mobile robot 100 includes a processor 101 and a memory 102, and the processor 101 and the memory 102 are connected via a bus 103, such as a PCIe (Peripheral Component Interconnect Express) bus.

[0029] Specifically, the processor 101 is used to provide computing and control capabilities to support the operation of the entire mobile robot 100. The processor 101 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0030] Specifically, the memory 102 may be a Flash chip, a read-only memory (ROM) disk, an optical disk, a USB flash drive, or a mobile hard disk.

[0031] Those skilled in the art will understand that Figure 1 The structure shown in the figure is merely a block diagram of a partial structure related to the embodiment scheme of the present application, and does not constitute a limitation on the mobile robot to which the embodiment scheme of the present application is applied. The specific mobile robot may include more or fewer components than shown in the figure, or combine certain components, or have a different arrangement of components.

[0032] The processor 101 is used to run the computer program stored in the memory 102, and implement any one of the door opening and closing detection methods provided in the embodiments of the present application when executing the computer program. For example, the processor 101 is used to run the computer program stored in the memory 102, and can implement the following steps when executing the computer program:

[0033] Obtain the distance between the designated position point of the target room and the target room as the door detection distance of the target room; obtain the door detection width of the target room; determine the point cloud limit angle of the target room according to the door detection distance and the door detection width; when the mobile robot navigates to the designated position point, obtain each point cloud of interest in the target room according to the point cloud limit angle; based on the robot navigation posture of each point cloud of interest and the designated position point, detect the open and closed state of the door of the target room.

[0034] In some embodiments, the processor 101 is used to run a computer program stored in the memory 102, and when executing the computer program, the following steps may be implemented:

[0035] When the mobile robot navigates to the designated location, the mobile robot is controlled to collect data according to the point cloud limit angle to obtain first collected point cloud data of the target room; each point cloud of the first collected point cloud data is used as a point cloud of interest of the target room.

[0036] In some embodiments, the processor 101 is used to run a computer program stored in the memory 102, and when executing the computer program, the following steps may be implemented:

[0037] After the mobile robot navigates to the designated location point, the mobile robot is controlled to collect data according to a preset collection angle to obtain second collected point cloud data of the target room; each point cloud in the second collected point cloud data is traversed to obtain the angle between the current traversed point cloud and the designated location point as the discrimination angle of the current traversed point cloud; if the discrimination angle is less than or equal to the point cloud limit angle, the current traversed point cloud is used as the point cloud of interest.

[0038] In some embodiments, the processor 101 is used to run a computer program stored in the memory 102, and when executing the computer program, the following steps may be implemented:

[0039] Based on the robot navigation posture of the designated position point, the first direction vector of the designated position point is obtained; based on the navigation coordinate system coordinates of each of the interest point clouds, the projection length of each of the interest point clouds on the straight line corresponding to the first direction vector is obtained as the projection length of each of the interest point clouds; according to the projection length of each of the interest point clouds and the door detection distance, the open or closed state of the door of the target room is determined.

[0040] In some embodiments, the processor 101 is used to run a computer program stored in the memory 102, and when executing the computer program, the following steps may be implemented:

[0041] According to the projection length of the point cloud of interest and the door detection distance, the number of point clouds in the open state of the target room is obtained; when the number of point clouds in the open state is greater than a first preset point cloud number threshold, it is determined that the target room is in a door-open state; or, when the number of point clouds in the open state is less than or equal to the first preset point cloud number threshold, it is determined that the target room is in a door-closed state.

[0042] In some embodiments, the processor 101 is used to run a computer program stored in the memory 102, and when executing the computer program, the following steps may be implemented:

[0043] The difference between the projection length of each point cloud of interest and the door detection distance is obtained as the distance difference of each point cloud of interest; according to the distance difference of each point cloud of interest and a preset distance difference threshold, the state of each point cloud of interest is determined to obtain the opening and closing indication information of each point cloud of interest; according to the opening and closing indication information of each point cloud of interest, the number of open state point clouds of the target room is obtained.

[0044] In some embodiments, the processor 101 is used to run a computer program stored in the memory 102, and when executing the computer program, the following steps may be implemented:

[0045] According to the projection length of the point cloud of interest and the door detection distance, the number of point clouds in the closed state of the target room is obtained; when the number of point clouds in the closed state is less than or equal to a second preset point cloud number threshold, it is determined that the target room is in a door-open state; or, when the number of point clouds in the closed state is greater than the second preset point cloud number threshold, it is determined that the target room is in a door-closed state.

[0046] In some embodiments, the processor 101 is used to run a computer program stored in the memory 102, and when executing the computer program, the following steps may be implemented:

[0047] Based on the navigation coordinate system coordinates of each of the interest point clouds and the navigation coordinate system coordinates of the designated position point, the second direction vector of each of the interest point clouds is obtained; the angle between the second direction vector of each of the interest point clouds and the second direction vector is obtained as the projection angle of each of the interest point clouds; and the projection length of each of the interest point clouds is determined according to the projection angle of each of the interest point clouds, the first direction vector and the second direction vector of each of the interest point clouds.

[0048] In some embodiments, the processor 101 is used to run a computer program stored in the memory 102, and when executing the computer program, the following steps may be implemented:

[0049] When it is detected that the door of the target room is open, the mobile robot is controlled to open the door.

[0050] It should be noted that technicians in the relevant field can clearly understand that for the convenience and simplicity of description, the specific working process of the mobile robot described above can refer to the corresponding process in the following door opening and closing detection method embodiment, and will not be repeated here.

[0051] Please note: Figure 1 The scenario is only used to explain the door opening and closing detection method provided in the embodiment of the present application, but does not constitute a limitation on the application scenario of the door opening and closing detection method provided in the embodiment of the present application.

[0052] See also Figure 2 , Figure 2 201 to 205, wherein:

[0053] 201. Obtain a distance between a designated position point of a target room and the target room as a door detection distance of the target room.

[0054] Among them, the mobile robot can be a delivery robot, a food delivery robot, etc.

[0055] The target room is the room where the door opening and closing status detection is required. For example, in the scenario of mobile robot hotel food delivery, the target room can be the hotel room where the food is to be delivered. For another example, in the scenario of mobile robot post station express delivery, the target room can be the post station store.

[0056] The designated location point (denoted as p) may be a navigation location point of a target room. For example, in a mobile robot hotel cargo delivery scenario, a navigation location point (e.g., a location point 0.5 meters in front of the door of the target room) is set for each room, so that the mobile robot can navigate to the door of the target room according to the navigation location point to complete the cargo delivery to the target room.

[0057] The door detection distance (denoted as L) is the distance between room j and the designated location point of room j.

[0058] In some embodiments, the door detection distance of the target room can be preset according to the actual business scenario. For example, it can be determined by the deployment engineer of the mobile robot. The door detection distance is used to indicate how far away the mobile robot is from the door of the target room when it stops.

[0059] 202. Obtain a door detection width of the target room.

[0060] The door detection width (denoted as W) is used to indicate the detection width of the point cloud of room j when judging the open or closed state of the door of room j. Exemplarily, the door detection width of room j can be set to be greater than or equal to the actual width of room j, for example, the door detection width of room j is about 10% larger than the actual width of room j.

[0061] There are many ways to determine the door detection width in step 202, illustratively including:

[0062] (1) In some embodiments, the door detection width is a preset value. In this case, different door detection widths can be set for different rooms, and the room identification of each room can be associated with the door detection width and stored in a preset database; in step 202, the door detection width associated with the room identification of the target room can be queried from the preset database based on the room identification of the target room, as the door detection width of the target room. For example, different door detection widths can be set for different room types. For example, if Room 101 is a suite with a relatively large width, the door detection width of Room 101 can be set to 10 meters; if Room 102 is a single room with a relatively small width, the door detection width of Room 102 can be set to 5 meters; and Room 101 can be associated with a door detection width of 10 meters and stored in the preset database, and Room 102 can be associated with a door detection width of 5 meters and stored in the preset database.

[0063] (2) In some embodiments, the door detection width is dynamically determined based on the actual door width. In this case, the room identifier of each room can be associated with the actual door width and stored in a preset database; in step 202, first, based on the room identifier of the target room, the actual door width associated with the room identifier of the target room can be queried from the preset database; then, according to the preset detection ratio (e.g., if the door detection width is about 10% larger than the actual width, the preset detection ratio can be set to 110%) and the actual door width, the target room's detection width is calculated.

[0064] 203. Determine the point cloud limit angle of the target room according to the door detection distance and the door detection width.

[0065] Among them, the point cloud limit angle is used to indicate the angle range of the point cloud of interest of room j. The point cloud limit angle is dynamically determined according to the door detection distance and the door detection width, wherein the point cloud limit angle is negatively correlated with the door detection distance, and the point cloud limit angle is positively correlated with the door detection width. Therefore, by obtaining the door detection distance and the door detection width of the target room to dynamically determine the point cloud limit angle, the point cloud limit angle can be dynamically adjusted for different rooms, so that the detection range can be dynamically adjusted according to the actual delivery conditions of different rooms, and the detection accuracy of door opening and closing can be improved. On the one hand, it is avoided to set the point cloud limit angle of different rooms to a unified fixed value, thereby avoiding the problem that the point cloud limit angle is set too small and the door cannot be fully detected. On the other hand, it is avoided to set the point cloud limit angle of different rooms to a unified fixed value, thereby avoiding the problem that the point cloud limit angle is set too large and the point cloud data processing amount for door opening and closing state detection is too large.

[0066] For example, see Figure 3 , Figure 3 : is a schematic diagram illustrating the point cloud limit angle in the embodiment of the present application. For example, the door detection distance and door detection width of the target room can be substituted into the following formula 1, and the point cloud limit angle φ can be calculated according to the following formula 1:

[0067]

[0068] In Formula 1, L represents the door detection distance of the target room, W represents the door detection width of the target room, and φ represents the point cloud limit angle of the target room.

[0069] 204. After the mobile robot navigates to the designated location, each point cloud of interest in the target room is acquired according to the point cloud limit angle.

[0070] For easier understanding, please refer to Figure 4 , the following first introduces some names involved in this embodiment:

[0071] 1. Navigation map: The navigation map can be constructed by using map construction technology (such as SLAM technology) to obtain a navigation map of the unknown environment by moving a mobile robot in an unknown environment (such as a hotel scenario, a navigation map can be constructed for each floor). The navigation map can be a three-dimensional map or a two-dimensional map. In this embodiment, the navigation map is a two-dimensional map as an example.

[0072] 2. Navigation coordinate system (i.e. global coordinate system): the reference coordinate system of the navigation map, used to describe the entire environment or background. Figure 4As shown, assuming that the rectangular box is the area range of the navigation map, the lower left corner of the navigation map can be taken as the origin O, the direction from the lower left corner to the lower right corner of the navigation map as the x-axis direction, and the direction from the lower left corner to the upper left corner of the navigation map as the y-axis direction, to construct a navigation coordinate system oxy. Therefore, any point on the navigation map can be represented by the navigation coordinate system.

[0073] The navigation coordinate system coordinates refer to the coordinates obtained by using the navigation coordinate system. Figure 4 As shown, the coordinates of the navigation coordinate system can be expressed by two-dimensional coordinates (x, y).

[0074] 3. Robot coordinate system (i.e. local coordinate system): A coordinate system connected to the mobile robot that moves and rotates with the mobile robot. For example, Figure 4 As shown, the navigation coordinate system o'x'y' can be constructed by taking the center point of the mobile robot as the origin o', the front direction of the mobile robot as the x' axis direction, and the front direction of the mobile robot as the y' axis direction.

[0075] 4. Angle between the robot coordinate system and the navigation coordinate system: In this embodiment, when the x'-axis is parallel to the x-axis and the y'-axis is parallel to the y-axis, the angle θ between the robot coordinate system and the navigation coordinate system is 0°; taking this as a reference point, the direction angle of the moving robot at a certain point in time (i.e. the angle θ between the robot coordinate system and the navigation coordinate system) is equal to the cumulative angle of the robot coordinate system required to rotate clockwise relative to the xoy plane of the navigation coordinate system until the x'-axis is parallel to the x-axis and the y'-axis is parallel to the y-axis.

[0076] 5. Robot navigation posture: including the position representation and posture representation of the mobile robot.

[0077] 5.1. Position representation (ie, navigation coordinate system coordinates): In this embodiment, the navigation coordinate system coordinates of the mobile robot's position point p in the navigation map (eg, the projection point of the mobile robot's center point o' in the navigation map) are used as the position representation.

[0078] 5.2. Posture representation (ie, orientation angle): In this embodiment, the angle between the robot coordinate system and the navigation coordinate system is used as the posture representation.

[0079] In this embodiment, the navigation coordinate system and the robot coordinate system are two-dimensional coordinate systems for illustration. It can be understood that in some cases the navigation coordinate system and the robot coordinate system can also be three-dimensional coordinate systems. Converting the specific coordinate relationship from two-dimensional to three-dimensional can also implement the method of the embodiment of the present application.

[0080] For example, since the navigation coordinate system and the robot coordinate system are two-dimensional coordinate systems in this embodiment, the robot navigation posture in this embodiment has three degrees of freedom: movement along the x-axis, movement along the y-axis, and rotation perpendicular to the xoy plane of the navigation coordinate system. Figure 3 As shown in the figure, it is assumed that the mobile robot follows the robot navigation posture (p x ,p y ,θ) navigates to the specified position point p, the navigation coordinate system coordinates (p x ,p y ) is used as the position representation of the mobile robot, and the angle θ between the robot coordinate system and the navigation coordinate system is used as the posture representation of the mobile robot.

[0081] There are many ways to obtain the cloud of each point of interest of the target room in step 204, illustratively including:

[0082] (1) The mobile robot collects point clouds according to a preset collection angle. At this time, step 204 may specifically include the following steps 2041A to 2042A:

[0083] 2041A. After the mobile robot navigates to the designated location, the mobile robot is controlled to collect data according to the point cloud limit angle to obtain the first collected point cloud data of the target room.

[0084] The first collected point cloud data refers to the point cloud data collected by the mobile robot according to the point cloud limit angle.

[0085] 2042A. Use each point cloud of the first collected point cloud data as a point cloud of interest of the target room.

[0086] For example, Figure 3 As shown in the figure, taking the mobile robot collecting point cloud data through the lidar sensor as an example, when the mobile robot navigates to the specified position point p, assuming The direction is the initial direction of the point cloud detection line of the mobile robot, which can be calculated based on the point cloud limit angle φ and the initial position of the point cloud detection line (reference Figure 3 middle ), determine the maximum acquisition position on the left (reference Figure 3 middle ray pa), the maximum acquisition position on the right (reference Figure 3 Then, the point cloud detection line of the laser radar sensor controlling the mobile robot is changed from the initial position (reference Figure 3 middle ) Rotate the point cloud limit angle φ to the left to the maximum acquisition position on the left (reference Figure 3 ray pa), and by the initial position (reference Figure 3 middle ) Rotate the point cloud limit angle φ to the right to the maximum acquisition position (reference Figure 3 The point cloud collected by the point cloud detection line can be used as the first collected point cloud data C1 of the target room (reference Figure 3 Each point cloud in the first collected point cloud data C1 can be used as a point cloud of interest in the target room.

[0087] (2) The mobile robot collects point clouds according to the point cloud limit angle. At this time, step 204 may specifically include the following steps 2041B to 2043B:

[0088] 2041B. After the mobile robot navigates to the designated location, the mobile robot is controlled to collect data according to a preset collection angle to obtain second collection point cloud data of the target room.

[0089] The second collected point cloud data refers to the point cloud data collected by the mobile robot according to a preset collection angle.

[0090] 2042B. Traverse each point cloud in the second collected point cloud data, and obtain the angle between the currently traversed point cloud and the specified position point as the determination angle of the currently traversed point cloud.

[0091] 2043B. If the determination angle is less than or equal to the point cloud limit angle, the currently traversed point cloud is taken as the point cloud of interest.

[0092] For example, taking the mobile robot collecting point cloud data through a laser radar sensor as an example, assuming that the preset collection angle is the maximum detection angle of the laser radar sensor (such as 180°), the mobile robot can be controlled to collect according to the preset collection angle to obtain the second collected point cloud data C2 of the target room. Then, the following steps (2.1) to (2.6) can be performed on the second collected point cloud data C2 to obtain the point clouds of interest in the target room:

[0093] (2.1) For the i-th point cloud in the second collected point cloud data C2, obtain the angle between the i-th point cloud and the specified position point p as the judgment angle φ of the i-th point cloud i ; If the discriminant angle φ of the i-th point cloud i is less than or equal to the point cloud limit angle φ, go to step (2.2); if the judgment angle φ of the i-th point cloud i If it is greater than the point cloud limit angle φ, go to step (2.3).

[0094] (2.2) If the discriminant angle φ of the i-th point cloud i If it is less than or equal to the point cloud limit angle φ, the i-th point cloud is taken as the point cloud of interest and proceed to step (2.4).

[0095] (2.3) If the discriminant angle φ of the i-th point cloud i If it is greater than the point cloud limit angle φ, the i-th point cloud is discarded (i.e., the i-th point cloud is not considered as a point cloud of interest) and the process goes to step (2.4).

[0096] (2.4) Check whether i is equal to K. If i is equal to K, it proves that the traversal of each point cloud of the second collected point cloud data is completed, and enter step (2.6); if i is less than K, it proves that the traversal of each point cloud of the second collected point cloud data is not completed, and enter step (2.5).

[0097] Wherein, K is the total number of point clouds of the second collected point cloud data.

[0098] (2.5) Let i=i+1 and proceed to step (2.1).

[0099] (2.6) The determination process is terminated, and all the point clouds of interest in the target room are screened out from the second collected point cloud data.

[0100] It can be seen that by controlling the mobile robot to collect point cloud data according to the point cloud limit angle, each point cloud of the first collected point cloud data can be directly used as the point cloud of interest in the target room, thereby reducing the amount of point cloud collection and avoiding the process of determining whether the collected point cloud data is a point cloud of interest one by one, thereby improving the detection speed of the open and closed status of the door.

[0101] It is understandable that, in addition to collecting point cloud data through a lidar sensor, the mobile robot can also collect point cloud data through an RGBD sensor or other sensors.

[0102] 205. Detect the open / closed state of the door of the target room based on the robot navigation posture of each of the interest point clouds and the designated position point.

[0103] There are many ways to implement step 205, illustratively including the following ways (1) to (4):

[0104] (1) The relationship between the number of open point clouds of the point cloud of interest and the first preset point cloud number threshold can be counted to determine the door open or closed state of the target room. At this time, step 205 can specifically include the following steps 2051A to 2055A:

[0105] 2051A. Based on the robot navigation posture of the designated position point, obtain a first direction vector of the designated position point.

[0106] Among them, the first direction vector (denoted as ) refers to the direction vector of the specified position point. The first direction vector According to the robot navigation posture (p x ,p y ,θ).

[0107] For example, the robot navigation pose (p x ,p y ,θ) includes the navigation coordinate system coordinates of the mobile robot at the specified position point p (p x ,p y ) and the orientation angle θ of the mobile robot, the first direction vector of the specified position point can be calculated by referring to the following formula 2

[0108]

[0109] 2052A. Based on the navigation coordinate system coordinates of each of the interest point clouds, obtain the projection length of each of the interest point clouds on the straight line corresponding to the first direction vector as the projection length of each of the interest point clouds.

[0110] Exemplarily, the process of determining the projection length of each point cloud of interest in step 2052A may specifically include: obtaining the second direction vector of each point cloud of interest based on the navigation coordinate system coordinates of each point cloud of interest and the navigation coordinate system coordinates of the designated location point; obtaining the angle between the second direction vector of each point cloud of interest and the second direction vector as the projection angle of each point cloud of interest; determining the projection length of each point cloud of interest based on the projection angle of each point cloud of interest, the first direction vector and the second direction vector of each point cloud of interest.

[0111] Among them, the second direction vector (denoted as ) refers to the cloud of points of interest c i direction vector, the second direction vector According to the navigation coordinate system coordinates of the specified position point (p x ,p y ) and the navigation coordinate system coordinates of the point cloud of interest It depends.

[0112] Please refer to Figure 5 For example, first, the point cloud c i The navigation coordinate system coordinates and the navigation coordinate system coordinates of the specified location point (p x ,p y ) is substituted into the following formula 3, and the point cloud of interest c is calculated by referring to the following formula 3 i The second direction vector Then, refer to the following formula 4 to calculate the point cloud of interest c i The projection angle φ iFinally, the point cloud of interest c i The second direction vector First direction vector and the point cloud of interest c i The projection angle φ i Substitute into the following formula 5 and refer to the following formula 5 to calculate the projection length dist of the point cloud of interest i .

[0113]

[0114]

[0115] 2053A. Obtain the number of open state point clouds of the target room according to the projection length of the point cloud of interest and the door detection distance.

[0116] There are many ways to implement step 2053A, illustratively including the following ways ① to ②:

[0117] ① In some embodiments, due to the measurement error of the laser radar sensor and the positioning error of the mobile robot, in order to improve the detection accuracy of the door opening and closing state, if (the cloud of interest point c i The projection length dist i -door detection distance L)>preset distance difference threshold δ, then the point cloud of interest c i As the open state point cloud, the number of open state point clouds of the target room is obtained. At this time, step 2053A specifically includes: obtaining the difference between the projection length of each of the interest point clouds and the door detection distance as the distance difference of each of the interest point clouds; according to the distance difference of each of the interest point clouds and the preset distance difference threshold, determining the state of each of the interest point clouds to obtain the opening and closing indication information of each of the interest point clouds; according to the opening and closing indication information of each of the interest point clouds, obtaining the number of open state point clouds of the target room.

[0118] Among them, the opening and closing indication information is used to indicate the point cloud of interest c i Whether the point cloud is in the open state.

[0119] For example, refer to Figure 5 And the following formula 6, first, get the point cloud c of interest i The projection length dist i The difference between the door detection distance L and the point of interest cloud c i The distance difference (i.e., dist i -L); Then, determine the point cloud of interest c i The distance difference (i.e., dist i -L) is greater than the preset distance difference threshold δ, such as Figure 5As shown in (b), if the point cloud c i The distance difference (i.e., dist i -L) is greater than the preset distance difference threshold δ, then the point cloud of interest c i As the open state point cloud, the timer N of the number of open state point clouds is increased by 1, that is, N = N + 1; otherwise, Figure 5 As shown in (a), if the point cloud c i The distance difference (i.e., dist i -L) is less than or equal to the preset distance difference threshold δ, then the point cloud of interest c i As the closed state point cloud, continue to determine the next point cloud of interest c i+1 Whether to use it as an open state point cloud. When all the point clouds of interest are judged, the value of the timer N is the number of open state point clouds in the target room.

[0120] dist i -L>δ Formula 6

[0121] ② In some embodiments, if the point cloud of interest c i The projection length dist i If it is greater than the door detection distance L, the point cloud of interest c i As the open state point cloud, the number of open state point clouds of the target room is obtained. At this time, step 2053A specifically includes: determining the point cloud of interest c i The projection length dist i Is it greater than the door detection distance L? Figure 5 As shown in (b), if the point cloud c i The projection length dist i If it is greater than the door detection distance L, then the point cloud of interest c i As the open state point cloud, the timer N of the number of open state point clouds is increased by 1, that is, N = N + 1; otherwise, Figure 5 As shown in (a), if the point cloud c i The projection length dist i If the distance L is less than or equal to the door detection distance, the point cloud c i As the closed state point cloud, continue to determine the next point cloud of interest c i+1 Whether to use it as an open state point cloud. When all the point clouds of interest are judged, the value of the timer N is the number of open state point clouds in the target room.

[0122] 2054A. When the number of the open state point clouds is greater than a first preset point cloud number threshold, it is determined that the target room is in a door open state.

[0123] 2055A. When the number of point clouds in the open state is less than or equal to a first preset point cloud number threshold, it is determined that the target room is in a door closed state.

[0124] Among them, the specific value of the first preset point cloud quantity threshold (denoted as Nt) can be set according to the actual business scenario requirements. There is no restriction on the specific value of the first preset point cloud quantity threshold here. For example, in order to reduce the point cloud noise caused by the lidar sensor, the first preset point cloud quantity threshold Nt can be set to about 5 to 10.

[0125] (2) The relationship between the number of closed point clouds of the point cloud of interest and the second preset point cloud number threshold can be counted to determine the door open or closed state of the target room. At this time, step 205 can specifically include the following steps 2051B to 2055B:

[0126] 2051B. Based on the robot navigation posture of the designated position point, obtain a first direction vector of the designated position point.

[0127] 2052B. Based on the navigation coordinate system coordinates of each of the interest point clouds, obtain the projection length of each of the interest point clouds on the straight line corresponding to the first direction vector as the projection length of each of the interest point clouds.

[0128] The implementation of steps 2051B to 2052B is similar to that of steps 2051A to 2052A. For details, please refer to the relevant descriptions in the previous text, which will not be repeated here.

[0129] 2053B. Obtain the number of closed state point clouds of the target room according to the projection length of the point cloud of interest and the door detection distance.

[0130] There are many ways to implement step 2053B, illustratively including the following ways ① to ②:

[0131] ① In some embodiments, due to the measurement error of the laser radar sensor and the positioning error of the mobile robot, in order to improve the detection accuracy of the door opening and closing state, if (the cloud of interest point c i The projection length dist i -door detection distance L)≤preset distance difference threshold δ, then the point cloud of interest c iAs the closed state point cloud, the number of closed state point clouds of the target room is obtained. At this time, step 2053B specifically includes: obtaining the difference between the projection length of each of the interest point clouds and the door detection distance as the distance difference of each of the interest point clouds; according to the distance difference of each of the interest point clouds and the preset distance difference threshold, determining the state of each of the interest point clouds to obtain the opening and closing indication information of each of the interest point clouds; according to the opening and closing indication information of each of the interest point clouds, obtaining the number of closed state point clouds of the target room.

[0132] Among them, the opening and closing indication information is used to indicate the point cloud of interest c i Whether it is a closed point cloud.

[0133] For example, refer to Figure 5 And the following formula 7, first, get the point cloud c of interest i The projection length dist i The difference between the door detection distance L and the point of interest cloud c i The distance difference (i.e., dist i -L); Then, determine the point cloud of interest c i The distance difference (i.e., dist i -L) is less than or equal to the preset distance difference threshold δ, such as Figure 5 As shown in (a), if the point cloud c i The distance difference (i.e., dist i -L) is less than or equal to the preset distance difference threshold δ, then the point cloud of interest c i As the closed state point cloud, the timer N of the closed state point cloud number is increased by 1, that is, M = M + 1; otherwise, Figure 5 As shown in (b), if the point cloud c i The distance difference (i.e., dist i -L) is greater than the preset distance difference threshold δ, then the point cloud of interest c i As the open state point cloud, continue to determine the next point cloud of interest c i+1 Whether to use it as a closed state point cloud. When all the point clouds of interest are judged, the value of the timer M is the number of closed state point clouds in the target room.

[0134] dist i -L≤δ Formula 7

[0135] ② In some embodiments, if the point cloud of interest c i The projection length dist i If it is less than or equal to the door detection distance L, the point cloud of interest c iAs the closed state point cloud, the number of closed state point clouds of the target room is obtained. At this time, step 2053B specifically includes: determining the point cloud of interest c i The projection length dist i Is it less than or equal to the door detection distance L? Figure 5 As shown in (a), if the point cloud c i The projection length dist i is less than or equal to the door detection distance L, then the point cloud of interest c i As the closed state point cloud, the timer M of the closed state point cloud number is increased by 1, that is, M = N + 1; otherwise, Figure 5 As shown in (b), if the point cloud c i The projection length dist i If the distance L is greater than the door detection distance, the point cloud c i As the open state point cloud, continue to determine the next point cloud of interest c i+1 Whether to use it as a closed state point cloud. When all the point clouds of interest are judged, the value of the timer M is the number of closed state point clouds in the target room.

[0136] 2054B. When the number of point clouds in the closed state is less than or equal to a second preset point cloud number threshold, it is determined that the target room is in a door open state.

[0137] 2055B. When the number of closed state point clouds is greater than a second preset point cloud number threshold, it is determined that the target room is in a door closed state.

[0138] Among them, the specific value of the second preset point cloud quantity threshold can be set according to the actual business scenario requirements, and the specific value of the second preset point cloud quantity threshold is not limited here.

[0139] (3) The relationship between the ratio of the open point cloud of the point cloud of interest and the first preset ratio threshold can be counted to determine the open or closed state of the door of the target room. At this time, step 205 can specifically include the following steps 2051C to 2056C:

[0140] 2051C. Based on the robot navigation posture of the designated position point, obtain a first direction vector of the designated position point.

[0141] 2052C. Based on the navigation coordinate system coordinates of each of the interest point clouds, obtain the projection length of each of the interest point clouds on the straight line corresponding to the first direction vector as the projection length of each of the interest point clouds.

[0142] 2053C. Obtain the number of open state point clouds of the target room according to the projection length of the point cloud of interest and the door detection distance.

[0143] The implementation of steps 2051C to 2053C is similar to that of steps 2051A to 2053A. For details, please refer to the relevant descriptions in the previous text, which will not be repeated here.

[0144] 2054C. Determine a ratio of the open state point clouds of the target room according to the number of the open state point clouds and the total number of the point clouds of interest.

[0145] For example, the ratio between the number of on-state point clouds and the total number of interesting point clouds may be used as the ratio of on-state point clouds in the target room.

[0146] 2055C. When the open state point cloud ratio is greater than or equal to a first preset ratio threshold, it is determined that the target room is in a door open state.

[0147] 2056C. When the open state point cloud ratio is less than a first preset ratio threshold, it is determined that the target room is in a door closed state.

[0148] (4) The relationship between the percentage of the closed point cloud of the point cloud of interest and the second preset percentage threshold can be counted to determine the open or closed state of the door of the target room. At this time, step 205 can specifically include the following steps 2051D to 2055D:

[0149] 2051D. Based on the robot navigation posture of the designated position point, obtain a first direction vector of the designated position point.

[0150] 2052D. Based on the navigation coordinate system coordinates of each of the interest point clouds, obtain the projection length of each of the interest point clouds on the straight line corresponding to the first direction vector as the projection length of each of the interest point clouds.

[0151] 2053D. Obtain the number of closed state point clouds of the target room according to the projection length of the point cloud of interest and the door detection distance.

[0152] The implementation of steps 2051D to 2053D is similar to that of steps 2051B to 2053B. For details, please refer to the relevant descriptions in the previous text, which will not be repeated here.

[0153] 2054D. Determine the percentage of closed state point clouds of the target room according to the number of closed state point clouds and the total number of interest point clouds.

[0154] For example, the ratio between the number of closed state point clouds and the total number of interesting point clouds can be used as the closed state point cloud ratio of the target room.

[0155] 2055D. When the closed state point cloud ratio is greater than or equal to a second preset ratio threshold, it is determined that the target room is in a door closed state.

[0156] 2056D. When the closed state point cloud ratio is less than a second preset ratio threshold, it is determined that the target room is in a door open state.

[0157] Please refer to Figure 3 , Figure 5 and Figure 6 In order to better understand the embodiments of the present application, the following takes "in the workflow of the hotel delivery robot, the mobile robot delivers the goods to the room door before and after, and autonomously determines the state of the robot's cabin door by detecting the opening and closing state of the room door" as an example to introduce the door opening and closing detection process in the embodiments of the present application. It is assumed that the mobile robot includes a navigation module (used to control the mobile robot to reach the specified position point of the target room according to the robot's navigation posture), a door state detection module (detects the opening and closing state of the door of the target room through a laser radar) and a cabin door control module (used to control the opening and closing of the robot's cabin door). At this time, Figure 6 As shown, the door opening and closing detection process can be as follows:

[0158] 1. Through the navigation module of the mobile robot, control the mobile robot according to the robot navigation posture (p x ,p y ,θ) arrives at the designated location point of the target room.

[0159] 2. When the mobile robot reaches the target room through the navigation module, the door status detection module of the mobile robot is turned on; the door status detection module of the mobile robot detects the door opening and closing status of the target room. The implementation process of the door status detection module can be as follows (corresponding to steps 203 to 205):

[0160] 2.1) Obtain the point cloud data C2 of the laser radar; refer to step 2041B for detailed implementation;

[0161] 2.2) Set the counter N to 0, and calculate the limit angle of the point cloud in the range of interest (i.e., the point cloud limit angle) according to the door detection width W and the door detection distance L. Calculate the direction vector of the specified position point

[0162] 2.3) For the point cloud c in C2 i , calculate the point cloud c i Direction vector And calculate the point cloud c i Direction vector The direction vector to the specified location point The angle φ i ;

[0163] 2.4) If φ i <φ, calculate the point cloud c i The length of the projection on the line corresponding to the direction vector

[0164]

[0165] 2.5) If dist i -L>δ, then add 1 to the counter, that is, N=N+1;

[0166] 2.6) Repeat steps 2.3), 2.4) and 2.5) until all point clouds in C2 are counted and then proceed to step 2.7);

[0167] 2.7) If N is greater than the set threshold Nt, the target room is returned to the door open state, otherwise the target room is returned to the door closed state.

[0168] 3. If the door status detection module detects that the target room is in an open door state, the mobile robot is controlled to open the door through the door control module of the mobile robot; if the door status detection module detects that the target room is in a closed door state, the door opening and closing state is continued to be detected until the door is detected to be open and the door is opened.

[0169] From the above content, it can be seen that, on the one hand, by determining the point cloud limit angle of the target room according to the door detection distance and door detection width of the target room, the point cloud data of interest can be dynamically determined for judging the open and closed state of the room door, so that the point cloud detection range can be dynamically adjusted according to the actual delivery conditions of different rooms (such as room width, distance between the mobile robot and the door), thereby improving the detection accuracy of the open and closed state of the room door. On the second hand, by dynamically determining the point cloud data of interest in combination with the door detection width, it is avoided that different rooms use a unified point cloud detection range for room door open and closed state detection, thereby avoiding the problem of low detection accuracy of the room door open and closed state detection caused by the room being too large or too small. On the third hand, by dynamically determining the point cloud data of interest in combination with the door detection distance, it is avoided that different rooms use a unified point cloud detection range for room door open and closed state detection, thereby avoiding the problem of low detection accuracy of the room door open and closed state detection caused by the door detection distance being too large or too small.

[0170] A person of ordinary skill in the art will appreciate that all or part of the steps in the above-mentioned door opening and closing detection method may be completed by instructions, or by controlling related hardware through instructions. The instructions may be stored in a computer-readable storage medium and loaded and executed by a processor.

[0171] To this end, an embodiment of the present application provides a computer-readable storage medium, in which a plurality of computer programs are stored, and the computer program can be loaded by a processor to execute any of the door opening and closing detection methods provided in the embodiments of the present application. For example, the computer program can be loaded by a processor to execute the following steps:

[0172] Obtain the distance between the designated position point of the target room and the target room as the door detection distance of the target room; obtain the door detection width of the target room; determine the point cloud limit angle of the target room according to the door detection distance and the door detection width; when the mobile robot navigates to the designated position point, obtain each point cloud of interest in the target room according to the point cloud limit angle; based on the robot navigation posture of each point cloud of interest and the designated position point, detect the open and closed state of the door of the target room.

[0173] In some embodiments, the computer program can be loaded by a processor to perform the following steps:

[0174] When the mobile robot navigates to the designated location, the mobile robot is controlled to collect data according to the point cloud limit angle to obtain first collected point cloud data of the target room; each point cloud of the first collected point cloud data is used as a point cloud of interest of the target room.

[0175] In some embodiments, the computer program can be loaded by a processor to perform the following steps:

[0176] After the mobile robot navigates to the designated location point, the mobile robot is controlled to collect data according to a preset collection angle to obtain second collected point cloud data of the target room; each point cloud in the second collected point cloud data is traversed to obtain the angle between the current traversed point cloud and the designated location point as the discrimination angle of the current traversed point cloud; if the discrimination angle is less than or equal to the point cloud limit angle, the current traversed point cloud is used as the point cloud of interest.

[0177] In some embodiments, the computer program can be loaded by a processor to perform the following steps:

[0178] Based on the robot navigation posture of the designated position point, the first direction vector of the designated position point is obtained; based on the navigation coordinate system coordinates of each of the interest point clouds, the projection length of each of the interest point clouds on the straight line corresponding to the first direction vector is obtained as the projection length of each of the interest point clouds; according to the projection length of each of the interest point clouds and the door detection distance, the open or closed state of the door of the target room is determined.

[0179] In some embodiments, the computer program can be loaded by a processor to perform the following steps:

[0180] According to the projection length of the point cloud of interest and the door detection distance, the number of point clouds in the open state of the target room is obtained; when the number of point clouds in the open state is greater than a first preset point cloud number threshold, it is determined that the target room is in a door-open state; or, when the number of point clouds in the open state is less than or equal to the first preset point cloud number threshold, it is determined that the target room is in a door-closed state.

[0181] In some embodiments, the computer program can be loaded by a processor to perform the following steps:

[0182] The difference between the projection length of each point cloud of interest and the door detection distance is obtained as the distance difference of each point cloud of interest; according to the distance difference of each point cloud of interest and a preset distance difference threshold, the state of each point cloud of interest is determined to obtain the opening and closing indication information of each point cloud of interest; according to the opening and closing indication information of each point cloud of interest, the number of open state point clouds of the target room is obtained.

[0183] In some embodiments, the computer program can be loaded by a processor to perform the following steps:

[0184] According to the projection length of the point cloud of interest and the door detection distance, the number of point clouds in the closed state of the target room is obtained; when the number of point clouds in the closed state is less than or equal to a second preset point cloud number threshold, it is determined that the target room is in a door-open state; or, when the number of point clouds in the closed state is greater than the second preset point cloud number threshold, it is determined that the target room is in a door-closed state.

[0185] In some embodiments, the computer program can be loaded by a processor to perform the following steps:

[0186] Based on the navigation coordinate system coordinates of each of the interest point clouds and the navigation coordinate system coordinates of the designated position point, the second direction vector of each of the interest point clouds is obtained; the angle between the second direction vector of each of the interest point clouds and the second direction vector is obtained as the projection angle of each of the interest point clouds; and the projection length of each of the interest point clouds is determined according to the projection angle of each of the interest point clouds, the first direction vector and the second direction vector of each of the interest point clouds.

[0187] In some embodiments, the computer program can be loaded by a processor to perform the following steps:

[0188] When it is detected that the door of the target room is open, the mobile robot is controlled to open the door.

[0189] The computer-readable storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0190] In the above-mentioned door opening and closing detection method, computer-readable storage medium, and mobile robot embodiments, the description of each embodiment has its own emphasis. For parts not described in detail in a certain embodiment, reference can be made to the relevant description of other embodiments. Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working process and beneficial effects of the above-described computer-readable storage medium, mobile robot and its corresponding units can refer to the description of the door opening and closing detection method in the above embodiment, and will not be repeated here.

[0191] The above is a detailed introduction to a door opening and closing detection method, a mobile robot and a computer-readable storage medium provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea; at the same time, for technical personnel in this field, according to the ideas of the present application, there will be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present application.

Claims

1. A door opening and closing detection method, characterized in that: The method comprises: Obtaining the distance between the designated position point of the target room and the target room as the door detection distance of the target room; Obtaining the door detection width of the target room; Determining the point cloud limit angle of the target room according to the door detection distance and the door detection width; After the mobile robot navigates to the designated location, each point cloud of interest in the target room is obtained according to the point cloud limit angle; Based on the cloud of points of interest and the robot navigation posture of the designated position point, the door opening and closing status of the target room is detected.

2. The door opening and closing detection method according to claim 1, characterized in that: After the mobile robot navigates to the designated location, obtaining each point cloud of interest in the target room according to the point cloud limit angle includes: When the mobile robot navigates to the designated location, the mobile robot is controlled to collect data according to the point cloud limit angle to obtain first collected point cloud data of the target room; Each point cloud of the first collected point cloud data is used as a point cloud of interest of the target room.

3. The door opening and closing detection method according to claim 1, characterized in that: After the mobile robot navigates to the designated location, obtaining each point cloud of interest in the target room according to the point cloud limit angle includes: After the mobile robot navigates to the designated location, the mobile robot is controlled to collect data according to a preset collection angle to obtain second collection point cloud data of the target room; Traversing each point cloud in the second collected point cloud data, and obtaining an angle between the currently traversed point cloud and the specified position point as a determination angle of the currently traversed point cloud; If the determination angle is less than or equal to the point cloud limit angle, the currently traversed point cloud is taken as the point cloud of interest.

4. The door opening and closing detection method according to claim 1, characterized in that: The detecting the door opening and closing state of the target room based on the robot navigation posture of each of the interest point clouds and the designated position point comprises: Based on the robot navigation posture of the designated position point, obtaining a first direction vector of the designated position point; Based on the navigation coordinate system coordinates of each of the interest point clouds, obtaining the projection length of each of the interest point clouds on the straight line corresponding to the first direction vector as the projection length of each of the interest point clouds; The door opening and closing status of the target room is determined according to the projection length of each of the interest point clouds and the door detection distance.

5. The door opening and closing detection method according to claim 4, characterized in that: The step of determining the door opening and closing state of the target room according to the projection length of each of the interest point clouds and the door detection distance includes: According to the projection length of the point cloud of interest and the door detection distance, the number of the open state point clouds of the target room is obtained; When the number of the open state point clouds is greater than a first preset point cloud number threshold, it is determined that the target room is in a door open state; Alternatively, when the number of point clouds in the open state is less than or equal to a first preset point cloud number threshold, it is determined that the target room is in a door closed state.

6. The door opening and closing detection method according to claim 5, characterized in that: The acquiring the number of open state point clouds of the target room according to the projection length of the point cloud of interest and the length of the line segment corresponding to the first direction vector comprises: Obtaining the difference between the projection length of each of the interest point clouds and the door detection distance as the distance difference of each of the interest point clouds; According to the distance difference between each of the interest point clouds and a preset distance difference threshold, a state determination is performed on each of the interest point clouds to obtain opening and closing indication information of each of the interest point clouds; According to the opening and closing indication information of each of the interest point clouds, the number of open state point clouds of the target room is obtained.

7. The door opening and closing detection method according to claim 4, characterized in that: The step of determining the door opening and closing state of the target room according to the projection length of each of the interest point clouds and the door detection distance includes: According to the projection length of the point cloud of interest and the door detection distance, the number of closed state point clouds of the target room is obtained; When the number of the closed state point clouds is less than or equal to a second preset point cloud number threshold, it is determined that the target room is in a door open state; Alternatively, when the number of closed state point clouds is greater than a second preset point cloud number threshold, it is determined that the target room is in a door closed state.

8. The door opening and closing detection method according to claim 4, characterized in that: The obtaining, based on the navigation coordinate system coordinates of each of the interest point clouds, the projection length of each of the interest point clouds on the straight line corresponding to the first direction vector as the projection length of each of the interest point clouds includes: Based on the navigation coordinate system coordinates of each of the interest point clouds and the navigation coordinate system coordinates of the designated position point, obtaining a second direction vector of each of the interest point clouds; Acquire the angle between the second direction vector of each of the interest point clouds and the second direction vector as the projection angle of each of the interest point clouds; The projection length of each of the interest point clouds is determined according to the projection angle of each of the interest point clouds, the first direction vector, and the second direction vector of each of the interest point clouds.

9. The door opening and closing detection method according to claim 1, characterized in that: The method further comprises: When it is detected that the door of the target room is open, the mobile robot is controlled to open the door.

10. A mobile robot, characterized in that: The method comprises a processor and a memory, wherein the memory stores a computer program, and when the processor calls the computer program in the memory, the method for detecting the opening and closing of a door as claimed in any one of claims 1 to 9 is executed.

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