Congestion degree identification method, self-moving device and storage medium
By acquiring and analyzing the target area point cloud data collected from mobile devices, determining the degree of congestion, the problem of insufficient accuracy in identifying space congestion by mobile devices is solved, avoiding entry into congested space and collisions, and improving the accuracy of equipment identification degree and operational efficiency.
Patent Information
- Application Number
- CN202411130835.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-16
- Publication Date
- 2025-01-03
AI Technical Summary
Existing mobile devices lack accuracy when identifying space congestion, resulting in continued entry into congested space or colliding with objects, and easily misjudgment of local congestion.
By acquiring point cloud data from the target area collected by the mobile device, the number of point clouds located in the first and second areas is determined to determine the degree of congestion of the target area, avoiding the preset routes entering the congested space and reducing the risk of collision.
It improves the accuracy of identifying space congestion by mobile devices, avoids more congestion or collisions in space, and improves the operating efficiency of the equipment.
Smart Images
Figure CN119251751A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of equipment automation technology, and in particular to a congestion identification method, a self-equipping device and a storage medium. Background Art
[0002] With the rapid development of self-moving devices (such as mobile robots), the application scope of self-moving devices is becoming more and more extensive. Self-moving devices used in commercial scenarios such as hotels and buildings need to move indoors to complete corresponding tasks. However, some self-moving devices in current technologies do not have recognition functions and only move along preset routes. As a result, when the space to be entered is crowded, such as the inside of a room or an elevator, the self-moving device will continue to try to enter the space, causing collisions with objects originally placed in the space or making these spaces more crowded; some self-moving devices have recognition functions, but these recognition functions mostly recognize the nearest objects to determine the crowdedness of the space, and the recognition accuracy of the crowdedness of the entire space is insufficient. Summary of the invention
[0003] The present application provides a congestion identification method, a self-moving device and a storage medium, which solves the problem of low accuracy in identifying the congestion of a space by a self-moving device in the related art.
[0004] In a first aspect, the present application provides a method for identifying congestion, the method comprising the following steps:
[0005] Acquire point cloud data corresponding to a target area collected by a self-mobile device, the self-mobile device is outside the target area, the target area includes at least a first area and a second area, the first area is located between a current position of the self-mobile device and the second area;
[0006] Determining, in the point cloud data, a first point cloud located in the first area and a second point cloud located in the second area;
[0007] The congestion degree of the target area is determined according to the number of the first point clouds and the number of the second point clouds.
[0008] In a second aspect, the present application further provides a self-moving device, the self-moving device comprising a memory, a processor, and a point cloud acquisition sensor;
[0009] The point cloud acquisition sensor is used to collect point cloud data;
[0010] The memory is used to store computer programs;
[0011] The processor is used to execute the computer program and implement the congestion identification method provided in the first aspect when executing the computer program.
[0012] In a third aspect, the present application further provides a computer-readable storage medium, on which a computer program is stored, wherein when the computer program is executed by a processor, the steps of the above-mentioned congestion identification method are implemented.
[0013] The present application provides a method, apparatus, device and computer-readable storage medium for identifying congestion. The present application obtains point cloud data corresponding to a target area collected by a self-mobile device, determines a first point cloud located in a first area and a second point cloud located in a second area in the point cloud data, and determines the degree of congestion in the target area according to the number of the first point clouds and the number of the second point clouds, thereby avoiding the situation where the self-mobile device still enters the space along a preset route when the space to be entered is relatively crowded, causing the space to become more crowded or colliding with objects in the space; and solves the problem that the self-mobile device is prone to misjudgment when the space to be entered is partially crowded, thereby improving the recognition accuracy of the self-mobile device for the congestion of the space. BRIEF DESCRIPTION OF THE DRAWINGS
[0014] In order to more clearly illustrate the technical solutions of 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 some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.
[0015] Figure 1 A flowchart of a method for identifying congestion provided in an embodiment of the present application;
[0016] Figure 2 A schematic diagram of a target area provided in an embodiment of the present application;
[0017] Figure 3 This is a flowchart of the operation of a mobile device provided by an embodiment of the present application;
[0018] Figure 4 A schematic block diagram of the structure of a self-moving device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0019] 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 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 ordinary technicians in this field without creative work are within the scope of protection of this application.
[0020] 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.
[0021] The embodiment of the present application provides a method for identifying congestion, a self-moving device, and a storage medium. The method for identifying congestion can be applied to a self-moving device, which can be used to transport items. For example, the self-moving device is applied to a building to provide item delivery services to people in the building. It should be understood that the self-moving device can also be applied to other scenarios, which are not limited by the present application.
[0022] In conjunction with the accompanying drawings, some embodiments of the present application are described in detail below. In the absence of conflict, the following embodiments and features in the embodiments can be combined with each other.
[0023] Please refer to Figure 1 , Figure 1 A flowchart of a congestion identification method provided in an embodiment of the present application.
[0024] like Figure 1 As shown, the congestion identification method includes steps S101 to S103.
[0025] Step S101, obtaining point cloud data corresponding to a target area collected by a self-mobile device, wherein the self-mobile device is outside the target area, and the target area includes at least a first area and a second area, and the first area is located between a current position of the self-mobile device and the second area.
[0026] The target area may be a semi-enclosed space or a closable space. Taking an elevator as an example, when the self-mobile device moves toward the elevator, if the elevator door is in an open state, the self-mobile device may collect point cloud data inside the elevator car. If the elevator door is in a closed state, the self-mobile device may collect point cloud data outside the elevator. In this embodiment, when the target area is a semi-enclosed space or a closable space, the point cloud data corresponding to the target area may indicate point cloud data inside the semi-enclosed space, such as point cloud data inside the elevator car.
[0027] Exemplarily, before the self-moving device enters the target area, point cloud data corresponding to the target area is collected outside the target area, wherein the target area includes at least a first area and a second area, and the first area is located between the current position of the self-moving device and the second area. The self-moving device can determine whether the target area meets the preset entry conditions based on the collected point cloud data, thereby achieving control over the self-moving device.
[0028] In the specific implementation process, the self-mobile device is provided with a point cloud acquisition sensor, such as a laser radar, and the self-mobile device can scan the target area by driving the laser radar to obtain point cloud data corresponding to the target area. It should be understood that the self-mobile device can also obtain point cloud data by setting other sensors, which is not limited in this application. In some embodiments, when the scan is completed and the initial point cloud data is obtained, the initial point cloud data is filtered by a radius filter to filter out outlier point clouds in the initial point cloud data, and the point cloud data corresponding to the target area is obtained, which improves the accuracy of point cloud acquisition, thereby improving the accuracy of the target area congestion identification.
[0029] In some embodiments, the target area includes at least an interior area of the elevator car surrounded by the side walls of the elevator car, the first areas are all located in the interior area of the elevator car, and the second area has at least two area boundaries that overlap with the projection of the elevator car side walls on the ground inside the elevator car.
[0030] For example, the mobile device needs to identify the congestion level inside the elevator car. Specifically, the target area at least includes the elevator car internal area surrounded by the side walls of the elevator car, the first area is located in the elevator car internal area, and the second area has at least two area boundaries that overlap with the projection of the side walls of the elevator car on the ground inside the elevator car.
[0031] See also Figure 2 , Figure 2 A schematic diagram of a target area provided in one embodiment of the present application.
[0032] like Figure 2 As shown, Figure 2 Area A in the figure is used to indicate the first area, and area B is used to indicate the second area. It should be understood that the elevator door is located on the side of the first area away from the second area, and the mobile device is located outside the elevator to collect point cloud data of the internal area of the elevator car to determine whether the elevator meets the preset entry conditions.
[0033] It is understandable that when the self-mobile device collects point cloud data of the internal area of the elevator car outside the elevator, the elevator door is in an open state. There are many ways to detect whether the elevator door is open, such as installing a photoelectric sensor or other sensor that can detect the state of the elevator door on the elevator to detect the opening and closing state of the elevator door, and then the robot communicates with the elevator to obtain the opening and closing state of the elevator door, and when the elevator door is in an open state, the laser radar on it is driven to scan the internal area of the elevator to obtain the point cloud data corresponding to the internal area of the elevator car. For example, the opening and closing of the elevator door can be detected by various sensors configured on the self-mobile device, and the various sensors can be depth sensors, photoelectric sensors, etc.
[0034] Step S102: determining a first point cloud located in the first area and a second point cloud located in the second area in the point cloud data.
[0035] Exemplarily, since the target area includes at least the first area and the second area, a first point cloud located in the first area and a second point cloud located in the second area can be determined in the collected point cloud data.
[0036] In the specific implementation process, after the mobile device collects point cloud data of the target area, the first point cloud located in the first area and the second point cloud located in the second area are determined through the positions of the first area and the second area in the target area, and the distribution status of the point cloud in the target area.
[0037] In other implementations, the self-moving device can also collect the first point cloud corresponding to the first area and the second point cloud corresponding to the second area respectively. Specifically, taking the target area as the internal area of the elevator car as an example, in the process of collecting point clouds, the point cloud collection range is controlled to achieve the purpose of collecting the first point cloud and the second point cloud. For example, when collecting the first point cloud corresponding to the first area, the farthest distance in the collection range of the control point cloud is the distance between the boundary of the first area away from the self-moving device and the self-moving device; and when collecting the second point cloud corresponding to the second area, the farthest distance in the collection range can be set to be greater than the distance between the side wall of the elevator car away from the self-moving device and the self-moving device. By setting the collection range, the side walls of the elevator car can be detected as much as possible when the elevator is idle, and people or objects in the internal area of the elevator can be detected as much as possible when the elevator is relatively crowded, thereby improving the accuracy of determining the degree of congestion in the target area based on the first point cloud and the second point cloud.
[0038] In the specific implementation process, the target area, the first area and the second area are all rectangular areas. Taking the length of the first boundary of the target area as L1 and the length of the second boundary as L2 as an example, the target area is symmetrical about the straight line perpendicular to the elevator door, the length of the boundary parallel to the first boundary in the first area is 0.8L1, the length of the other boundary of the first area is 0.25L1, and the shortest distance between the first area and the target area is 0.1L1; the length of the boundary parallel to the first boundary in the second area is L1, and the length of the boundary parallel to the second boundary is 0.3L2.
[0039] The specific values of the positions and sizes of the first area and the second area provided above are only for illustration, and the present application does not limit the specific positions and sizes of the first area and the second area.
[0040] During the process of point cloud collection of the second area by the mobile device, the point cloud collection length of the boundary parallel to the first boundary in the second area by the mobile device is greater than the length of the boundary parallel to the first boundary in the second area, and / or, the point cloud collection length of the boundary parallel to the second boundary in the second area by the mobile device is greater than the length of the boundary parallel to the second boundary in the second area. For example, the point cloud collection length of the boundary parallel to the first boundary in the second area is 1.2L1, and the point cloud collection length of the boundary parallel to the second boundary in the second area is 0.4L2, wherein 1.2L1 is used to indicate that both sides of the second area in the direction of the first boundary are extended by 0.1L1, respectively, so that the point cloud collection length of the boundary is slightly greater than the boundary length of the target area, so as to reduce the influence of the location of the mobile device on the point cloud data.
[0041] Exemplarily, the size of the first area and the second area can be determined according to the size of the target area. When the first area and the second area are placed at two relative boundaries of the target area and their ground projections do not overlap, the first area can shrink relative to the size of the target area, and the second area can expand relative to the size of the target area; wherein, shrinking is used to indicate that the first area shrinks relative to the second boundary of the target area in the direction of the first boundary, and expanding is used to indicate that the second area expands relative to the second boundary of the target area in the direction of the first boundary, that is, by shortening the length of the boundary parallel to the first boundary in the first area, the shrinkage of the first area is achieved; and by increasing the length of the boundary parallel to the first boundary in the second area, the expansion of the second area is achieved. Of course, more areas can be set on this basis to assist in determining the degree of congestion in the target area, such as setting a third area, a fourth area, etc. The specific number of areas set can be determined according to the size and carrying conditions of the target area. Wherein, if a third area and a fourth area are set, the third area and the fourth area can be preferentially placed at the other two relative boundaries of the target area.
[0042] By setting the first area and the second area, the recognition accuracy of the congestion level of the target area is improved.
[0043] Step S103: Determine the degree of congestion of the target area according to the number of the first point clouds and the number of the second point clouds.
[0044] Exemplarily, the first area is an area close to the entry and exit of the target area. When the number of first point clouds is large, it is determined that the target area is relatively crowded. For example, taking an elevator as an example, when there are many people or even it is crowded with people, there will be more people standing near the elevator door (first area). Therefore, the number of first point clouds collected will also be large, and because the people in the first area block the people inside the elevator, the number of second point clouds collected will be small; and when the number of first point clouds is small, it is determined that the target area is relatively idle. For example, when there are few people or even no people in the elevator, the number of first point clouds is close to zero or zero, and the number of second point clouds will be relatively large (point clouds corresponding to people inside the elevator or the side walls of the elevator), and then the degree of congestion of the target area can be determined according to the number of first point clouds and the number of second point clouds.
[0045] In some embodiments, determining the degree of congestion of the target area based on the number of the first point clouds and the number of the second point clouds includes: determining the degree of congestion of the target area based on the number of the first point clouds, the number of the second point clouds and a preset threshold, wherein the degree of congestion includes at least a first degree of congestion and a second degree of congestion, and the first degree of congestion is greater than the second degree of congestion.
[0046] Exemplarily, by setting a preset threshold, the degree of congestion of the target area is determined based on the comparison result of the number of the first point cloud and the preset threshold, and the comparison result of the number of the second point cloud and the preset threshold, wherein the first degree of congestion is used to indicate that the target area is in a relatively crowded state, and the second degree of congestion is used to indicate that the target area is in a relatively idle state.
[0047] In the specific implementation process, the same preset threshold is set to compare with the number of the first point cloud and the number of the second point cloud; or different preset thresholds are set to compare with the number of the first point cloud and the number of the second point cloud respectively. This application does not limit whether to set the same preset threshold.
[0048] In some embodiments, determining the degree of congestion of the target area based on the number of the first point clouds, the number of the second point clouds and a preset threshold includes: when the number of the first point clouds is greater than or equal to a first quantity threshold and the number of the second point clouds is less than or equal to a second quantity threshold, determining that the degree of congestion of the target area is the first degree of congestion.
[0049] Exemplarily, when different preset thresholds are set to be compared with the number of the first point cloud and the number of the second point cloud, respectively, the preset thresholds include a first number threshold and a second number threshold. If the number of the first point cloud is greater than or equal to the first number threshold, and the number of the second point cloud is less than or equal to the second number threshold, it is determined that the congestion degree of the target area is the first congestion degree, that is, the target area is in a relatively crowded state.
[0050] It should be understood that, taking the target area as the internal area of the elevator car as an example, when the number of first point clouds is greater than or equal to the first quantity threshold, it is determined that more people are standing and / or more objects are placed in the first area, and because there are more people and / or objects in the first area, during the point cloud collection process, the people and / or objects located in the first area block the people and / or objects located in the second area, so only a small part of the point cloud data can be detected in the second area, which in turn causes the number of second point clouds to be less than or equal to the second quantity threshold. Therefore, when the number of first point clouds is greater than or equal to the first quantity threshold, and the number of second point clouds is less than or equal to the second quantity threshold, it is determined that the target area is in a relatively crowded state.
[0051] When the number of the first point clouds is less than the first number threshold, or the number of the second point clouds is greater than the second number threshold, the congestion level of the target area is determined to be the second congestion level.
[0052] It should be understood that when there are fewer people and / or objects in the first area, the number of collected first point clouds is less than the first quantity threshold, and since there is no occlusion of people and / or objects in the first area, people, objects and / or elevator side walls in the second area can be detected, which leads to the number of collected second point clouds being greater than the second quantity threshold; therefore, when the number of first point clouds is less than the first quantity threshold or the number of second point clouds is greater than the second quantity threshold, it is determined that the target area is in an idle state.
[0053] In some embodiments, before obtaining point cloud data corresponding to the target area collected from the mobile device, the method also includes: obtaining an upper limit value of the number of target objects in the first area and a lower limit value of the number of target objects in the second area; determining the upper limit value as a first quantity threshold, and determining the lower limit value as a second quantity threshold.
[0054] Exemplarily, the first quantity threshold and the second quantity threshold can be determined according to the actual situation of the scene corresponding to the target area. In the specific implementation process, the target object is a person taking an elevator, and multiple target objects are allowed to stand randomly inside the elevator for multiple times, and the number of the first point cloud collected during the i-th random standing is recorded as N1(i), and the number of the second point cloud is recorded as N2(i). The first quantity threshold and the second quantity threshold are determined by the following formula:
[0055] T1=max[N1(i)]
[0056] T2=min[N2(i)]
[0057] Among them, T1 is used to indicate the first quantity threshold, and T2 is used to indicate the second quantity threshold; that is, the above formula indicates that the first quantity threshold is the maximum value of the number of first point clouds recorded in multiple random positions, and the second quantity threshold is the minimum value of the number of second point clouds recorded in multiple random positions. The number of target objects is determined according to the size of the target area, and this application does not limit it.
[0058] By setting the first quantity threshold and the second quantity threshold, the congestion recognition method can recognize the congestion of target areas of different sizes, thereby improving the applicability of the method and the recognition accuracy of the congestion.
[0059] In other embodiments, determining the degree of congestion of the target area based on the number of the first point clouds, the number of the second point clouds and a preset threshold includes: determining the sum of the number of the first point clouds and the number of the second point clouds to obtain the total number of point clouds; when the ratio of the number of the first point clouds to the total number of point clouds is greater than or equal to a first point cloud ratio threshold, and the ratio of the number of the second point clouds to the total number of point clouds is less than or equal to a second point cloud ratio threshold, determining the degree of congestion of the target area to be the first degree of congestion; when the ratio of the number of the first point clouds to the total number of point clouds is less than the first point cloud ratio threshold, or the ratio of the number of the second point clouds to the total number of point clouds is greater than the second point cloud ratio threshold, determining the degree of congestion of the target area to be the second degree of congestion.
[0060] Exemplarily, the preset thresholds include a first point cloud ratio threshold and a second point cloud ratio threshold. After determining the number of the first point cloud and the number of the second point cloud, the sum of the number of the first point cloud and the number of the second point cloud is calculated to obtain the total number of point clouds, and when the ratio of the number of the first point cloud to the total number of point clouds is greater than or equal to the first point cloud ratio threshold, and when the ratio of the number of the second point cloud to the total number of point clouds is less than or equal to the second point cloud ratio threshold, it is determined that the target area is in a relatively crowded state. It should be understood that if the ratio of the number of the first point cloud to the total number of point clouds is less than the first point cloud ratio threshold, or the ratio of the number of the second point cloud to the total number of point clouds is greater than the second point cloud ratio threshold, it is determined that the target area is in a relatively idle state. Among them, the first point cloud ratio threshold and the second point cloud ratio threshold can be set according to actual conditions.
[0061] The total number of point clouds is obtained by calculating the sum of the number of the first point cloud and the number of the second point cloud, and then the congestion of the target area is determined by comparing the ratio of the number of the first point cloud to the total number of point clouds with the first point cloud ratio threshold, as well as the ratio of the number of the second point cloud to the total number of point clouds with the second point cloud ratio threshold. This avoids direct statistics and comparison of the number of point clouds, and also avoids the influence of the change in the number of point clouds as the distance between the self-moving device and the target area changes, making the method more robust.
[0062] In some embodiments, before obtaining point cloud data corresponding to the target area collected from the mobile device, the method also includes: obtaining an upper limit value of the number of target objects in the first area and a lower limit value of the number of target objects in the second area; determining the sum of the upper limit value and the lower limit value to obtain a target value; determining a ratio of the upper limit value to the target value as the first point cloud ratio threshold, and determining a ratio of the lower limit value to the target value as the second point cloud ratio threshold.
[0063] In the specific implementation process, the target object is a person taking the elevator. Let multiple target objects stand randomly inside the elevator for multiple times, and record the number of the first point cloud collected at the i-th random standing position as
[0064] N3(i), the number of the second point cloud is N4(i), and the first point cloud ratio threshold and the second point cloud ratio threshold are determined by the following formula:
[0065]
[0066] Among them, t1 is used to indicate the first point cloud ratio threshold, t2 is used to indicate the second point cloud ratio threshold; max[N3(i)] is the maximum value of the number of first point clouds recorded in multiple random positions of the target object, min[N4(i)] is the minimum value of the number of second point clouds recorded in multiple random positions of the target object, and the number of target objects is determined according to the size of the target area, which is not limited in this application.
[0067] In some embodiments, after determining the congestion level of the target area, the method further includes: controlling the movement state of the self-moving device according to the congestion level of the target area.
[0068] For example, after the mobile device determines the congestion level of the target area, the movement state of the mobile device can be controlled according to the congestion level of the target area to avoid directly entering the target area via a preset route, which may cause the target area to become more congested or cause a collision in the target area.
[0069] In some embodiments, after determining the degree of congestion in the target area, the method further includes: when the degree of congestion is a second degree of congestion, controlling the self-mobile device to move to the target area; when the degree of congestion is a first degree of congestion, controlling the self-mobile device to stay at the current position.
[0070] Exemplarily, when the congestion level is the second congestion level indicating that the target area is relatively idle, the mobile device is controlled to move to the target area, and if the congestion level is the first congestion level indicating that the target area is relatively crowded, the mobile device is controlled to stay at the current position.
[0071] In the specific implementation process, the target area is taken as an example to illustrate that after determining the crowdedness of the elevator, it is determined whether the elevator meets the preset entry conditions according to the determined crowdedness. For example, when the crowdedness of the elevator is determined to be the second crowdedness, it is determined that the elevator meets the preset entry conditions, and when the crowdedness of the elevator is determined to be the first crowdedness, it is determined that the elevator does not meet the preset entry conditions, thereby realizing the control of whether the self-moving device enters the elevator according to the crowdedness of the elevator, and the method provided by the above embodiment can effectively improve the recognition accuracy of the crowdedness of the elevator, thereby improving the operation efficiency of the self-moving device itself.
[0072] In some embodiments, when it is determined that the elevator meets the preset entry conditions, the self-moving device is controlled to enter the elevator and stay, and the self-moving device is controlled to move out of the elevator after the elevator reaches the target floor; when it is determined that the elevator does not meet the preset entry conditions, the self-moving device is controlled to stay at the current position.
[0073] For example, a mobile device can take an elevator to different floors in a building to provide item delivery services to residents on different floors in the building.
[0074] See also Figure 3 , Figure 3 This is a flowchart of the operation of a mobile device provided by one embodiment of the present application.
[0075] In the specific implementation process, the operation status of the self-mobile device includes the following steps:
[0076] S201, move to the elevator waiting area in front of the elevator to wait for the elevator to arrive, so as to complete the loading of the items to be transported, or complete the transportation task of the items. During this process, the self-moving device can plan from the current position of the self-moving device to the position waiting for the elevator.
[0077] S202: When the mobile device detects that the elevator door is open, the congestion level of the elevator interior area is identified using the method provided in the above embodiment.
[0078] S203: Determine whether the elevator meets preset entry conditions based on the recognition result of the congestion degree.
[0079] S204. When it is determined that the elevator meets the preset entry conditions according to the recognition result of the congestion degree, the self-moving device is controlled to enter the elevator and stay, and the self-moving device is controlled to leave the elevator after the elevator reaches the target floor, thereby completing the up and down floor operation of the self-moving device.
[0080] S205. If it is determined according to the recognition result of the congestion degree that the elevator does not meet the preset entry conditions, the self-moving device is controlled to stay at the current position and continue to wait for the next arrival of the elevator. This avoids the inconvenience caused to the passengers in the elevator by the self-moving device still entering the elevator when the elevator is crowded, and when there is space inside the elevator but the positions of the passengers standing are relatively concentrated, the self-moving device can also identify the presence of a stop position inside the elevator and enter the elevator, thereby improving the operation efficiency of the equipment.
[0081] In some embodiments, controlling the self-moving device to move to the target area includes: in the process of controlling the self-moving device to move to the target area, adjusting the staying position of the self-moving device in the target area according to the point cloud data.
[0082] Exemplarily, when determining that the elevator meets preset entry conditions and when a mobile device moves to the interior area of the elevator, the route of the mobile device when moving to the interior area of the elevator and / or its stop position in the interior area of the elevator are adjusted through pre-collected point cloud data or currently collected point cloud data to reduce the impact on people riding the elevator.
[0083] For example, when it is determined based on the point cloud data that the space inside the elevator is relatively spacious, an area with relatively scattered or less point cloud data can be determined from the interior space of the elevator according to the spatial distribution of the point cloud data as the stopping position of the self-moving device. Then, the self-moving device can adjust its relative posture before entering the elevator so that it can stay in the stopping position after entering the elevator.
[0084] The congestion identification method provided in the above embodiment obtains point cloud data corresponding to the target area collected by the self-mobile device, determines a first point cloud located in the first area and a second point cloud located in the second area in the point cloud data, and determines the degree of congestion of the target area according to the number of the first point cloud and the number of the second point cloud, thereby avoiding the situation where the self-mobile device still enters the space along a preset route when the space corresponding to the target area to be entered is relatively crowded, causing the space to become more crowded or colliding with objects in the space; and solves the problem of low operating efficiency of the self-mobile device due to the self-mobile device making a wrong judgment and failing to enter the space when the space to be entered is partially crowded or the preset position of the self-mobile device is occupied but the space is still relatively idle, thereby improving the recognition accuracy of the self-mobile device for the congestion of the space and improving the operating efficiency of the self-mobile device.
[0085] See also Figure 4 , Figure 4 A schematic block diagram of the structure of a self-moving device provided in an embodiment of the present application.
[0086] like Figure 4 As shown, the self-mobile device 300 includes a point cloud acquisition sensor 301, a memory 302 and a processor 303 connected via a system bus.
[0087] The point cloud acquisition sensor 301 is used to collect point cloud data.
[0088] The memory 302 includes a storage medium that can store an operating system and a computer program. The computer program includes program instructions, and when the program instructions are executed, the processor 303 can execute any one of the congestion identification methods.
[0089] The processor 303 is used to provide computing and control capabilities to support the operation of the entire mobile device 300 .
[0090] Those skilled in the art will understand that Figure 4 The structure shown in the figure is merely a block diagram of a partial structure related to the scheme of the present application, and does not constitute a limitation on the self-moving device 300 to which the scheme of the present application is applied. The specific self-moving device 300 may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.
[0091] It should be understood that the processor may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate 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.
[0092] In one embodiment, the processor is used to run a computer program stored in the memory to implement the following steps:
[0093] Acquire point cloud data corresponding to a target area collected by a self-mobile device, the self-mobile device is outside the target area, the target area includes at least a first area and a second area, the first area is located between a current position of the self-mobile device and the second area;
[0094] Determining, in the point cloud data, a first point cloud located in the first area and a second point cloud located in the second area;
[0095] The congestion degree of the target area is determined according to the number of the first point clouds and the number of the second point clouds.
[0096] In one embodiment, when the processor determines the degree of crowding of the target area according to the number of the first point cloud and the number of the second point cloud, the processor is used to implement:
[0097] The congestion degree of the target area is determined according to the number of the first point clouds, the number of the second point clouds and a preset threshold, wherein the congestion degree includes at least a first congestion degree and a second congestion degree, and the first congestion degree is greater than the second congestion degree.
[0098] In one embodiment, the preset threshold includes a first quantity threshold and a second quantity threshold, and the processor, when determining the degree of congestion of the target area according to the number of the first point cloud, the number of the second point cloud and the preset threshold, is used to implement:
[0099] When the number of the first point clouds is greater than or equal to a first number threshold, and the number of the second point clouds is less than or equal to a second number threshold, determining the congestion degree of the target area to be the first congestion degree;
[0100] When the number of the first point clouds is less than the first number threshold, or the number of the second point clouds is greater than the second number threshold, the congestion level of the target area is determined to be the second congestion level.
[0101] In one embodiment, before obtaining the point cloud data corresponding to the target area collected from the mobile device, the processor is further configured to:
[0102] Acquire an upper limit value of the number of target objects in the first area and a lower limit value of the number of target objects in the second area;
[0103] The upper limit value is determined as a first quantity threshold, and the lower limit value is determined as a second quantity threshold.
[0104] In one embodiment, the preset threshold includes a first point cloud ratio threshold and a second point cloud ratio threshold; when the processor determines the degree of congestion of the target area according to the number of the first point cloud, the number of the second point cloud and the preset threshold, it is used to implement:
[0105] Determine the sum of the number of the first point cloud and the number of the second point cloud to obtain a total number of point clouds;
[0106] When the ratio of the number of the first point clouds to the total number of point clouds is greater than or equal to a first point cloud ratio threshold, and the ratio of the number of the second point clouds to the total number of point clouds is less than or equal to a second point cloud ratio threshold, determining that the congestion degree of the target area is the first congestion degree;
[0107] When the ratio of the number of the first point clouds to the total number of point clouds is less than the first point cloud ratio threshold, or the ratio of the number of the second point clouds to the total number of point clouds is greater than the second point cloud ratio threshold, the congestion level of the target area is determined to be the second congestion level.
[0108] In one embodiment, before obtaining the point cloud data corresponding to the target area collected from the mobile device, the processor is further configured to:
[0109] Acquire an upper limit value of the number of target objects in the first area and a lower limit value of the number of target objects in the second area;
[0110] Determine the sum of the upper limit value and the lower limit value to obtain a target value;
[0111] The ratio of the upper limit value to the target value is determined as the first point cloud ratio threshold, and the ratio of the lower limit value to the target value is determined as the second point cloud ratio threshold.
[0112] In one embodiment, after determining the congestion level of the target area, the processor is further configured to:
[0113] When the congestion level is a second congestion level, controlling the mobile device to move to the target area;
[0114] When the congestion level is the first congestion level, the self-moving device is controlled to stay at the current position.
[0115] In one embodiment, when controlling the mobile device to move to the target area, the processor is used to implement:
[0116] In the process of controlling the self-moving device to move to the target area, the staying position of the self-moving device in the target area is adjusted according to the point cloud data.
[0117] It should be noted that those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the mobile device described above can refer to the corresponding process in the aforementioned congestion identification method embodiment, and will not be repeated here.
[0118] An embodiment of the present application further provides a computer-readable storage medium, on which a computer program is stored. The computer program includes program instructions. The method implemented when the program instructions are executed can refer to the various embodiments of the congestion identification method of the present application.
[0119] The computer-readable storage medium may be an internal storage unit of the self-mobile device described in the foregoing embodiments, such as a hard disk or memory of the self-mobile device. The computer-readable storage medium may also be an external storage device of the self-mobile device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc., equipped on the self-mobile device.
[0120] It should be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.
[0121] It should also be understood that the term "and / or" used in the specification of this application and the appended claims refers to any combination of one or more of the associated listed items and all possible combinations, including these combinations. It should be noted that, in this article, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or system 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, article or system. In the absence of further restrictions, an element defined by the sentence "including a..." does not exclude the presence of other identical elements in the process, method, article or system including the element.
[0122] The serial numbers of the embodiments of the present application are for description only and do not represent the advantages and disadvantages of the embodiments. The above description is only a specific implementation mode of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of various equivalent modifications or replacements within the technical scope disclosed in the present application, and these modifications or replacements should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.
Claims
1. A method for identifying congestion, characterized in that: The method comprises: Acquire point cloud data corresponding to a target area collected by a self-mobile device, the self-mobile device is outside the target area, the target area includes at least a first area and a second area, the first area is located between a current position of the self-mobile device and the second area; Determining, in the point cloud data, a first point cloud located in the first area and a second point cloud located in the second area; The congestion degree of the target area is determined according to the number of the first point clouds and the number of the second point clouds.
2. The method for identifying congestion according to claim 1, characterized in that: The determining the degree of congestion of the target area according to the number of the first point cloud and the number of the second point cloud includes: The congestion degree of the target area is determined according to the number of the first point clouds, the number of the second point clouds and a preset threshold, wherein the congestion degree includes at least a first congestion degree and a second congestion degree, and the first congestion degree is greater than the second congestion degree.
3. The method for identifying congestion according to claim 2, characterized in that: The preset threshold includes a first quantity threshold and a second quantity threshold; and determining the degree of congestion of the target area according to the quantity of the first point cloud, the quantity of the second point cloud and the preset threshold includes: When the number of the first point clouds is greater than or equal to a first number threshold, and the number of the second point clouds is less than or equal to a second number threshold, determining the congestion degree of the target area to be the first congestion degree; When the number of the first point clouds is less than the first number threshold, or the number of the second point clouds is greater than the second number threshold, the congestion level of the target area is determined to be the second congestion level.
4. The method for identifying congestion according to claim 3, characterized in that: Before acquiring the point cloud data corresponding to the target area collected from the mobile device, the method further includes: Acquire an upper limit value of the number of target objects in the first area and a lower limit value of the number of target objects in the second area; The upper limit value is determined as the first quantity threshold, and the lower limit value is determined as the second quantity threshold.
5. The method for identifying congestion according to claim 2, characterized in that: The preset threshold includes a first point cloud ratio threshold and a second point cloud ratio threshold; The determining the degree of congestion of the target area according to the number of the first point clouds, the number of the second point clouds and a preset threshold comprises: Determine the sum of the number of the first point cloud and the number of the second point cloud to obtain a total number of point clouds; When the ratio of the number of the first point clouds to the total number of point clouds is greater than or equal to a first point cloud ratio threshold, and the ratio of the number of the second point clouds to the total number of point clouds is less than or equal to a second point cloud ratio threshold, determining that the congestion degree of the target area is the first congestion degree; When the ratio of the number of the first point clouds to the total number of point clouds is less than the first point cloud ratio threshold, or the ratio of the number of the second point clouds to the total number of point clouds is greater than the second point cloud ratio threshold, the congestion level of the target area is determined to be the second congestion level.
6. The method for identifying congestion according to claim 5, characterized in that: Before acquiring the point cloud data corresponding to the target area collected from the mobile device, the method further includes: Acquire an upper limit value of the number of target objects in the first area and a lower limit value of the number of target objects in the second area; Determine the sum of the upper limit value and the lower limit value to obtain a target value; The ratio of the upper limit value to the target value is determined as the first point cloud ratio threshold, and the ratio of the lower limit value to the target value is determined as the second point cloud ratio threshold.
7. The method for identifying congestion according to any one of claims 1 to 6, characterized in that: After determining the degree of congestion in the target area, the method further includes: When the congestion level is a second congestion level, controlling the mobile device to move to the target area; When the congestion level is the first congestion level, the self-moving device is controlled to stay at the current position.
8. The method for identifying congestion according to claim 7, characterized in that: The controlling the mobile device to move to the target area includes: In the process of controlling the self-moving device to move to the target area, the staying position of the self-moving device in the target area is adjusted according to the point cloud data.
9. The method for identifying congestion according to any one of claims 1 to 6, characterized in that: The target area at least includes an interior area of the elevator car surrounded by side walls of the elevator car, the first areas are all located in the interior area of the elevator car, and the second area has at least two area boundaries that overlap with the projection of the side walls of the elevator car on the ground inside the elevator car.
10. A self-propelled device, characterized in that: The self-mobile device includes a memory, a processor, and a point cloud acquisition sensor; The point cloud acquisition sensor is used to collect point cloud data; The memory is used to store computer programs; The processor is configured to execute the computer program and implement the congestion identification method according to any one of claims 1 to 9 when executing the computer program.
11. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the processor implements the congestion degree identification method according to any one of claims 1 to 9.