Parking lot parking space management method and device, equipment and medium

Abstract

The invention discloses a parking lot parking space management method and device, equipment and a medium. The method comprises the following steps: preprocessing point clouds collected by a main laser radar and an auxiliary laser radar to obtain a target point cloud; the main laser radar and the auxiliary laser radar are deployed at different positions of the parking lot; clustering the target point clouds to obtain obstacle point clouds in the parking lot space; determining idle parking spaces in the parking lot according to the intrusion condition of the obstacle point cloud to the grids on the parking spaces; at least two grids are drawn on each parking space in advance. According to the embodiment of the invention, the accuracy of parking space occupation detection can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent parking, and in particular, to a method, device, equipment and medium for managing parking spaces in a parking lot. Background Art

[0002] The occupancy status of parking spaces in a parking lot can help drivers find available parking spaces in time or find other parking lots in time when there are no available parking spaces, avoiding wasting time. Currently, most of the automatic detection of available parking spaces uses computer vision. However, this method is greatly affected by light conditions and occlusion outdoors, and its detection ability has limitations.

[0003] Compared with cameras, lidar has the advantages of strong anti-light influence ability and accurate position measurement, and is more suitable for real-time detection of targets in outdoor open conditions. By detecting obstacles in the parking lot and combining information such as the position and size of parking spaces in the parking lot, it is possible to determine whether a parking space is available and update it in real time. It has the advantages of accurate detection, strong real-time performance, and high degree of automation.

[0004] Although the theoretical upper limit of lidar-based parking space occupancy detection is higher, the processing of point cloud data is more difficult. Compared with mature image detection and recognition technologies, there is no obvious gap in detection accuracy. Coupled with the high cost of lidar, it is difficult to promote the lidar-based parking space occupancy detection scheme. Summary of the Invention

[0005] The present invention provides a method, device, equipment and medium for managing parking spaces in a parking lot to improve the detection accuracy of the lidar-based parking space occupancy detection scheme.

[0006] According to one aspect of the present invention, there is provided a method for managing parking spaces in a parking lot, including:

[0007] Preprocessing the point clouds collected by the main lidar and the auxiliary lidar to obtain target point clouds; the main lidar and the auxiliary lidar are deployed at different positions in the parking lot;

[0008] Clustering the target point clouds to obtain obstacle point clouds in the parking lot space;

[0009] Determining the available parking spaces in the parking lot according to the intrusion situation of the grids on the parking spaces into the obstacle point clouds; at least two grids are pre-drawn on each parking space.

[0010] According to another aspect of the present invention, there is provided a device for managing parking spaces in a parking lot, including:

[0011] A preprocessing module for preprocessing the point clouds collected by the main lidar and the auxiliary lidar to obtain target point clouds; the main lidar and the auxiliary lidar are deployed at different positions in the parking lot;

[0012] A clustering module for clustering the target point clouds to obtain obstacle point clouds in the parking lot space;

[0013] A determination module for determining the available parking spaces in the parking lot according to the intrusion of the obstacle point clouds into the grids on the parking spaces; at least two grids are pre-drawn on each parking space.

[0014] According to another aspect of the present invention, there is provided a computer program product including a computer program which, when executed by a processor, implements the method for managing parking spaces in the parking lot according to any embodiment of the present invention.

[0015] According to another aspect of the present invention, there is provided an electronic device including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, the at least one processor is enabled to execute the method for managing parking spaces in the parking lot according to any embodiment of the present invention.

[0016] According to another aspect of the present invention, there is provided a computer-readable storage medium storing computer instructions for causing a processor to implement the method for managing parking spaces in the parking lot according to any embodiment of the present invention when executed.

[0017] In the embodiments of the present invention, by analyzing the intrusion of the obstacle point clouds into the grids, the influence of the obstacles on the parking spaces is indirectly inferred, so as to determine whether the parking spaces are available parking spaces, solving the problems of inaccurate detection caused by noise, other point cloud interference points, and distortion of the obstacle detection bounding box, improving the accuracy of parking space occupancy detection, and making it easier to popularize the parking space occupancy detection based on lidar.

[0018] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present invention, nor is it used to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. Description of the Drawings

[0019] To more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the accompanying drawings required for the description of the embodiments. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0020] Figure 1 is a flowchart of a method for managing parking spaces in a parking lot according to an embodiment of the present invention;

[0021] Figure 2 is a flowchart of a method for managing parking spaces in a parking lot according to another embodiment of the present invention;

[0022] Figure 3 is a schematic structural diagram of a device for managing parking spaces in a parking lot according to another embodiment of the present invention;

[0023] Figure 4 is a schematic structural diagram of an electronic device for implementing the embodiments of the present invention. Detailed implementation manners

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the following clearly and completely describes the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, rather than all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0025] It should be noted that the terms "first", "second", etc. in the present invention are used to distinguish similar objects and do not necessarily describe a specific order or sequence. It should be understood that such used data can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units does not necessarily have to be limited to those clearly listed steps or units, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0026] Figure 1The flowchart of a management method for parking spaces in a parking lot provided by an embodiment of the present invention. This embodiment is applicable to installing lidar in a parking lot and detecting the availability of parking spaces in the parking lot through the point cloud collected by the lidar. This method can be executed by a management device for parking spaces in the parking lot, and the device can be implemented in the form of hardware and / or software. The device can be configured in an electronic device with corresponding data processing capabilities, such as a parking space management system in a parking lot. As Figure 1 shown, the method includes:

[0027] S110. Preprocess the point clouds collected by the main lidar and the auxiliary lidar to obtain target point clouds.

[0028] S120. Cluster the target point clouds to obtain obstacle point clouds in the parking lot space.

[0029] S130. Determine the available parking spaces in the parking lot according to the intrusion situation of the grids on the parking spaces by the obstacle point clouds.

[0030] Among them, the main lidar and the auxiliary lidar are deployed at different positions in the parking lot, and at least two grids are pre-drawn on each parking space.

[0031] Specifically, according to the different sizes and regular shapes of the parking lot, select one main lidar and at least two auxiliary lidars. The lidar can be a mechanical lidar, a semi-solid-state lidar, or a solid-state lidar. Fix each lidar above the parking lot through a device pole or other means and adjust its angle. Due to the differences in parking lots, the installation criterion for the lidar is that the configured lidar field of view should be able to cover all the parking spaces to be detected in the parking lot. After installation, calibrate the external parameters of the auxiliary lidar and the main lidar for subsequent processing. Select a suitable point on the ground of the parking lot as the coordinate origin and establish a field center (ENU) coordinate system, that is, select the east-north-up coordinate system as the reference system for subsequent point cloud analysis and parking lot mapping. In the constructed ENU coordinate system, draw the overall area plane map of the parking lot on the XOY projection plane through methods such as drone measurement, lidar measurement, and satellite map measurement. Measure several parking lot boundary points and construct the parking lot boundary by curve fitting to draw the parking lot map. Similarly, draw the plane map of the parking space area on the XOY projection plane of the coordinate system by measuring the corresponding positions of the four corner points of each parking space. Draw at least two equally spaced grids (the spacing can be appropriately adjusted according to the size of the parking space) on each parking space area on the XOY projection plane of the coordinate system at a certain interval, and the preparatory work is completed.

[0032] During the management process, the main lidar and the auxiliary lidar cross-collect the point cloud of the parking lot to ensure that all parking spaces are covered. The point clouds collected by different lidars are preprocessed, such as point cloud fusion, stitching, filtering, etc., and finally the target point cloud that can be used is obtained. Euclidean clustering is performed on the target point cloud to obtain clustering clusters belonging to different obstacles, and the point clouds in the clustering clusters are the obstacle point clouds. In the past, when dealing with obstacle point clouds, it was customary to treat the clustering cluster as a whole, that is, according to the obstacle point cloud in the clustering cluster, an obstacle bounding box corresponding to the clustering cluster was constructed, and whether the obstacle would affect the vehicle's parking in the parking space was analyzed based on the obstacle bounding box. However, due to the uneven distribution of the obstacle point clouds within the obstacle bounding box, it is difficult to solve problems such as detection inaccuracies caused by noise, other point cloud interference points, and the distortion of the obstacle detection envelope box. Therefore, the present invention abandons the idea of constructing an obstacle bounding box based on the obstacle point cloud, draws multiple grids on the parking space area, and indirectly infers the impact of the obstacle on the parking space by analyzing the intrusion of the obstacle point cloud into the grids, so as to determine whether the parking space is an idle parking space.

[0033] In the embodiment of the present invention, by analyzing the intrusion of the obstacle point cloud into the grids, the impact of the obstacle on the parking space is indirectly inferred, so as to determine whether the parking space is an idle parking space, solve problems such as detection inaccuracies caused by noise, other point cloud interference points, and the distortion of the obstacle detection envelope box, improve the accuracy of parking space occupancy detection, and make it easier to promote the parking space occupancy detection based on lidar.

[0034] Figure 2 The flowchart of a method for managing parking spaces in a parking lot provided by another embodiment of the present invention is optimized and improved based on the above embodiment. As Figure 2 shown, the method includes:

[0035] S210. Perform coordinate transformation and stitching on the point clouds collected by the main lidar and the auxiliary lidar to obtain the global point cloud of the parking lot.

[0036] S220. Take the station-centered coordinate system as the target coordinate system, and perform coordinate transformation on the global point cloud to obtain an intermediate point cloud.

[0037] S230. Filter the interference point clouds in the intermediate point cloud to obtain the target point cloud.

[0038] Specifically, first, based on the extrinsic parameter relationship between the main lidar coordinate system and the auxiliary lidar coordinate system, the point cloud coordinates of the point cloud collected by the auxiliary lidar are transformed to the main lidar coordinate system through translation and rotation, and point cloud stitching is completed to form the global point cloud within the entire lidar field of view. Then, based on the extrinsic parameter relationship between the main lidar coordinate system and the local-level coordinate system, the coordinate system of the global point cloud is transformed (from the main lidar coordinate system to the local-level coordinate system) to obtain the intermediate point cloud. Finally, according to the preset filtering rules, various types of interfering point clouds in the intermediate point cloud are filtered out to obtain the target point cloud that can be used.

[0039] Based on the above embodiments, optionally, the interfering point cloud includes at least one of the following: point clouds outside the parking lot area, point clouds with a height greater than the preset parking space height, and ground point clouds.

[0040] Specifically, the interfering point cloud mainly includes three categories. One is the point cloud not within the overall area of the parking lot, such as the point cloud corresponding to a vehicle outside the parking lot entrance. The second is the point cloud with a height greater than the preset parking space height, which is set manually, and the value usually ranges from 1.9 m to 2.5 m; obstacles higher than the preset parking height will not affect the vehicle's parking into the parking space, so the corresponding point cloud with a height greater than the preset parking space height should also be filtered out before clustering. Finally, there are ground point clouds, which refer to the set of points in the point cloud data that belong to the ground or flat areas. The goal of filtering out the ground point cloud is to separate the background from the target, improve the data quality, and optimize the algorithm efficiency. The filtering of the ground point cloud can be achieved through the Random Sample Consensus (RANSAC) algorithm or other algorithms.

[0041] S240. Cluster the target point cloud to obtain the obstacle point cloud in the parking lot space.

[0042] S250. Project the obstacle point cloud onto the plane where the grid on the parking space is located to obtain the projected point cloud; determine the available parking spaces in the parking lot according to the intrusion situation of each grid by the projected point cloud.

[0043] Specifically, project the obstacle point cloud in the clustering cluster onto the plane where the grid on the parking space is located (i.e., the XOY projection plane of the ENU coordinate system). The point cloud in the grid after projection is the projected point cloud, and the point cloud not in the grid after projection will not be counted as the projected point cloud. Count the number of projected point clouds in the grid to determine the intrusion situation of the projected point cloud on the grid. The more the number of projected point clouds in the grid, the more serious the intrusion situation; the fewer the number of projected point clouds in the grid, the less serious the intrusion situation. For each parking space, determine whether the parking space is an available parking space according to the intrusion situation of each grid on the parking space.

[0044] Based on the above embodiments, optionally, determining the available parking spaces in the parking lot according to the intrusion situation of each grid by the projected point cloud includes:

[0045] For each grid, if the number of projected point clouds in the grid is not greater than the occupancy quantity threshold, then determine the grid as an available grid;

[0046] Determine the parking spaces where all grids on the parking space are available grids as the available parking spaces in the parking lot.

[0047] Specifically, preset an occupancy quantity threshold (for example, 5). For each grid, if the number of projected point clouds in the grid is not greater than the occupancy quantity threshold, it indicates that the intrusion of the obstacle into the grid is very slight or there is no intrusion at all, and determine the grid as an available grid. For each parking space, if all grids on the parking space are available grids, it indicates that the obstacles in the parking lot do not have a substantial impact on the parking of vehicles in this parking space, and determine the parking space as an available parking space where vehicles can be parked.

[0048] Based on the above embodiments, optionally, if the number of projected point clouds in the grid is greater than the occupancy quantity threshold, then determine the grid as an occupied grid;

[0049] Determine the parking spaces where there are grids on the parking space that are occupied grids as the occupied parking spaces in the parking lot;

[0050] Correspondingly, after determining the available parking spaces in the parking lot according to the intrusion situation of the grids on the parking space by the obstacle point cloud, it further includes:

[0051] If the available parking space is not determined to be an occupied parking space within a preset time, then publish the available parking space.

[0052] Specifically, for each grid, if the number of projected point clouds in the grid is greater than the occupancy quantity threshold, it indicates that the intrusion of the obstacle into the grid is serious, and determine the grid as an occupied grid. For each parking space, if there are occupied grids among the grids on the parking space, it indicates that the obstacles in the parking lot have a substantial impact on the parking of vehicles in this parking space, and determine the parking space as an occupied parking space where vehicles cannot be parked. To eliminate the influence of movable obstacles such as plastic bags and pedestrians, the system will detect available and occupied parking spaces regularly (for example, every 5s). For available parking spaces, within a subsequent preset time (for example, 30s), the system will conduct multiple rechecks on them regularly. Only the available parking spaces that are not determined to be occupied parking spaces in each recheck will be published to reduce the misjudgment rate.

[0053] Based on the above embodiments, optionally, the method further includes:

[0054] Update the size and quantity of the grids on the parking space regularly according to the manual review result of the available parking space.

[0055] Specifically, it is not difficult to see from the above content that the size and quantity of the grids on the parking space have a great impact on the misjudgment rate. Taking a household car parking space with a length of 5m and a width of 2.5m as an example, if only one grid is drawn on this parking space and the grid is 5m * 2.5m, due to the grid being too large, even if the projected point cloud in the parking space is sparse, the number of projected point clouds falling within this grid is very likely to exceed the occupancy quantity threshold, resulting in this parking space being determined as an occupied parking space. On the contrary, if 1250 grids are drawn on the parking space, each grid is a square grid with a side length of 0.1m. Due to the grids being too small, even if the projected point cloud in the parking space is dense, the number of projected point clouds falling within each grid is very difficult to exceed the occupancy quantity threshold, resulting in this parking space being determined as an available parking space. Therefore, during the debugging phase, according to the manual review result of the available parking space, repeatedly update and debug the size and quantity of the grids on the parking space until the misjudgment rate of the available parking space reaches the expectation.

[0056] In the embodiment of the present invention, by fully preprocessing the point cloud data before analyzing the point cloud data, the difficulty of subsequent available parking space detection is reduced, and the detection efficiency of available parking spaces is improved.

[0057] Figure 3 It is a schematic structural diagram of a management device for parking spaces in a parking lot provided by another embodiment of the present invention. As Figure 3 shown, the device includes:

[0058] A preprocessing module 310, configured to preprocess the point cloud collected by the main lidar and the auxiliary lidar to obtain a target point cloud; the main lidar and the auxiliary lidar are deployed at different positions in the parking lot;

[0059] A clustering module 320, configured to cluster the target point cloud to obtain an obstacle point cloud in the parking lot space;

[0060] A determination module 330, configured to determine the available parking spaces in the parking lot according to the intrusion situation of the obstacle point cloud into the grids on the parking space; at least two grids are pre-drawn on each parking space.

[0061] The management device for parking spaces in the parking lot provided by the embodiment of the present invention can execute the management method for parking spaces in the parking lot provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0062] Optionally, the determination module 330 includes:

[0063] A projection unit, configured to project the obstacle point cloud onto the plane where the grids on the parking space are located to obtain a projected point cloud;

[0064] A determination unit, configured to determine free parking spaces in the parking lot according to the intrusion conditions of each grid by the projected point cloud.

[0065] Optionally, the determination unit is specifically configured to: for each grid, if the number of projected point clouds in the grid is not greater than an occupancy quantity threshold, determine the grid as a free grid; and determine a parking space as a free parking space in the parking lot if all grids on the parking space are free grids.

[0066] Optionally, the method further includes:

[0067] An occupancy module, configured to if the number of projected point clouds in the grid is greater than the occupancy quantity threshold, determine the grid as an occupied grid; and determine a parking space as an occupied parking space in the parking lot if there is an occupied grid on the parking space.

[0068] A release module, configured to if the free parking space is not determined as an occupied parking space again within a preset time, release the free parking space externally.

[0069] Optionally, the apparatus further includes:

[0070] An update module, configured to regularly update the size and quantity of grids on the parking space according to the manual review result of the free parking space.

[0071] Optionally, the preprocessing module 310 includes:

[0072] A splicing unit, configured to perform coordinate system conversion and splicing on the point clouds collected by the main lidar and the auxiliary lidar to obtain the global point cloud of the parking lot.

[0073] A conversion unit, configured to perform coordinate system conversion on the global point cloud with the earth-centered coordinate system as the target coordinate system to obtain an intermediate point cloud.

[0074] A filtering unit, configured to filter out interfering point clouds in the intermediate point cloud to obtain a target point cloud.

[0075] Optionally, the interfering point clouds include at least one of the following: point clouds outside the parking lot area, point clouds with a height greater than a preset parking space height, and ground point clouds.

[0076] Furthermore, the parking space management apparatus described above can also execute the parking space management method provided in any embodiment of the present invention, and has corresponding functional modules and beneficial effects for executing the method.

[0077] Figure 4The structural schematic diagram of an electronic device 40 that can be used to implement the embodiments of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0078] As Figure 4 shown, the electronic device 40 includes at least one processor 41, and a memory communicatively connected to the at least one processor 41, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc. The memory stores a computer program executable by the at least one processor. The processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. In the RAM 43, various programs and data required for the operation of the electronic device 40 can also be stored. The processor 41, the ROM 42, and the RAM 43 are connected to each other through a bus 44. The input / output (I / O) interface 45 is also connected to the bus 44.

[0079] Multiple components in the electronic device 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disc, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0080] The processor 41 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The processor 41 executes the various methods and processes described above, such as the management method of parking spaces in a parking lot.

[0081] In some embodiments, the method for managing parking spaces in a parking lot can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 40 via the ROM 42 and / or the communication unit 49. When the computer program is loaded into the RAM 43 and executed by the processor 41, one or more steps of the method for managing parking spaces in a parking lot described above can be executed. Alternatively, in other embodiments, the processor 41 can be configured to execute the method for managing parking spaces in a parking lot by any other suitable means (e.g., by means of firmware).

[0082] The various embodiments of the systems and techniques described above in this document can be implemented in digital electronic circuitry, integrated circuit systems, field programmable gate arrays (FPGA), application specific integrated circuits (ASIC), application specific standard products (ASSP), systems on a chip (SOC), complex programmable logic devices (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special-purpose or general-purpose programmable processor that receives data and instructions from a storage system, at least one input device, and at least one output device, and transmits the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0083] The computer programs for implementing the methods of the present invention can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer programs are executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer programs can be executed entirely on the machine, partially on the machine, as a stand-alone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0084] In the context of the present invention, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer-readable storage medium can include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, the computer-readable storage medium can be a machine-readable signal medium. More specific examples of the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0085] To provide for interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can also be used to provide for interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including acoustic input, voice input, or tactile input).

[0086] The systems and techniques described herein can be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0087] A computing system may include a client and a server. The client and the server are generally far from each other and usually interact via a communication network. The client-server relationship is created by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or a cloud host, which is a host product in the cloud computing service system, solving the defects of difficult management and weak business scalability existing in traditional physical hosts and VPS services.

[0088] It should be understood that various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps recited in the present invention can be executed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved, and no limitation is imposed herein.

[0089] The above specific embodiments do not constitute a limitation on the protection scope of the present invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A management method for parking spaces in a parking lot, characterized in that, The method includes: Preprocessing the point clouds collected by the main lidar and the auxiliary lidar to obtain target point clouds; the main lidar and the auxiliary lidar are deployed at different positions in the parking lot; Clustering the target point clouds to obtain obstacle point clouds in the parking lot space; Determining the available parking spaces in the parking lot according to the intrusion situation of the grids on the parking spaces into the obstacle point clouds; at least two grids are pre-drawn on each parking space.

2. The method according to claim 1, characterized in that The determining the available parking spaces in the parking lot according to the intrusion situation of the grids on the parking spaces into the obstacle point clouds includes: Projecting the obstacle point clouds onto the plane where the grids on the parking spaces are located to obtain projected point clouds; Determining the available parking spaces in the parking lot according to the intrusion situation of the projected point clouds into each grid.

3. The method according to claim 2, characterized in that, The determining the available parking spaces in the parking lot according to the intrusion situation of the projected point clouds into each grid includes: For each grid, if the number of projected point clouds in the grid is not greater than the occupancy quantity threshold, then determine the grid as an available grid; Determine the parking spaces with all grids on the parking spaces being available grids as the available parking spaces in the parking lot.

4. The method according to claim 3, wherein The method further includes: If the number of projected point clouds in the grid is greater than the occupancy quantity threshold, then determine the grid as an occupied grid; Determine the parking spaces with grids being occupied grids on the parking spaces as the occupied parking spaces in the parking lot; Correspondingly, after determining the available parking spaces in the parking lot according to the intrusion situation of the grids on the parking spaces into the obstacle point clouds, it further includes: If the available parking space is not determined to be an occupied parking space within a preset time, then publish the available parking space.

5. The method according to claim 2, wherein The method further includes: Regularly updating the size and quantity of the grids on the parking spaces according to the manual review result of the available parking spaces.

6. The method according to claim 1, wherein The preprocessing the point clouds collected by the main lidar and the auxiliary lidar to obtain target point clouds includes: Performing coordinate system conversion and stitching on the point clouds collected by the main lidar and the auxiliary lidar to obtain the global point cloud of the parking lot; Performing coordinate system conversion on the global point cloud with the topocentric coordinate system as the target coordinate system to obtain intermediate point clouds; Filtering the interference point clouds in the intermediate point clouds to obtain target point clouds.

7. The method according to claim 6, wherein The interference point clouds include at least one of the following: point clouds outside the parking lot area, point clouds with a height greater than the preset parking space height, and ground point clouds.

8. A management device for parking spaces in a parking lot, characterized in that, The device includes: A preprocessing module for preprocessing the point clouds collected by the main lidar and the auxiliary lidar to obtain target point clouds; the main lidar and the auxiliary lidar are deployed at different positions in the parking lot; A clustering module for clustering the target point clouds to obtain obstacle point clouds in the parking lot space; A determining module for determining the available parking spaces in the parking lot according to the intrusion situation of the grids on the parking spaces into the obstacle point clouds; at least two grids are pre-drawn on each parking space.

9. An electronic device, characterized in that, The electronic device includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and when the computer program is executed by the at least one processor, enables the at least one processor to execute the parking space management method according to any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for implementing the parking space management method according to any one of claims 1-7 when executed by a processor.

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