CN111931704B - Method, device, apparatus and computer-readable storage medium for evaluating map quality - Google Patents
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Abstract
Embodiments of the present disclosure relate to methods, apparatuses, and computer-readable storage media for assessing map quality. A method of evaluating quality of a map includes identifying a flat region in a point cloud map based on semantic information of the point cloud map, and evaluating quality of the point cloud map by evaluating at least one attribute of the flat region. Embodiments of the present disclosure can evaluate the quality of a point cloud map by analyzing the properties of a flat region (e.g., a road surface region, etc.) in the point cloud map without comparing the point cloud map with a reference map, thereby enabling an automated evaluation of the quality of the point cloud map.
Description
Technical Field
Embodiments of the present disclosure relate generally to the field of maps, and more particularly, to a method, apparatus, device, and computer-readable storage medium for evaluating map quality.
Background
High-precision point cloud maps are commonly used in application scenarios such as object detection, high-precision positioning in automatic driving, and the like. The quality of the high-precision point cloud map will significantly affect the accuracy of object detection and high-precision positioning. Therefore, quality assessment of a high-precision point cloud map is particularly important.
Disclosure of Invention
Embodiments of the present disclosure provide methods, apparatuses, devices, and computer-readable storage media for assessing map quality.
In a first aspect of the present disclosure, a method of evaluating map quality is provided. The method includes identifying a flat region in a point cloud map based on semantic information of the point cloud map, and evaluating quality of the point cloud map by evaluating at least one attribute of the flat region.
In a second aspect of the present disclosure, an apparatus for evaluating map quality is provided. The apparatus includes a flat region identification module configured to identify a flat region in a point cloud map based on semantic information of the point cloud map, and a quality assessment module configured to assess quality of the point cloud map by assessing at least one attribute of the flat region.
In a third aspect of the present disclosure, there is provided an electronic device comprising one or more processors, and a memory for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement a method according to the first aspect of the present disclosure.
In a fourth aspect of the present disclosure, a computer-readable storage medium having a computer program stored thereon is provided. The computer program realizes any of the steps of the method described according to the first aspect of the present disclosure when executed by a processor.
The summary is provided to introduce a selection of concepts in a simplified form that are further described below in the detailed description. This summary is not intended to identify key features or essential features of the disclosure, nor is it intended to be used to limit the scope of the disclosure.
Drawings
The foregoing and other objects, features and advantages of the disclosure will be apparent from the following more particular descriptions of exemplary embodiments of the disclosure as illustrated in the accompanying drawings wherein like reference numbers generally represent like parts throughout the exemplary embodiments of the disclosure.
FIG. 1 illustrates a block diagram of an example environment in which embodiments of the present disclosure can be implemented;
fig. 2 shows a schematic block diagram of a map evaluation apparatus according to an embodiment of the present disclosure;
FIG. 3 illustrates a flowchart of an example method for evaluating map quality, according to an embodiment of the present disclosure;
FIG. 4 illustrates a flowchart of an example method for evaluating smoothness of a flat region according to an embodiment of the present disclosure;
FIG. 5 illustrates a block diagram of an example apparatus for evaluating map quality, and
Fig. 6 illustrates a block diagram of an example electronic device capable of implementing various embodiments of the disclosure.
Like or corresponding reference characters indicate like or corresponding parts throughout the several views.
Detailed Description
Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While the preferred embodiments of the present disclosure are illustrated in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the disclosure to those skilled in the art.
The term "comprising" and variations thereof as used herein means open ended, i.e., "including but not limited to. The term "or" means "and/or" unless specifically stated otherwise. The term "based on" means "based at least in part on". The terms "one example embodiment" and "one embodiment" mean "at least one example embodiment. The term "another embodiment" means "at least one additional embodiment". The terms "first," "second," and the like, may refer to different or the same object. Other explicit and implicit definitions are also possible below.
As described above, the high-precision point cloud map is generally used for application scenes such as object detection, high-precision positioning in automatic driving, and the like. The quality of the high-precision point cloud map will significantly affect the accuracy of object detection and high-precision positioning. Therefore, quality assessment of a high-precision point cloud map is particularly important. Some conventional approaches evaluate the quality of a point cloud map by comparing the point cloud map to a reference map. However, the reference map may not be readily available or the reference map itself may be inaccurate, which would affect the quality assessment of the point cloud map.
Embodiments of the present disclosure propose a solution to evaluate map quality that can address one or more of the above problems and other potential problems. In this scheme, a flat region in a point cloud map is identified based on semantic information of the point cloud map. The quality of the point cloud map is evaluated by evaluating at least one attribute of the flat region. In this way, the scheme can evaluate the quality of the point cloud map by analyzing the attributes of the flat area (e.g., road surface area, etc.) in the point cloud map without comparing the point cloud map with the reference map, thereby enabling an automated evaluation of the quality of the point cloud map.
FIG. 1 illustrates a block diagram of an example environment 100 in which embodiments of the present disclosure can be implemented. As shown in fig. 1, the environment 100 includes a map acquisition device 110 and a map evaluation device 120. It should be understood that the structure and function of environment 100 are described for illustrative purposes only and are not meant to suggest any limitation as to the scope of the disclosure. For example, embodiments of the present disclosure may also be applied in environments other than environment 100.
The map acquisition device 110 may include, but is not limited to, an acquisition cart or other device for acquiring map data. For example, a lidar may be mounted on the map acquisition device 110 for acquiring data. The map acquisition device 110 may be moved within a particular geographic area during an acquisition period to acquire point cloud data for mapping. The "point cloud data" described herein may refer to data information of respective points of the object surface returned when a beam of laser light irradiates the object surface, including three-dimensional coordinates (e.g., x-coordinate, y-coordinate, and z-coordinate) of each point, and laser reflection intensity (also referred to as "reflection value"). The map acquisition device 110 may generate a point cloud map 115 for a particular geographic area based on the acquired point cloud data.
The point cloud map 115 may be provided to a map evaluation device 120. The map evaluation device 120 may evaluate the quality of the point cloud map 115 by analyzing attributes of the road surface region in the point cloud map 115 to generate an evaluation result 125.
Fig. 2 shows a schematic block diagram of a map evaluation device 120 according to an embodiment of the present disclosure. As shown in fig. 2, the map evaluation device 120 may include a projection module 210, a flat region identification module 220, and an evaluation module 230. It should be understood that the structure and function of the map evaluation device 120 are described for exemplary purposes only and do not imply any limitation on the scope of the present disclosure. In some embodiments, the map evaluation apparatus 120 may be implemented in a different structure than that shown in fig. 2.
In some embodiments, the projection module 210 is configured to generate a projected map 215 corresponding to the point cloud map 115 by two-dimensional grid projecting the point cloud map 115. The projected map 215 may be divided into a plurality of grids (also referred to as "a plurality of cell areas"). For each grid, the height mean and the height variance corresponding to that grid may be determined based on the height values of the original coordinate points in the point cloud map 115 corresponding to that grid. Further, the reflection value mean and the reflection value variance corresponding to the grid may be determined based on the reflection values of the original coordinate points corresponding to the grid in the point cloud map 115. The projected map 215 is also referred to herein as a "two-dimensional projected map" or a "two-dimensional grid map".
In some embodiments, to generate the projected map 215 corresponding to the point cloud map 115, the projection module 210 may first aggregate the three-dimensional point cloud map 115 into coordinate points in three-dimensional grids, where each three-dimensional grid is a cube of a predetermined side length. For each three-dimensional grid, the projection module 210 may calculate weighted results for all of the original coordinate points that fall within, including height value weighted results and reflectance value weighted results. The projection module 210 may count a height value mean, a height value variance, a reflectance value mean, and a reflectance value variance for each three-dimensional grid. After deriving statistics for each three-dimensional grid, projection module 210 may divide the three-dimensional grid into a plurality of grid sets, where each grid set has the same horizontal coordinates (i.e., each grid set corresponds to the same x-coordinate and the same y-coordinate), also referred to herein as "posts". The projection module 210 may perform euclidean clustering of points in each column to obtain a plurality of clusters of points. The projection module 210 may calculate a height value weighted result and a reflection value weighted result for each cluster of points. The projection module 210 can filter out empty point clusters, dynamic point clusters, and less weighted point clusters (e.g., less-counted clusters) in each column. The projection module 210 may calculate, for each filtered pillar, a height value mean, a height value variance, a reflection value mean, and a reflection value variance, so as to obtain a projected map 215, for example, a plane area corresponding to each pillar is a unit area in the projected map 215.
The generation of the projected map 215 is described above for exemplary purposes only. It should be appreciated that in some embodiments, the projection module 210 may generate the projection map 215 corresponding to the point cloud map 115 in any other manner. The scope of the present disclosure is not limited in this respect.
In some embodiments, the flat region identification module 220 is configured to identify the flat region 225 in the projected map 215 based on semantic information of the flat region. Examples of flat areas may include, but are not limited to, road surface areas such as lanes, walkways, squares, and the like. The semantic information of the flat region may be predetermined. Additionally or alternatively, the semantic information of the flat region may be updated accordingly based on at least a portion of the evaluation result 125, thereby improving its accuracy.
In some embodiments, the evaluation module 230 is configured to evaluate the quality of the point cloud map 115 by evaluating at least one attribute of the flat region 225, thereby generating the evaluation result 125. The at least one attribute may include at least one of smoothness and thickness of the flat region. As shown in fig. 2, for example, the evaluation module 230 may include a smoothness evaluation module 231 and/or a thickness evaluation module 232, wherein the smoothness evaluation module 231 is configured to evaluate the smoothness of the flat region 225 and the thickness evaluation module 232 is configured to evaluate the thickness of the flat region 225.
The smoothness of the flat region 225 is mainly exhibited by the variation in height of the flat region 225. In some embodiments, to evaluate the smoothness of the flat region 225, the smoothness evaluation module 231 may obtain a pose map corresponding to the point cloud map 115, the pose map including a set of poses of the map acquisition device 110 when acquiring the point cloud map 115. In some embodiments, for each unit region of the plurality of unit regions, the smoothness evaluation module 231 may determine at least one pose associated with the unit region from the pose map. For example, the smoothness evaluation module 231 may determine at least one pose that is within the proximity of the unit region. The determination of the proximity range of the unit area may be implemented based on a K-nearest neighbor (KNN) algorithm or other similar algorithm, the scope of the disclosure being not limited in this respect. In some embodiments, the smoothness evaluation module 231 may determine the relative height value of the unit region based on the height average of the unit region and the corresponding height value of the at least one pose in the projected map 215. For example, the smoothness evaluation module 231 may weight average the respective height values of the at least one pose to obtain a height weighted average of the at least one pose. The weight of the altitude value for each of the at least one pose may be determined based on the following formula:
Where x p,i represents the x-coordinate of the i-th pose, x cell represents the x-coordinate of the unit region, y p,i represents the y-coordinate of the i-th pose and y cell represents the y-coordinate of the unit region. Max_weight represents a preset maximum WEIGHT, e.g., less than 1. The smoothness evaluation module 231 may calculate a difference between the height average of the unit region and the height weighted average of the at least one pose in the projection map 215 as the relative height value of the unit region.
In some embodiments, the smoothness evaluation module 231 may generate the grayscale image by mapping a plurality of relative height values of a plurality of unit areas to grayscale values (e.g., ranging from 0 to 255), respectively. The smoothness evaluation module 231 may generate a gradient map of the grayscale image by edge detection of the grayscale image. For example, edge detection may be implemented based on a Canny algorithm or other similar algorithm, the scope of the disclosure being not limited in this respect. Additionally or alternatively, the smoothness evaluation module 231 may binarize the gradient map, wherein a unit region of value 0 represents a region where the height change is below a predetermined threshold (also referred to herein as a "first threshold"), and a unit region of value other than 0 represents a region where the height change exceeds a predetermined threshold (also referred to herein as a "first threshold"). The smoothness evaluation module 231 may determine, for each of at least one unit region included in the flat region 225, whether the change in height of that unit region exceeds a first threshold (i.e., whether there is a gradient value other than 0 in the binarized gradient map). If a unit area has a gradient value other than 0 in the binarized gradient map, the map evaluating apparatus 120 may determine the unit area as a smoothness outlier within the flat area 225.
Additionally or alternatively, in some embodiments, the smoothness evaluation module 231 may further determine whether the distance of the smoothness outlier in the flat region 225 from the boundary of the flat region 225 is less than a predetermined threshold (also referred to herein as a "second threshold"). If the smoothness anomaly is located near the boundary of the flat region 225 (i.e., the distance from the boundary of the flat region 225 is below the second threshold), the smoothness evaluation module 231 may identify the smoothness anomaly as a smoothness alarm within the flat region 225, which indicates that the smoothness anomaly may be due to an error in the semantic information of the flat region. If the smoothness outlier is not near the boundary of the flat region 225 (i.e., the distance from the boundary of the flat region 225 exceeds the second threshold), the smoothness evaluation module 231 may identify the smoothness outlier as a smoothness error point within the flat region 225, which indicates that the smoothness outlier may be due to an error in the point cloud map. In some embodiments, the smoothness evaluation module 231 may generate statistical information about smoothness alarm points and/or smoothness error points within the flat region 225 as a smoothness evaluation result. For example, the statistics may indicate whether smoothness alarm points and/or smoothness error points exist in the flat region 225, their specific locations and proportions, etc.
The smoothness of the flat region 225 is mainly represented by the range of the height variation of the flat region 225 (i.e., the height variance). In some embodiments, to evaluate whether there is an anomaly in the thickness of the flat region 225, the thickness evaluation module 232 may generate statistics based on a respective variance in the height of at least one unit region included in the flat region 225. The statistical result may include a mean, a maximum, a minimum, etc. of the altitude variance. The thickness evaluation module 232 may determine whether there is an abnormality in the thickness of the flat region 225 based on the statistics and generate a thickness evaluation result.
Referring back to fig. 2, the evaluation module 230 may generate the evaluation result 125 based on at least one of the smoothness evaluation result generated by the smoothness evaluation module 231 and the thickness evaluation result generated by the thickness evaluation module 232. In this way, embodiments of the present disclosure can evaluate the quality of a point cloud map by analyzing the attributes of flat areas (e.g., road surface areas, etc.) in the point cloud map without comparing the point cloud map to a reference map, thereby enabling automated evaluation of the quality of the point cloud map. The evaluation result 125 may be used to update the point cloud map 115 and/or update map semantic information for identifying flat areas, thereby improving the quality of the point cloud map 115 and improving the accuracy of the map semantic information.
Fig. 3 illustrates a flowchart of an example method 300 for evaluating map quality, according to an embodiment of the disclosure. The method 300 may be performed, for example, at the map evaluation device 120 as shown in fig. 1 and 2. The method 300 will be described in detail below in conjunction with fig. 2. It should be understood that method 300 may also include blocks not shown and/or that the blocks shown may be omitted. The scope of the present disclosure is not limited in this respect.
At block 310, the map evaluation device 120 identifies the flat region 225 in the point cloud map 115 based on the semantic information of the point cloud map 115.
In some embodiments, to identify the flat region 225, the map evaluation device 120 may obtain the projected map 215 corresponding to the point cloud map 115 by two-dimensional grid projecting the point cloud map 115, wherein the projected map 215 is divided into a plurality of unit regions, each unit region having a height mean and a height variance determined based on a height value of an original coordinate point in the point cloud map 115 corresponding to the unit region. The map evaluation device 120 may identify a flat region 225 in the projected map 215 based on the semantic information, wherein the flat region 225 includes at least one unit region of the plurality of unit regions.
At block 320, the map evaluation device 120 evaluates the quality of the point cloud map by evaluating at least one attribute of the flat region.
In some embodiments, the map evaluation device 120 may evaluate the quality of the point cloud map 115 by evaluating at least one of the smoothness and thickness of the flat region 225.
In some embodiments, to evaluate the thickness of the flat region 225, the map evaluation device 120 may generate a statistical result based on the respective height variance of the at least one unit region, and then determine whether there is an abnormality in the thickness of the flat region 225 based on the statistical result.
In some embodiments, to evaluate the smoothness of the flat region 225, the map evaluation device 120 may determine smoothness outliers, smoothness alarm points, and/or smoothness error points within the flat region 225 and generate relevant statistics.
Fig. 4 illustrates a flowchart of an example method 400 for evaluating smoothness of a flat region according to an embodiment of the disclosure. The method 400 may be considered an example implementation of block 330, which may be performed, for example, at the map evaluation device 120 as shown in fig. 1 and 2. It should be understood that method 400 may also include blocks not shown and/or that the blocks shown may be omitted. The scope of the present disclosure is not limited in this respect.
As shown in fig. 4, at block 410, the map evaluation device 120 may acquire a pose map corresponding to the point cloud map 115, the pose map including a set of poses of the map acquisition device 110 when acquiring the point cloud map 115.
At block 420, the map evaluation device 120 determines a plurality of relative elevation values for a plurality of unit areas based on the projected map 215 and the acquired pose map.
In some embodiments, the map evaluation device 120 may determine, for each of a plurality of unit regions, at least one pose associated with the unit region from a set of poses, and then determine a relative height value for the unit region based on a height average of the unit region and a corresponding height value of the at least one pose in the projected map 215.
At block 430, the map evaluation device 120 may generate a grayscale image by mapping a plurality of relative height values of a plurality of unit areas to grayscale values, respectively.
At block 440, the map evaluation device 120 may generate a gradient map of the grayscale image by edge detecting the grayscale image, the gradient map indicating the respective gradients of the plurality of unit regions.
At block 450, the map evaluation device 120 may evaluate the smoothness of the flat region 225 based on the respective gradients of the at least one unit region.
In some embodiments, for each of the at least one unit region, if it is determined that the gradient of the unit region exceeds the first threshold, the map evaluation device 120 may determine the unit region as a smoothness outlier within the flat region 225.
Additionally or alternatively, in some embodiments, if the map evaluation device 120 determines that the distance of the smoothness outlier of the flat region 225 from the boundary of the flat region 225 is less than the second threshold, the map evaluation device 120 may determine the smoothness outlier as a smoothness alarm point within the flat region 225. In some embodiments, if the map evaluation device 120 determines that the distance of the smoothness outlier of the flat region 225 from the boundary of the flat region 225 exceeds the second threshold, the map evaluation device 120 may determine the smoothness outlier as a smoothness error point within the flat region 225. In some embodiments, the map evaluation device 120 may generate statistical information regarding smoothness alarm points and/or smoothness error points within the flat region 225.
Embodiments of the present disclosure also provide corresponding apparatus for implementing the above-described methods 300 and/or 400. Fig. 5 illustrates a block diagram of an example apparatus 500 for evaluating map quality, according to an embodiment of the disclosure.
As shown in fig. 5, the apparatus 500 includes an identification module 510 configured to identify a flat region in a point cloud map based on semantic information of the point cloud map. The apparatus 500 further comprises an evaluation module 520 configured to evaluate the quality of the point cloud map by evaluating at least one attribute of the flat region.
In some embodiments, the assessment module 520 includes at least one of a smoothness assessment module configured to assess the smoothness of the flat region and a thickness assessment module configured to assess the thickness of the flat region.
In some embodiments, the identification module 510 includes a projection module configured to obtain a projection map corresponding to a point cloud map by two-dimensional grid projection of the point cloud map, wherein the projection map is divided into a plurality of unit areas, each unit area having a height mean and a height variance determined based on a height value of an original coordinate point in the point cloud map corresponding to the unit area, and a flat area identification module configured to identify a flat area in the projection map based on semantic information, wherein the flat area includes at least one unit area of the plurality of unit areas.
In some embodiments, the smoothness evaluation module includes an acquisition unit configured to acquire a pose map corresponding to a point cloud map, the pose map including a set of poses of an acquisition device when the point cloud map is acquired, a first determination unit configured to determine a plurality of relative height values of a plurality of unit areas based on respective height values of the set of poses and respective height means of the plurality of unit areas in a projection map, a second determination unit configured to determine respective gradients of the plurality of unit areas based on the plurality of relative height values, and an evaluation unit configured to evaluate smoothness of a flat area based on the gradient of at least one unit area.
In some embodiments, the second determining unit is configured to generate a gray image by mapping the plurality of relative height values into gray values, respectively, and to generate a gradient map of the gray image by edge detection of the gray image, the gradient map indicating respective gradients of the plurality of unit areas.
In some embodiments, the evaluation unit is configured to determine, for each of the at least one unit region, the unit region as a smoothness outlier within the flat region if it is determined that the gradient of the unit region exceeds the first threshold.
In some embodiments, the evaluation unit is further configured to determine the smoothness outlier as a smoothness alarm point within the flat region if it is determined that the distance of the smoothness outlier from the boundary of the flat region is less than a second threshold, determine the smoothness outlier as a smoothness error point within the flat region if it is determined that the distance of the smoothness outlier from the boundary of the flat region exceeds the second threshold, and generate statistics related to the smoothness alarm point and/or the smoothness error point within the flat region.
In some embodiments, the thickness evaluation module includes a generation unit configured to generate a statistical result based on a respective height variance of at least one unit region, and a third determination unit configured to determine whether there is an abnormality in the thickness of the flat region based on the statistical result.
The modules and units included in the apparatus 500 may be implemented in various ways, including software, hardware, firmware, or any combination thereof. In some embodiments, one or more units may be implemented using software and/or firmware, such as machine executable instructions stored on a storage medium. In addition to or in lieu of machine-executable instructions, some or all of the modules and units in apparatus 500 may be implemented at least in part by one or more hardware logic components. By way of example and not limitation, exemplary types of hardware logic components that can be used include Field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standards (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
Fig. 6 illustrates a block diagram of an example electronic device 600 capable of implementing various embodiments of the disclosure. For example, the map evaluation device 120 as shown in FIG. 1 may be implemented by the device 600. As shown in fig. 6, the apparatus 600 includes a Central Processing Unit (CPU) 601, which can perform various suitable actions and processes according to computer program instructions stored in a Read Only Memory (ROM) 602 or computer program instructions loaded from a storage unit 608 into a Random Access Memory (RAM) 603. In the RAM 603, various programs and data required for the operation of the device 600 may also be stored. The CPU 601, ROM 602, and RAM 603 are connected to each other through a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
Various components in the device 600 are connected to the I/O interface 605, including an input unit 606, e.g., keyboard, mouse, etc., an output unit 607, e.g., various types of displays, speakers, etc., a storage unit 608, e.g., magnetic disk, optical disk, etc., and a communication unit 609, e.g., network card, modem, wireless communication transceiver, etc. The communication unit 609 allows the device 600 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The various processes and treatments described above, such as methods 300 and/or 400, may be performed by processing unit 601. For example, in some embodiments, methods 300 and/or 400 may be implemented as a computer software program tangibly embodied on a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and/or installed onto the device 600 via the ROM 602 and/or the communication unit 609. When the computer program is loaded into RAM 603 and executed by CPU 601, one or more of the acts of methods 300 and/or 400 described above may be performed.
The present disclosure may be methods, apparatus, systems, and/or computer program products. The computer program product may include a computer readable storage medium having computer readable program instructions embodied thereon for performing aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium include a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical encoding device, punch cards or intra-groove protrusion structures such as those having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
The computer program instructions for performing the operations of the present disclosure may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as SMALLTALK, C ++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present disclosure are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information of computer readable program instructions, which can execute the computer readable program instructions.
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processing unit of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processing unit of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The foregoing description of the embodiments of the present disclosure has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the improvement of technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (12)
1. A method of evaluating map quality, comprising:
Identifying a flat area in the point cloud map based on semantic information of the point cloud map, the flat area including at least one unit area;
and evaluating a quality of the point cloud map by evaluating at least one attribute of the flat region, wherein the evaluating the at least one attribute of the flat region includes evaluating a smoothness of the flat region;
The evaluating the smoothness of the flat region includes determining, for each of the at least one unit region, the unit region as a smoothness outlier within the flat region if it is determined that a gradient of the unit region exceeds a first threshold;
The method further includes determining a smoothness outlier within the flat region as a smoothness warning point within the flat region if a distance from the smoothness outlier within the flat region to a boundary of the flat region is determined to be less than a second threshold, determining the smoothness outlier as a smoothness error point within the flat region if a distance from the smoothness outlier within the flat region to the boundary of the flat region is determined to be greater than the second threshold, and generating statistical information regarding the smoothness warning point and/or the smoothness error point within the flat region.
2. The method of claim 1, wherein identifying a flat region in the point cloud map comprises:
Obtaining a projected map corresponding to the point cloud map by two-dimensional grid projection of the point cloud map, wherein the projected map is divided into a plurality of unit areas each having a height average value and a height variance determined based on a height value of an original coordinate point corresponding to the unit area in the point cloud map, and
Based on the semantic information, the flat region in the projected map is identified, wherein the flat region includes at least one unit region of the plurality of unit regions.
3. The method of claim 2, wherein evaluating smoothness of the flat region comprises:
acquiring a pose graph corresponding to the point cloud map, wherein the pose graph comprises a pose set of acquisition equipment when the point cloud map is acquired;
determining a plurality of relative height values of the plurality of unit areas based on the respective height values of the pose set and the respective height average values of the plurality of unit areas in the projection map;
Determining a respective gradient of the plurality of unit areas based on the plurality of relative height values, and
The smoothness of the flat region is evaluated based on the gradient of the at least one unit region.
4. The method of claim 3, wherein determining the respective gradients of the plurality of unit regions comprises:
generating a gray image by mapping the plurality of relative height values to gray values, respectively, and
A gradient map of the gray image is generated by edge detection of the gray image, the gradient map indicating respective gradients of the plurality of unit regions.
5. The method of claim 2, wherein the evaluating at least one attribute of the flat region comprises evaluating a thickness of the flat region, the method further comprising:
generating a statistical result based on the corresponding height variance of the at least one unit region, and
And determining whether the thickness of the flat area is abnormal or not based on the statistical result.
6. An apparatus for evaluating map quality, comprising:
An identification module configured to identify a flat area in a point cloud map based on semantic information of the point cloud map, the flat area including at least one unit area, and
An evaluation module configured to evaluate a quality of the point cloud map by evaluating at least one attribute of the flat region, wherein the evaluation module includes a smoothness evaluation module configured to evaluate smoothness of the flat region;
For each unit region in the at least one unit region, if it is determined that the gradient of the unit region exceeds a first threshold, determining the unit region as a smoothness outlier in the flat region;
If the distance between the smoothness abnormal point in the flat area and the boundary of the flat area is smaller than a second threshold value, determining the smoothness abnormal point as a smoothness alarm point in the flat area;
determining the smoothness anomaly point as a smoothness error point within the flat region if the distance between the smoothness anomaly point within the flat region and the boundary of the flat region is determined to be greater than the second threshold value, and
Statistical information about smoothness alarm points and/or smoothness error points within the flat region is generated.
7. The apparatus of claim 6, wherein the identification module comprises:
A projection module configured to obtain a projection map corresponding to the point cloud map by two-dimensional grid projection of the point cloud map, wherein the projection map is divided into a plurality of unit areas each having a height average value and a height variance determined based on a height value of an original coordinate point corresponding to the unit area in the point cloud map, and
A flat region identification module configured to identify the flat region in the projection map based on the semantic information, wherein the flat region includes at least one unit region of the plurality of unit regions.
8. The apparatus of claim 7, wherein the smoothness evaluation module comprises:
an acquisition unit configured to acquire a pose map corresponding to the point cloud map, the pose map including a set of poses of an acquisition device when acquiring the point cloud map;
a first determination unit configured to determine a plurality of relative height values of the plurality of unit areas based on respective height values of the pose set and respective height average values of the plurality of unit areas in the projection map;
a second determination unit configured to determine respective gradients of the plurality of unit areas based on the plurality of relative height values, and
An evaluation unit configured to evaluate smoothness of the flat region based on a gradient of the at least one unit region.
9. The apparatus of claim 8, wherein the second determination unit is configured to:
generating a gray image by mapping the plurality of relative height values to gray values, respectively, and
A gradient map of the gray image is generated by edge detection of the gray image, the gradient map indicating respective gradients of the plurality of unit regions.
10. The apparatus of claim 7, wherein the evaluation module comprises a thickness evaluation module configured to evaluate a thickness of the flat region, the thickness evaluation module comprising:
a generation unit configured to generate a statistical result based on the respective height variances of the at least one unit region, and
And a third determination unit configured to determine whether or not there is an abnormality in the thickness of the flat region based on the statistical result.
11. An electronic device, comprising:
one or more processors, and
A memory for storing one or more programs that, when executed by the one or more processors, cause the electronic device to implement the method of any of claims 1-5.
12. A computer readable storage medium having stored thereon a computer program which, when executed by a processor, implements the method according to any of claims 1-5.
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