CN111931704A - Method, device, equipment 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 evaluating map quality. A method of assessing map quality includes identifying a flat area in a point cloud map based on semantic information of the point cloud map; and evaluating the 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 attributes of a flat area (e.g., a road surface area, etc.) in the point cloud map without comparing the point cloud map with a reference map, thereby enabling automated evaluation of the quality of the point cloud map.
Description
Technical Field
Embodiments of the present disclosure generally relate to the field of maps, and more particularly, to methods, apparatuses, devices, and computer-readable storage media for evaluating map quality.
Background
High-precision point cloud maps are generally 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 can significantly affect the accuracy of object detection and high-precision positioning. Therefore, it is particularly important to perform quality evaluation on the high-precision point cloud map.
Disclosure of Invention
Embodiments of the present disclosure provide methods, apparatuses, devices, and computer-readable storage media for evaluating map quality.
In a first aspect of the disclosure, a method of evaluating map quality is provided. The method comprises the following steps: identifying a flat area in a point cloud map based on semantic information of the point cloud map; and evaluating the 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 device includes: a flat area identification module configured to identify a flat area in a point cloud map based on semantic information of the point cloud map; and a quality assessment module configured to assess the quality of the point cloud map by assessing at least one attribute of the flat region.
In a third aspect of the disclosure, an electronic device is provided that includes one or more processors; and 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 disclosure.
In a fourth aspect of the disclosure, a computer-readable storage medium is provided, having a computer program stored thereon. The computer program, when executed by a processor, implements any of the steps of the method described according to the first aspect of the disclosure.
This 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 device according to an embodiment of the present disclosure;
FIG. 3 shows a flowchart of an example method for assessing map quality, in accordance with an embodiment of the present disclosure;
FIG. 4 shows a flow diagram of an example method for assessing smoothness of a flat region, in accordance with an embodiment of the present disclosure;
FIG. 5 shows a block diagram of an example apparatus for evaluating map quality, in accordance with an embodiment of the present disclosure; and
FIG. 6 illustrates a block diagram of an example electronic device capable of implementing various embodiments of the present disclosure.
Like or corresponding reference characters designate 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 shown 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 "include" and variations thereof as used herein is meant to be inclusive in an open-ended manner, i.e., "including but not limited to". Unless specifically stated otherwise, the term "or" means "and/or". 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, a high-precision point cloud map is generally 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 can significantly affect the accuracy of object detection and high-precision positioning. Therefore, it is particularly important to perform quality evaluation on the high-precision point cloud map. Some conventional approaches evaluate the quality of a point cloud map by comparing it to a reference map. However, the reference map may not be readily available or the reference map itself may be inaccurate, which will affect the quality assessment of the point cloud map.
Embodiments of the present disclosure provide a solution for evaluating map quality that addresses one or more of the above-mentioned problems and other potential problems. In this scheme, a flat area 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 area. In this way, the scheme can evaluate the quality of the point cloud map by analyzing the attributes of a flat area (e.g., a road surface area, etc.) in the point cloud map without comparing the point cloud map with a reference map, thereby enabling 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, environment 100 includes a map collection device 110 and a map evaluation device 120. It should be understood that the description of the structure and function of environment 100 is for exemplary purposes only and does not imply any limitation as to the scope of the disclosure. For example, embodiments of the present disclosure may also be applied to environments other than environment 100.
The map capture device 110 may include, but is not limited to, a capture cart or other device for capturing 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. As used herein, "point cloud data" may refer to data information of each point of an object surface returned when a beam of laser light impinges on the object surface, including three-dimensional coordinates (e.g., x, y, and z coordinates) of each point and the intensity of the laser reflection (also referred to as a "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 the attributes of the road surface area 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 area 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 as to the scope of the present disclosure. In some embodiments, the map evaluation device 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 projection map 215 corresponding to the point cloud map 115 by two-dimensional raster projecting the point cloud map 115. The projected map 215 may be divided into a plurality of grids (also referred to as "cell regions"). For each grid, the height mean and height variance corresponding to the grid may be determined based on the height values of the original coordinate points in the point cloud map 115 corresponding to the grid. Further, the mean and variance of the reflection values corresponding to the grid may be determined based on the reflection values of the original coordinate points in the point cloud map 115 corresponding to the grid. The projection map 215 is also referred to herein as a "two-dimensional projection map" or a "two-dimensional grid map".
In some embodiments, to generate the projection map 215 corresponding to the point cloud map 115, the projection module 210 may first perform coordinate point aggregation of the three-dimensional point cloud map 115 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 a weighting result for all of the original coordinate points falling therein, including a height value weighting result and a reflection value weighting result. The projection module 210 may count a mean height value, a variance height value, a mean reflection value, and a variance reflection value for each three-dimensional grid. After obtaining the statistics for each three-dimensional grid, projection module 210 may partition the three-dimensional grid into multiple 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), each grid set also referred to herein as a "pillar (pilar)". The projection module 210 can perform Euclidean clustering on the points in each pillar, thereby obtaining a plurality of point clusters. The projection module 210 may calculate a height value weighting result and a reflection value weighting result for each of the clusters. The projection module 210 can filter out airborne clusters of points, dynamic clusters of points, and clusters of points with lower weights (e.g., clusters with fewer points) in each pillar. The projection module 210 may calculate the height mean, the height variance, the reflection mean, and the reflection variance for each filtered pillar, so as to obtain the projection map 215, for example, a planar area corresponding to each pillar is a unit area in the projection 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 projected 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 a 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, roadway areas such as lanes, sidewalks, plazas, 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 results 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 property may include at least one of smoothness and thickness of the flat region. As shown in fig. 2, for example, evaluation module 230 may include a smoothness evaluation module 231 and/or a thickness evaluation module 232, wherein smoothness evaluation module 231 is configured to evaluate the smoothness of flat region 225 and thickness evaluation module 232 is configured to evaluate the thickness of flat region 225.
The smoothness of the flat region 225 is mainly reflected by the height variation of the flat region 225. In some embodiments, to evaluate the smoothness of the flat region 225, the smoothness evaluation module 231 may retrieve a pose map corresponding to the point cloud map 115 that includes a set of poses of the map acquisition device 110 at the time the point cloud map 115 was acquired. In some embodiments, for each unit area of the plurality of unit areas, the smoothness evaluation module 231 may determine at least one pose associated with the unit area from the pose graph. For example, the smoothness evaluation module 231 may determine at least one pose located within a vicinity of the unit area. The determination of the vicinity of the unit area may be implemented based on a K-nearest neighbor (KNN) algorithm or other similar algorithm, and the scope of the present disclosure is not limited in this respect. In some embodiments, the smoothness evaluation module 231 may determine a relative height value for the unit area based on the height mean of the unit area and the respective height value of at least one pose in the projected map 215. For example, the smoothness evaluation module 231 may perform a weighted average of the respective height values of the at least one pose to obtain a weighted average of the heights of the at least one pose. A weight of the height value for each of the at least one pose may be determined based on the following formula:
wherein xp,iX coordinate, x, representing the ith posecellX-coordinate, y, representing the unit areap,iY coordinate representing the i-th pose and ycellThe y-coordinate of the unit area is represented. MAX _ WEIGHT represents a preset maximum WEIGHT, e.g., less than 1. The smoothness evaluation module 231 may calculate a difference between the average of the heights of the unit area and the weighted average of the heights of at least one pose in the projection map 215 as a relative height value of the unit area.
In some embodiments, the smoothness evaluation module 231 may generate a 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 performing edge detection on the grayscale image. For example, edge detection may be implemented based on the Canny algorithm or other similar algorithms, although the scope of the present disclosure is not limited in this respect. Additionally or alternatively, the smoothness evaluation module 231 may binarize the gradient map, wherein unit regions with a value of 0 represent regions having a height variation below a predetermined threshold (also referred to herein as "first threshold"), and unit regions with a value of less than 0 represent regions having a height variation exceeding a predetermined threshold (also referred to herein as "first threshold"). The smoothness evaluation module 231 may determine, for each of at least one unit area included in the flat area 225, whether a change in height of the unit area exceeds a first threshold (i.e., whether there is a gradient value other than 0 in the binarized gradient map). If a certain unit area has a gradient value other than 0 in the binarized gradient map, the map evaluation device 120 may determine the unit area as a smoothness singular point within the flat area 225.
Additionally or alternatively, in some embodiments, smoothness evaluation module 231 may further determine whether a distance of a smoothness outlier in flat region 225 from a boundary of flat region 225 is less than a predetermined threshold (also referred to herein as a "second threshold"). If the smoothness anomaly point 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 point as a smoothness alert point within the flat region 225, indicating 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, indicating that the smoothness outlier may be due to an error in the point cloud map. In some embodiments, smoothness evaluation module 231 may generate statistical information about smoothness alert points and/or smoothness error points within flat region 225 as a smoothness evaluation result. For example, the statistical information may indicate whether smoothness alert points and/or smoothness error points are present in the flat region 225, their specific locations and proportions, and the like.
The smoothness of the flat region 225 is mainly reflected by the range of height variation (i.e., height variance) of the flat region 225. In some embodiments, to evaluate whether an anomaly exists in the thickness of the flat region 225, the thickness evaluation module 232 may generate a statistical result based on a respective height variance of at least one unit region included by the flat region 225. The statistics may include mean, maximum, minimum, etc. of the altitude variance. The thickness evaluation module 232 can determine whether an anomaly exists in the thickness of the flat region 225 based on the statistics and generate a thickness evaluation result.
Referring back to fig. 2, evaluation module 230 may generate evaluation result 125 based on at least one of a smoothness evaluation result generated by smoothness evaluation module 231 and a thickness evaluation result generated by thickness evaluation module 232. In this way, embodiments of the present disclosure can evaluate the quality of a point cloud map by analyzing attributes of a flat area (e.g., a road surface area, etc.) in the point cloud map without comparing the point cloud map with a reference map, thereby enabling automated evaluation of the quality of the point cloud map. The evaluation results 125 may be used to update the point cloud map 115 and/or update map semantic information used to identify flat areas, thereby improving the quality of the point cloud map 115 and improving the accuracy of the map semantic information.
Fig. 3 shows a flowchart of an example method 300 for assessing map quality in accordance with an embodiment of the present 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 may omit blocks shown. The scope of the present disclosure is not limited in this respect.
At block 310, the map evaluation device 120 identifies a flat area 225 in the point cloud map 115 based on semantic information of the point cloud map 115.
In some embodiments, to identify the flat region 225, the map evaluation device 120 may derive the projection map 215 corresponding to the point cloud map 115 by two-dimensional grid projecting the point cloud map 115, wherein the projection map 215 is divided into a plurality of unit regions, each unit region having a height mean and a height variance determined based on the height value of the original coordinate point in the point cloud map 115 corresponding to the unit region. The map evaluation device 120 can 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 area.
In some embodiments, the map evaluation device 120 may evaluate the quality of the point cloud map 115 by evaluating at least one of 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 at least one unit region, and then determine whether there is an anomaly 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 anomaly points, smoothness alert points, and/or smoothness error points within the flat region 225 and generate relevant statistical information.
Fig. 4 shows a flow diagram of an example method 400 for evaluating smoothness of a flat region, in accordance with an embodiment of the present disclosure. Method 400 may be considered an example implementation of block 330, which may be performed, for example, at 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 may omit blocks shown. 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 comprising a set of poses of the map acquisition device 110 at the time the point cloud map 115 was acquired.
At block 420, the map evaluation device 120 determines a plurality of relative height values for a plurality of unit areas based on the projected map 215 and the acquired pose graph.
In some embodiments, the map evaluation device 120 may determine, for each unit area of the plurality of unit areas, at least one pose associated with the unit area from the set of poses, and then determine a relative height value for the unit area based on a height mean of the unit area and a respective height value of the at least one pose in the projection 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 for the grayscale image by edge detecting the grayscale image, the gradient map indicating respective gradients of the plurality of unit regions.
At block 450, the map evaluation device 120 may evaluate a smoothness of the flat region 225 based on the respective gradient of the at least one unit region.
In some embodiments, for each of the at least one unit area, if the gradient of the unit area is determined to exceed the first threshold, the map evaluation device 120 may determine the unit area as a smoothness anomaly within the flat area 225.
Additionally or alternatively, in some embodiments, if the map evaluation device 120 determines that the smoothness outlier of the flat region 225 is less than a second threshold from the boundary of the flat region 225, 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 apparatus 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 apparatus 120 may determine the smoothness outlier as a smoothness outlier within the flat region 225. In some embodiments, the map evaluation device 120 may generate statistical information about smoothness alert 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 shows a block diagram of an example apparatus 500 for evaluating map quality, in accordance with an embodiment of the present disclosure.
As shown in fig. 5, the apparatus 500 includes an identifying module 510 configured to identify a flat area 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 area.
In some embodiments, the evaluation module 520 includes at least one of: a smoothness evaluation module configured to evaluate a smoothness of the flat region; and a thickness evaluation module configured to evaluate a 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 performing two-dimensional grid projection on the point cloud map, wherein the projection map is divided into a plurality of unit areas, and each unit area has a height mean 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 area identification module configured to identify a flat area in the projected map based on the 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 comprises: the acquisition unit is configured to acquire a pose graph corresponding to the point cloud map, wherein the pose graph comprises a set of poses of the acquisition equipment when the point cloud map is acquired; a first determination unit configured to determine 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 mean 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 the smoothness of the flat region based on the gradient of the at least one unit region.
In some embodiments, the second determination unit is configured to: generating a gray image by mapping the plurality of relative height values to gray values, respectively; and generating a gradient map of the grayscale image by edge detecting the grayscale image, the gradient map indicating respective gradients of the plurality of unit regions.
In some embodiments, the evaluation unit is configured to: for each unit area of the at least one unit area, if it is determined that the gradient of the unit area exceeds a first threshold, the unit area is determined as a smoothness anomaly point within the flat area.
In some embodiments, the evaluation unit is further configured to: if the distance between the smoothness abnormal point in the flat area and the boundary of the flat area is determined to be smaller than a second threshold value, determining the smoothness abnormal point as a smoothness alarm point in the flat area; if the distance between the smoothness abnormal point in the flat area and the boundary of the flat area is determined to exceed a second threshold value, determining the smoothness abnormal point as a smoothness error point in the flat area; and generating statistical information about smoothness alarm points and/or smoothness error points within the flat region.
In some embodiments, the thickness evaluation module comprises: a generation unit configured to generate a statistical result based on the respective height variances of the at least one unit area; 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 of the units may be implemented using software and/or firmware, such as machine executable instructions stored on a storage medium. In addition to, or in the alternative to, 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 may be used include Field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standards (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and so forth.
Fig. 6 illustrates a block diagram of an example electronic device 600 capable of implementing multiple embodiments of the present 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, device 600 includes a Central Processing Unit (CPU)601 that may perform various appropriate actions and processes in accordance with computer program instructions stored in a Read Only Memory (ROM)602 or 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 can also be stored. The CPU 601, ROM 602, and RAM 603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
A number of components in the device 600 are connected to the I/O interface 605, including: an input unit 606 such as a keyboard, a mouse, or the like; an output unit 607 such as various types of displays, speakers, and the like; a storage unit 608, such as a magnetic disk, optical disk, or the like; and a communication unit 609 such as a 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.
Various processes and processes 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 in 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 loaded into RAM 603 and executed by CPU 601, the computer programs may perform one or more of the acts of methods 300 and/or 400 described above.
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 carrying out various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: 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 coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical 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 via 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 transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter 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 carrying out operations of the present disclosure may be assembler 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 execute 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 type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
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 storing the instructions comprises 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 flowchart 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.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (18)
1. A method of assessing map quality, comprising:
identifying a flat area in a point cloud map based on semantic information of the point cloud map; and
evaluating a quality of the point cloud map by evaluating at least one attribute of the flat region.
2. The method of claim 1, wherein evaluating at least one attribute of the flat region comprises:
evaluating at least one of smoothness and thickness of the flat region.
3. The method of claim 2, wherein identifying a flat area in the point cloud map comprises:
obtaining a projection map corresponding to the point cloud map by performing two-dimensional grid projection on the point cloud map, wherein the projection map is divided into a plurality of unit areas, and each unit area has a height mean value and a height variance which are determined based on a height value of an original coordinate point corresponding to the unit area in the point cloud map; and
identifying the flat region in the projected map based on the semantic information, wherein the flat region includes at least one unit region of the plurality of unit regions.
4. The method of claim 3, wherein evaluating smoothness of the flat region comprises:
acquiring a pose graph corresponding to the point cloud map, wherein the pose graph comprises a set of poses of an acquisition device when the point cloud map is acquired;
determining a plurality of relative height values for the plurality of unit areas based on the respective height values for the set of poses and the respective height means for the plurality of unit areas in the projection map;
determining respective gradients of the plurality of unit regions based on the plurality of relative height values; and
evaluating smoothness of the flat region based on a gradient of the at least one unit region.
5. The method of claim 4, wherein determining 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
generating a gradient map of the grayscale image by edge detecting the grayscale image, the gradient map indicating respective gradients of the plurality of unit regions.
6. The method of claim 4, wherein evaluating smoothness of the flat region based on the respective gradient of the at least one unit region comprises:
for each unit area of the at least one unit area, if it is determined that the gradient of the unit area exceeds a first threshold, determining the unit area as a smoothness anomaly point within the flat area.
7. The method of claim 6, further comprising:
if the distance between the smoothness abnormal point in the flat area and the boundary of the flat area is determined to be smaller than a second threshold value, determining the smoothness abnormal point as a smoothness alarm point in the flat area;
determining a smoothness outlier within the flat region 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
generating statistical information about smoothness alert points and/or smoothness error points within the flat region.
8. The method of claim 3, wherein evaluating the thickness of the flat region comprises:
generating a statistical result based on the respective height variance of the at least one unit area; and
determining whether there is an anomaly in the thickness of the flat region based on the statistical result.
9. 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; and
an evaluation module configured to evaluate a quality of the point cloud map by evaluating at least one attribute of the flat region.
10. The apparatus of claim 9, wherein the evaluation module comprises at least one of:
a smoothness evaluation module configured to evaluate a smoothness of the flat region; and
a thickness evaluation module configured to evaluate a thickness of the flat region.
11. The apparatus of claim 9, wherein the identification module comprises:
a projection module configured to obtain a projection map corresponding to the point cloud map by performing two-dimensional grid projection on 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 corresponding to the unit area in the point cloud map; and
a flat region identification module configured to identify the flat region in the projected map based on the semantic information, wherein the flat region includes at least one unit region of the plurality of unit regions.
12. The apparatus of claim 11, 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 at the time of 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 the respective height values of the pose set and the respective height mean 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.
13. The apparatus of claim 12, wherein the second determining unit is configured to:
generating a gray image by mapping the plurality of relative height values to gray values, respectively; and
generating a gradient map of the grayscale image by edge detecting the grayscale image, the gradient map indicating respective gradients of the plurality of unit regions.
14. The apparatus of claim 12, wherein the evaluation unit is configured to:
for each unit area of the at least one unit area, if it is determined that the gradient of the unit area exceeds a first threshold, determining the unit area as a smoothness anomaly point within the flat area.
15. The apparatus of claim 14, wherein the evaluation unit is further configured to:
if the distance between the smoothness abnormal point in the flat area and the boundary of the flat area is determined to be smaller than a second threshold value, determining the smoothness abnormal point as a smoothness alarm point in the flat area;
determining a smoothness outlier within the flat region 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
generating statistical information about smoothness alert points and/or smoothness error points within the flat region.
16. The apparatus of claim 11, wherein the thickness evaluation module comprises:
a generating unit configured to generate a statistical result based on the respective height variances of the at least one unit area; 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.
17. An electronic device, comprising:
one or more processors; and
memory 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 1-8.
18. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-8.
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