CN111931704A - Method, apparatus, device and computer-readable storage medium for assessing map quality - Google Patents

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

Method, apparatus, device and computer-readable storage medium for assessing map quality

technical field

Embodiments of the present disclosure generally relate to the field of maps, and in particular, to a method, apparatus, device, and computer-readable storage medium for evaluating map quality.

Background technique

High-precision point cloud maps are usually used in application scenarios such as object detection and high-precision positioning in autonomous driving. The quality of high-precision point cloud maps will significantly affect the accuracy of object detection and high-precision positioning. Therefore, it is particularly important to evaluate the quality of high-precision point cloud maps.

SUMMARY OF THE INVENTION

Embodiments of the present disclosure provide a method, apparatus, device, and computer-readable storage medium for evaluating map quality.

In a first aspect of the present disclosure, a method of assessing map quality is provided. The method includes: identifying a flat area in the 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 area.

In a second aspect of the present disclosure, an apparatus for evaluating map quality is provided. The apparatus includes: a flat area identification module configured to identify a flat area in the point cloud map based on semantic information of the point cloud map; and a quality assessment module configured to evaluate at least one attribute of the flat area Evaluate the quality of the point cloud map.

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, when the one or more programs are executed by the one or more processors When the electronic device is made to implement the method according to the first aspect of the present disclosure.

In a fourth aspect of the present disclosure, there is provided a computer-readable storage medium having a computer program stored thereon. The computer program, when executed by a processor, implements any of the steps of the method described in accordance with the first aspect of the present 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 limit the scope of the disclosure.

Description of drawings

The above and other objects, features and advantages of the present disclosure will become more apparent from the more detailed description of the exemplary embodiments of the present disclosure, taken in conjunction with the accompanying drawings, wherein the same reference numerals generally refer to the exemplary embodiments of the present disclosure. same parts.

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;

3 shows a flowchart of an example method for assessing map quality according to an embodiment of the present disclosure;

4 shows a flowchart of an example method for evaluating smoothness of a flat region according to an embodiment of the present disclosure;

FIG. 5 shows a block diagram of an example apparatus for evaluating map quality according to an embodiment of the present disclosure; and

6 illustrates a block diagram of an example electronic device capable of implementing various embodiments of the present disclosure.

In the various figures, the same or corresponding reference numerals designate the same or corresponding parts.

Detailed ways

Preferred embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While 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 by 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.

As used herein, the term "including" and variations thereof mean open-ended inclusion, ie, "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", etc. may refer to different or the same objects. Other explicit and implicit definitions may also be included below.

As mentioned above, high-precision point cloud maps are usually used in application scenarios such as object detection and high-precision positioning in autonomous driving. The quality of high-precision point cloud maps will significantly affect the accuracy of object detection and high-precision positioning. Therefore, it is particularly important to evaluate the quality of high-precision point cloud maps. Some traditional schemes evaluate the quality of point cloud maps by comparing them with reference maps. 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 propose a solution for evaluating map quality, which can address one or more of the above-mentioned problems and other potential problems. In this scheme, the flat areas in the point cloud map are identified based on the semantic information of the point cloud map. The quality of the point cloud map is evaluated by evaluating at least one property of the flat area. In this way, the scheme can evaluate the quality of the point cloud map by analyzing the properties of the flat area (eg, road area, etc.) in the point cloud map without comparing the point cloud map with the reference map, so that the point cloud map can be compared. Automated assessment of map quality.

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 functionality of environment 100 are described for exemplary purposes only and do not imply any limitation on the scope of the present disclosure. For example, embodiments of the present disclosure may also be applied in environments other than environment 100 .

The map collection device 110 may include, but is not limited to, a collection vehicle or other device for collecting map data. For example, a lidar may be mounted on the map collection device 110 for collecting data. The map acquisition device 110 may be moved within a particular geographic area during the acquisition period to collect point cloud data for mapping. The "point cloud data" mentioned here may refer to the data information of various points on the object surface returned when a laser beam is irradiated on the object surface, including the three-dimensional coordinates of each point (for example, x coordinate, y coordinate and z-coordinates) and laser reflection intensity (also called "reflection value"). The map collection device 110 may generate a point cloud map 115 for a particular geographic area based on the collected point cloud data.

The point cloud map 115 may be provided to the map evaluation device 120 . The map evaluation device 120 may evaluate the quality of the point cloud map 115 by analyzing the properties of the road surface area in the point cloud map 115 to generate the 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 functionality 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 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 the projected map 215 corresponding to the point cloud map 115 by subjecting the point cloud map 115 to a two-dimensional grid projection. Projected map 215 may be divided into multiple grids (also referred to as "multiple unit regions"). For each grid, the height mean and height variance corresponding to the grid can be determined based on the height values of the original coordinate points corresponding to the grid in the point cloud map 115 . In addition, the reflection value mean and reflection value variance corresponding to the grid can be determined based on the reflection values of the original coordinate points corresponding to the grid in the point cloud map 115 . Projected map 215 is also referred to herein as a "two-dimensional projected map" or "two-dimensional grid map."

In some embodiments, in order to generate the projected map 215 corresponding to the point cloud map 115 , the projection module 210 may first perform coordinate point aggregation on the three-dimensional point cloud map 115 according to three-dimensional grids, wherein each three-dimensional grid has a predetermined side length cube. For each three-dimensional grid, the projection module 210 can calculate the weighted results of all original coordinate points falling therein, including the weighted results of height values and the weighted results of reflection values. The projection module 210 may count the average height value, the variance of the height value, the average value of the reflection value and the variance of the reflection value for each three-dimensional grid. After obtaining the statistical results of each three-dimensional grid, the projection module 210 may divide the three-dimensional grid into a plurality of grid sets, wherein each grid set has the same horizontal coordinates (ie, each grid set corresponds to the same x coordinate and the same y coordinate), each grid set is also referred to herein as a "pillar". The projection module 210 may perform Euclidean clustering on the points in each column to obtain multiple point clusters. The projection module 210 can calculate the height value weighted result and the reflection value weighted result of each point cluster. The projection module 210 can filter out aerial point clusters, dynamic point clusters, and point clusters with lower weights (eg, clusters with fewer points) in each column. The projection module 210 can calculate the mean height value, the height value variance, the reflection value mean value and the reflection value variance for each filtered column, thereby obtaining the projection map 215. For example, the plane area corresponding to each column is in the projection map 215. a unit area.

The generation of the projected map 215 has been described above for example 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 regard.

In some embodiments, the flat area identification module 220 is configured to identify the flat area 225 in the projected map 215 based on the semantic information of the flat area. Examples of flat areas may include, but are not limited to, pavement areas such as driveways, sidewalks, plazas, and the like. The semantic information of the flat region can 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 to generate 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 To evaluate the thickness of the flat region 225 .

The smoothness of the flat area 225 is mainly reflected by the height change of the flat area 225 . In some embodiments, in order to evaluate the smoothness of the flat area 225 , the smoothness evaluation module 231 may obtain a pose map corresponding to the point cloud map 115 , the pose map including the map collection device 110 when collecting the point cloud map 115 . Pose collection. In some embodiments, for each unit region of the plurality of unit regions, the smoothness assessment module 231 may determine from the pose map at least one pose associated with the unit region. For example, the smoothness evaluation module 231 may determine at least one pose located within the vicinity of the unit area. The determination of the proximity range of the unit area may be implemented based on a K-Nearest Neighbor (KNN) algorithm or other similar algorithms, and the scope of the present disclosure is not limited in this regard. In some embodiments, the smoothness evaluation module 231 may determine the relative height value of the unit area based on the mean height of the unit area in the projected map 215 and the corresponding height value of at least one pose. For example, the smoothness evaluation module 231 may perform a weighted average of the corresponding height values of the at least one pose to obtain a height-weighted average value of the at least one pose. The weight of the height value of each pose in the at least one pose may be determined based on the following formula:

where x _p,i represents the x coordinate of the ith pose, x _cell represents the x coordinate of the unit area, y _p,i represents the y coordinate of the ith pose and y _cell represents the y coordinate of the unit area. MAX_WEIGHT represents the preset maximum weight, for example, it is less than 1. The smoothness evaluation module 231 may calculate the difference between the height mean value of the unit area and the height weighted mean value of the at least one pose in the projected map 215 as the relative height value of the unit area.

In some embodiments, the smoothness evaluation module 231 may generate a grayscale image by respectively mapping a plurality of relative height values of a plurality of unit regions into grayscale values (eg, ranging from 0 to 255). The smoothness evaluation module 231 can 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, and the scope of the present disclosure is not limited in this regard. Additionally or alternatively, the smoothness assessment module 231 may binarize the gradient map, where a unit region with a value of 0 represents a region with a height variation below a predetermined threshold (also referred to herein as a "first threshold"), Whereas, a unit area with a value other than 0 represents an area where the change in height exceeds a predetermined threshold (also referred to herein as a "first threshold"). The smoothness evaluation module 231 may, for each unit area in the at least one unit area included in the flat area 225, determine whether the height change of the unit area exceeds a first threshold (that is, whether there is an inconsistency in the binarized gradient map). a gradient value of 0). If a certain unit area has a gradient value other than 0 in the binarized gradient map, the map evaluation apparatus 120 may determine the unit area as a smoothness abnormal point within the flat area 225 .

Additionally or alternatively, in some embodiments, the smoothness evaluation module 231 may further determine whether the distance between the smoothness outlier in the flat region 225 and the boundary of the flat region 225 is less than a predetermined threshold (also referred to herein as "the first"). two thresholds"). If the smoothness outlier is located near the boundary of the flat area 225 (ie, the distance from the boundary of the flat area 225 is lower than the second threshold), the smoothness evaluation module 231 may identify the smoothness outlier as being within the flat area 225 The smoothness alarm point of , which indicates that the smoothness abnormality may be caused by the error in the semantic information of the flat area. If the smoothness outlier is not near the boundary of the flat area 225 (ie, the distance from the boundary of the flat area 225 exceeds the second threshold), the smoothness evaluation module 231 may identify the smoothness outlier as a smoothness outlier within the flat area 225 Smoothness error points, which indicate that smoothness anomalies may be due to errors in the point cloud map. In some embodiments, the smoothness assessment module 231 may generate statistical information about smoothness warning points and/or smoothness error points within the flat region 225 as a smoothness assessment result. For example, the statistical information may indicate whether there are smoothness warning points and/or smoothness error points in the flat area 225, their specific locations and proportions, and the like.

The smoothness of the flat area 225 is mainly reflected by the range of height variation (ie, height variance) of the flat area 225 . In some embodiments, in order to evaluate whether the thickness of the flat region 225 is abnormal, the thickness evaluation module 232 may generate statistical results based on the corresponding height variance of at least one unit region included in the flat region 225 . The statistical results may include mean, maximum, minimum, etc. of the height variance. The thickness evaluation module 232 may determine whether there is an abnormality in the thickness of the flat region 225 based on the statistical result, 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 properties of flat areas (eg, road areas, etc.) in the point cloud map without comparing the point cloud map with a reference map, thereby enabling Automated assessment of the quality of point cloud maps. The evaluation results 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 shows a flowchart of an example method 300 for evaluating map quality according to an embodiment of the present disclosure. The method 300 may be performed, for example, at the map evaluation device 120 as shown in FIGS. 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 blocks shown may be omitted. The scope of the present disclosure is not limited in this regard.

At block 310 , the map evaluation device 120 identifies flat regions 225 in the point cloud map 115 based on the semantic information of the point cloud map 115 .

In some embodiments, in order to identify the flat area 225, the map evaluation device 120 may obtain the projected map 215 corresponding to the point cloud map 115 by subjecting the point cloud map 115 to a two-dimensional grid projection, wherein the projected map 215 is divided into multiple Each unit area has a height mean value and a height variance determined based on the height values of the original coordinate points corresponding to the unit area in the point cloud map 115 . The map evaluation device 120 may identify a flat area 225 in the projected map 215 based on the semantic information, where the flat area 225 includes at least one unit area of the plurality of unit areas.

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, map evaluation device 120 may evaluate the quality of point cloud map 115 by evaluating at least one of smoothness and thickness of flat regions 225 .

In some embodiments, to assess the thickness of the flat region 225, the map evaluation device 120 may generate statistics based on the respective height variances of at least one unit region, and then determine whether there is an abnormality in the thickness of the flat region 225 based on the statistics.

In some embodiments, to assess the smoothness of the flat area 225, the map evaluation device 120 may determine smoothness outliers, smoothness warning points, and/or smoothness error points within the flat area 225, and generate associated statistics.

FIG. 4 shows a flowchart of an example method 400 for evaluating smoothness of a flat region according to an embodiment of the present disclosure. Method 400 may be viewed as an example implementation of block 330 , which may be performed, for example, at map evaluation device 120 as shown in FIGS. 1 and 2 . It should be understood that method 400 may also include blocks not shown and/or blocks shown may be omitted. The scope of the present disclosure is not limited in this regard.

As shown in FIG. 4 , at block 410 , the map evaluation device 120 may obtain a pose map corresponding to the point cloud map 115 , the pose map including the set of poses of the map acquisition device 110 when the point cloud map 115 was collected.

At block 420, the map evaluation device 120 determines a plurality of relative height values for the plurality of unit areas based on the projected map 215 and the acquired pose map.

In some embodiments, the map evaluation device 120 may, for each unit area of the plurality of unit areas, determine at least one pose associated with the unit area from the set of poses, and then, based on the unit area in the projected map 215 , determine at least one pose associated with the unit area. The average height of the area and the corresponding height value of at least one pose are used to determine the relative height value of the unit area.

At block 430, the map evaluation device 120 may generate a grayscale image by respectively mapping the plurality of relative height values of the plurality of unit areas into grayscale values.

At block 440, the map evaluation device 120 may generate a gradient map of the grayscale image by performing edge detection on the grayscale image, the gradient map indicating the corresponding 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 corresponding gradient of the at least one unit region.

In some embodiments, for each unit area in the at least one unit area, if it is determined that the gradient of the unit area exceeds the first threshold, the map evaluation device 120 may determine the unit area as an abnormal smoothness within the flat area 225 point.

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 of the flat region 225. The outliers are identified as smoothness warning points 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 the flat region 225 Smoothness error points within. In some embodiments, map evaluation device 120 may generate statistical information related to smoothness warning points and/or smoothness error points within flat region 225 .

Embodiments of the present disclosure also provide corresponding apparatuses for implementing the above-mentioned methods 300 and/or 400 . FIG. 5 shows a block diagram of an example apparatus 500 for evaluating map quality according to an embodiment of the present disclosure.

As shown in FIG. 5 , the apparatus 500 includes an identification module 510 configured to identify flat areas in the point cloud map based on semantic information of the point cloud map. The apparatus 500 also includes 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 the smoothness of the flat region; and a thickness evaluation module configured to evaluate 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 the point cloud map by performing a two-dimensional grid projection on the point cloud map, wherein the projection map is divided into a plurality of unit areas , each unit area has a height mean and height variance determined based on the height value of the original coordinate point corresponding to the unit area in the point cloud map; A flat area, 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 obtaining unit configured to obtain a pose map corresponding to the point cloud map, the pose map including a pose set of the collecting device when collecting the point cloud map; a first determining unit , is configured to determine multiple relative height values of multiple unit areas based on the corresponding height values of the pose set and the corresponding height mean values of multiple unit areas in the projection map; the second determination unit is configured to be based on multiple relative heights. 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 determining unit is configured to: generate a grayscale image by respectively mapping a plurality of relative height values to grayscale values; and generate a gradient of the grayscale image by performing edge detection on the grayscale image Figure, the gradient map indicates the corresponding gradients of multiple unit regions.

In some embodiments, the evaluation unit is configured to: for each unit area in the at least one unit area, if it is determined that the gradient of the unit area exceeds the first threshold, determine the unit area as a smoothness outlier in the flat area .

In some embodiments, the evaluation unit is further configured to: if it is determined that the distance between the smoothness abnormal point in the flat area and the boundary of the flat area is less than the second threshold, determine the smoothness abnormal point as a smoothness alarm in the flat area If it is determined that the distance between the smoothness abnormal point in the flat area and the boundary of the flat area exceeds the second threshold, determine the smoothness abnormal point as a smoothness error point in the flat area; and generate a smoothness error point in the flat area. Statistics related to degree warning points and/or smoothness error points.

In some embodiments, the thickness assessment module includes: a generating unit configured to generate a statistical result based on the corresponding height variance of the at least one unit area; and a third determining unit configured to determine the thickness of the flat area based on the statistical result Is there an exception.

The modules and units included in the apparatus 500 may be implemented in various manners, 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 as an 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 chips (SOCs), complex programmable logic devices (CPLD), etc.

6 illustrates a block diagram of an example electronic device 600 capable of implementing various embodiments of the present disclosure. For example, map evaluation device 120 as shown in FIG. 1 may be implemented by device 600 . As shown in FIG. 6, device 600 includes a central processing unit (CPU) 601 that may be loaded into random access memory (RAM) 603 according to computer program instructions stored in read only memory (ROM) 602 or from storage unit 608 computer program instructions to perform various appropriate actions and processes. In the RAM 603, various programs and data necessary for the operation of the device 600 can also be stored. The CPU 601 , the ROM 602 , and the 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, such as a keyboard, mouse, etc.; an output unit 607, such as various types of displays, speakers, etc.; a storage unit 608, such as a magnetic disk, an optical disk, etc. ; and a communication unit 609, such as a network card, a modem, a wireless communication transceiver, and the like. The communication unit 609 allows the device 600 to exchange information/data with other devices through a computer network such as the Internet and/or various telecommunication networks.

The various procedures and processes described above, eg, 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 on device 600 via ROM 602 and/or communication unit 609 . When a computer program is loaded into RAM 603 and executed by CPU 601, one or more of the actions of methods 300 and/or 400 described above may be performed.

The present disclosure may be a method, apparatus, system and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions loaded thereon for carrying out various aspects of the present disclosure.

A computer-readable storage medium may be a tangible device that can hold and store instructions for use by the instruction execution device. The computer-readable storage medium may be, for example, but not limited to, an electrical 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 (non-exhaustive list) of computer readable storage media include: portable computer disks, hard disks, random access memory (RAM), read only memory (ROM), erasable programmable read only memory (EPROM) or flash memory), static random access memory (SRAM), portable compact disk read only memory (CD-ROM), digital versatile disk (DVD), memory sticks, floppy disks, mechanically coded devices, such as printers with instructions stored thereon Hole cards or raised structures in grooves, and any suitable combination of the above. Computer-readable storage media, as used herein, are not to be construed as transient signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (eg, light pulses through fiber optic cables), or through electrical wires transmitted electrical signals.

The computer readable program instructions described herein may be downloaded to various computing/processing devices from a computer readable storage medium, 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 transmission, wireless transmission, routers, firewalls, switches, gateway computers, and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer-readable program instructions from a network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in each computing/processing device .

Computer program instructions for carrying out operations of the present disclosure may be assembly instructions, instruction set architecture (ISA) instructions, machine instructions, machine-dependent instructions, microcode, firmware instructions, state setting data, or instructions in one or more programming languages. Source or object code, written in any combination, including object-oriented programming languages, such as Smalltalk, C++, etc., and conventional procedural programming languages, such as the "C" 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 implement. 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 (eg, using an Internet service provider through the Internet connect). In some embodiments, custom electronic circuits, such as programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), can be personalized by utilizing state information of computer readable program instructions. Computer readable program instructions are executed to implement various aspects of the present disclosure.

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 that causes the instructions when executed by the processing unit of the computer or other programmable data processing apparatus , resulting in means for implementing the functions/acts specified in one or more blocks of the flowchart and/or block diagrams. These computer readable program instructions can also be stored in a computer readable storage medium, these instructions cause a computer, programmable data processing apparatus and/or other equipment to operate in a specific manner, so that the computer readable medium on which the instructions are stored includes An article of manufacture comprising instructions for implementing various aspects of the functions/acts specified in one or more blocks of the flowchart and/or block diagrams.

Computer readable program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other equipment to cause a series of operational steps to be performed on the computer, other programmable data processing apparatus, or other equipment to produce a computer-implemented process , thereby causing instructions executing on a computer, other programmable data processing apparatus, or other device to implement the functions/acts specified in one or more blocks of the flowcharts and/or block diagrams.

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 functions for implementing the specified logical function(s) executable instructions. In some alternative implementations, the functions noted in the blocks may occur out of the order noted in the figures. For example, two blocks 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 is also noted that each block of the block diagrams and/or flowchart illustrations, and combinations of blocks in the block diagrams and/or flowchart illustrations, can be implemented in dedicated hardware-based systems that perform the specified functions or actions , or can be implemented in a combination of dedicated hardware and computer instructions.

Various embodiments of the present disclosure have been described above, and the foregoing descriptions are exemplary, not exhaustive, and not limiting of the disclosed embodiments. Numerous 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 was chosen to best explain the principles of the various embodiments, the practical application or improvement over the technology in the marketplace, or to enable others of ordinary skill in the art to understand the various 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.

Images