Intelligent bridge inspection vehicle pier identification method and device based on laser radar

Abstract

The invention relates to the technical field of intelligent detection, in particular to an intelligent bridge inspection vehicle pier recognition method and device based on a laser radar, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring point cloud data of a target bridge; dividing the point cloud data based on a preset reference direction and intervals, and dividing the point cloud data into a plurality of point cloud intervals; traversing and counting histogram features of the point cloud data in each point cloud interval, and screening the point cloud intervals based on the histogram features; and determining pier information of the target bridge according to the plurality of screened target point cloud intervals. By adopting the method, the detection accuracy of the intelligent bridge inspection vehicle can be improved.

Description

Technical Field

[0001] The present application relates to the field of intelligent detection technology, and in particular to a method, device, computer equipment, storage medium and computer program product for bridge pier recognition by an intelligent bridge inspection vehicle based on laser radar. Background Art

[0002] A bridge inspection vehicle is a special vehicle used to inspect and maintain engineering structures such as bridges and tunnels. Bridge inspection vehicles are usually equipped with various testing equipment to evaluate and test the structure, materials and status of bridges.

[0003] In the related art, inspectors can use bridge inspection vehicles to conduct inspections and get close to bridge structures for observation and measurement. However, current bridge inspection vehicles may face a variety of interference scenarios during inspections, resulting in a high error rate in the inspection results. Summary of the invention

[0004] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for bridge pier identification of an intelligent bridge inspection vehicle based on laser radar, which can improve the detection accuracy of the intelligent bridge inspection vehicle in response to the above technical problems.

[0005] In a first aspect, the present application provides a method for identifying bridge piers by an intelligent bridge inspection vehicle based on laser radar. The method comprises:

[0006] Acquire point cloud data of the target bridge;

[0007] Dividing the point cloud data into a plurality of point cloud intervals based on a preset reference direction and interval;

[0008] Traversing and counting the histogram features of the point cloud data in each of the point cloud intervals, and screening the point cloud intervals based on the histogram features;

[0009] The pier information of the target bridge is determined according to the screened target point cloud intervals.

[0010] In one embodiment, the traversing and counting the histogram features of the point cloud data in each point cloud interval, and screening the point cloud interval based on the histogram features comprises:

[0011] Obtaining height features and width features of the point cloud interval;

[0012] The point cloud interval is filtered based on a preset height threshold and a width threshold to filter out interference targets in the point cloud interval.

[0013] In one embodiment, the traversing and counting the histogram features of the point cloud data in each point cloud interval, and screening the point cloud interval based on the histogram features comprises:

[0014] Determine an area characteristic value of the point cloud interval based on the height feature and the width feature;

[0015] The point cloud interval is filtered based on a preset area threshold to filter out interference targets in the point cloud interval.

[0016] In one embodiment, the step of dividing the point cloud data based on a preset reference direction and interval, and dividing the point cloud data into a plurality of point cloud intervals, further comprises:

[0017] Performing cluster analysis on the point cloud data in the point cloud interval based on a preset cluster analysis algorithm;

[0018] The point cloud interval is segmented based on the result of the cluster analysis to obtain candidate targets that meet preset conditions, thereby forming a set of candidate targets.

[0019] In one embodiment, determining the pier information of the target bridge according to the screened target point cloud intervals includes:

[0020] Determine the geometric area and the scanning area of ​​the candidate target according to the point cloud data, and use the product of the geometric area and the scanning area as a positioning feature value;

[0021] The location information of the target bridge pier is determined according to the point cloud data corresponding to the maximum value of the positioning feature value.

[0022] In one embodiment, the method further comprises:

[0023] Acquiring auxiliary positioning feature information on the target bridge;

[0024] The pier information of the target bridge is determined based on the point cloud data and the auxiliary positioning feature information.

[0025] In a second aspect, the present application also provides a bridge pier recognition device for an intelligent bridge inspection vehicle based on laser radar. The device comprises:

[0026] A point cloud data module, used for acquiring point cloud data of the target bridge;

[0027] An interval division module, used to divide the point cloud data into a plurality of point cloud intervals based on a preset reference direction and interval;

[0028] A histogram feature module, used for traversing and counting the histogram features of the point cloud data in each point cloud interval, and screening the point cloud interval based on the histogram features;

[0029] The pier information module is used to determine the pier information of the target bridge according to the screened target point cloud intervals.

[0030] In one embodiment, the histogram feature module includes:

[0031] A size feature module, used to obtain the height feature and width feature of the point cloud interval;

[0032] The first interference filtering module is used to filter the point cloud interval based on a preset height threshold and a width threshold to filter out interference targets in the point cloud interval.

[0033] In one embodiment, the histogram feature module includes:

[0034] An area feature module, used to determine the area feature value of the point cloud interval based on the height feature and the width feature;

[0035] The second interference filtering module is used to filter the point cloud interval based on a preset area threshold to filter out interference targets in the point cloud interval.

[0036] In one embodiment, after the interval division module, it also includes:

[0037] A cluster analysis module, used for performing cluster analysis on the point cloud data in the point cloud interval based on a preset cluster analysis algorithm;

[0038] The target segmentation module is used to segment the point cloud interval based on the result of the cluster analysis to obtain candidate targets that meet preset conditions and form a set of candidate targets.

[0039] In one embodiment, the pier information module includes:

[0040] A positioning feature value module, used to determine the geometric area and the scanning area of ​​the candidate target according to the point cloud data, and use the product of the geometric area and the scanning area as the positioning feature value;

[0041] The pier positioning module is used to determine the position information of the target pier according to the point cloud data corresponding to the maximum value of the positioning characteristic value.

[0042] In one embodiment, the device further comprises:

[0043] An auxiliary positioning feature module, used to obtain auxiliary positioning feature information on the target bridge;

[0044] The auxiliary positioning module is used to determine the pier information of the target bridge based on the point cloud data and the auxiliary positioning feature information.

[0045] In a third aspect, the present application further provides a computer device. The computer device includes a memory and a processor, the memory stores a computer program, and the processor implements the steps in the method for identifying bridge piers of an intelligent bridge inspection vehicle based on laser radar when executing the computer program as described in any one of the embodiments in the first aspect.

[0046] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps in the method for identifying bridge piers of an intelligent bridge inspection vehicle based on laser radar are implemented as described in any one of the embodiments in the first aspect.

[0047] In a fifth aspect, the present application further provides a computer program product, which includes a computer program, and when the computer program is executed by a processor, the steps in the method for identifying bridge piers of an intelligent bridge inspection vehicle based on laser radar are implemented as described in any one of the embodiments in the first aspect.

[0048] The above-mentioned method, device, computer equipment, storage medium and computer program product for bridge pier recognition by an intelligent bridge inspection vehicle based on laser radar can achieve the beneficial effects of the technical problems in the corresponding background technology by deducing the technical features in the claims:

[0049] The present application provides a method for identifying bridge piers of an intelligent bridge inspection vehicle based on laser radar, comprising acquiring point cloud data of the target bridge; dividing the point cloud data into a plurality of point cloud intervals based on preset reference directions and intervals; traversing and counting the histogram features of the point cloud data in each of the point cloud intervals, and filtering the point cloud intervals based on the histogram features; and determining the pier information of the target bridge based on the plurality of target point cloud intervals obtained by filtering. In implementation, by acquiring the point cloud data of the target bridge, the point cloud data is divided according to the reference direction and the preset interval, thereby dividing the point cloud data into several point cloud intervals (bins), so that the histogram features of each point cloud interval can be statistically analyzed in intervals, and independent calculation and comparison of different point cloud intervals can be achieved, so as to achieve the purpose of statistical analysis of a large amount of point cloud data. Finally, the point cloud intervals are screened based on the histogram features to obtain several groups of qualified point cloud intervals, and the bridge pier information is determined according to the point cloud data in the point cloud intervals. This helps to reduce the impact of interference items in detection and improve the accuracy of bridge pier identification through statistics, analysis, comparison and screening of multiple groups of point cloud data. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 This is a diagram of an application environment of a bridge pier recognition method for an intelligent bridge inspection vehicle based on laser radar in one embodiment;

[0051] Figure 2 It is a schematic diagram of a first process of a bridge pier recognition method of an intelligent bridge inspection vehicle based on laser radar in one embodiment;

[0052] Figure 3 It is a second flow chart of a bridge pier recognition method of an intelligent bridge inspection vehicle based on laser radar in another embodiment;

[0053] Figure 4 A third flow chart of a method for identifying bridge piers by an intelligent bridge inspection vehicle based on laser radar in another embodiment;

[0054] Figure 5 A schematic diagram of a fourth process of a bridge pier identification method of an intelligent bridge inspection vehicle based on laser radar in another embodiment;

[0055] Figure 6 A fifth flow chart of a method for identifying bridge piers by an intelligent bridge inspection vehicle based on laser radar in another embodiment;

[0056] Figure 7 A sixth flow chart of a method for identifying bridge piers by an intelligent bridge inspection vehicle based on laser radar in another embodiment;

[0057] Figure 8 It is a structural block diagram of a bridge pier recognition device of an intelligent bridge inspection vehicle based on laser radar in one embodiment;

[0058] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0059] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0060] A bridge inspection vehicle is a special vehicle used to inspect and maintain engineering structures such as bridges and tunnels. Bridge inspection vehicles are usually equipped with various testing equipment to evaluate and test the structure, materials and status of bridges.

[0061] In the related art, inspectors can use bridge inspection vehicles to conduct inspections and get close to bridge structures for observation and measurement. However, current bridge inspection vehicles may face a variety of interference scenarios during inspections, resulting in a high error rate in the inspection results.

[0062] Based on the above problems, the embodiments of the present application provide a method, device, computer equipment, storage medium and computer program product for bridge pier identification by an intelligent bridge inspection vehicle based on laser radar.

[0063] The embodiment of the present application provides a method for identifying bridge piers of an intelligent bridge inspection vehicle based on laser radar, which can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The terminal can be set on an intelligent bridge inspection vehicle for implementing bridge inspection tasks, and the intelligent bridge inspection vehicle can also be provided with. The server 104 can be implemented by an independent server or a server cluster composed of multiple servers.

[0064] In one embodiment, Figure 2 As shown in the figure, a bridge pier recognition method based on laser radar is provided for intelligent bridge inspection vehicle. Figure 1 The terminal in is used as an example to illustrate, including the following steps:

[0065] Step 202: Acquire point cloud data of the target bridge.

[0066] Step 204: Divide the point cloud data into a plurality of point cloud intervals based on preset reference directions and intervals.

[0067] Step 206: traverse and count the histogram features of the point cloud data in each point cloud interval, and filter the point cloud interval based on the histogram features.

[0068] Step 208: Determine the pier information of the target bridge according to the screened target point cloud intervals.

[0069] For example, the terminal can obtain the laser radar point cloud data, set the x-axis as the driving direction of the bridge inspection vehicle, divide the point cloud according to the x-axis direction, and divide the point cloud by interval ∆d, and count the histogram features. In the actual scene, the bridge piers appear on the front and rear sides of the bridge inspection vehicle. Therefore, the histogram is filtered out according to the positive and negative directions of the x-axis to select the first k bins with the largest number of point clouds, that is, the point cloud interval. Finally, the bridge pier information of the target bridge is determined based on the filtered bins.

[0070] In the above-mentioned method for identifying bridge piers of an intelligent bridge inspection vehicle based on laser radar, reasonable deduction is performed in combination with the technical features in the embodiment, which can achieve the following beneficial effects of solving the technical problems raised in the background technology:

[0071] The present application provides a method for identifying bridge piers of an intelligent bridge inspection vehicle based on laser radar, comprising acquiring point cloud data of the target bridge; dividing the point cloud data into a plurality of point cloud intervals based on preset reference directions and intervals; traversing and counting the histogram features of the point cloud data in each of the point cloud intervals, and filtering the point cloud intervals based on the histogram features; and determining the pier information of the target bridge based on the plurality of target point cloud intervals obtained by filtering. In implementation, by acquiring the point cloud data of the target bridge, the point cloud data is divided according to the reference direction and the preset interval, thereby dividing the point cloud data into several point cloud intervals (bins), so that the histogram features of each point cloud interval can be statistically analyzed in intervals, and independent calculation and comparison of different point cloud intervals can be achieved, so as to achieve the purpose of statistical analysis of a large amount of point cloud data. Finally, the point cloud intervals are screened based on the histogram features to obtain several groups of qualified point cloud intervals, and the bridge pier information is determined according to the point cloud data in the point cloud intervals. This helps to reduce the impact of interference items in detection and improve the accuracy of bridge pier identification through statistics, analysis, comparison and screening of multiple groups of point cloud data.

[0072] In one embodiment, if Figure 3 As shown, step 206 includes:

[0073] Step 302: Obtain the height feature and width feature of the point cloud interval.

[0074] Step 304: Filter the point cloud interval based on a preset height threshold and a width threshold to filter out interference targets in the point cloud interval.

[0075] For example, since the bridge pier has a large area, it occupies the largest number of points in the histogram. However, in order to avoid the radar generating a large number of points in the bridge structure near the radar, the point cloud height can be limited, and the width and height thresholds can be designed to filter out interference targets.

[0076] In this embodiment, by screening the height features and width features of the point cloud interval, it helps to reduce the possibility of a large number of points generated by the bridge structure near the radar, which may cause detection interference, and improve the accuracy of bridge pier identification.

[0077] In one embodiment, if Figure 4 As shown, step 208 includes:

[0078] Step 402: Determine the area feature value of the point cloud interval based on the height feature and the width feature.

[0079] Step 404: Filter the point cloud interval based on a preset area threshold to filter out interference targets in the point cloud interval.

[0080] For example, for a large bridge, when the bridge inspection vehicle travels to the middle position, the bridge piers are usually not visible. Therefore, according to step 2, the screened targets need to be further filtered by calculating the area threshold to avoid false detection and miss detection of real obstacles.

[0081] The situations that are likely to cause false detection are analyzed as follows.

[0082] Case 1: When the point clouds of the top k bins with the largest number of points are filtered out according to the histogram and the width and height of each bin point cloud set are calculated, it may happen that the points in the bin are sparse interference points, but the height and width values ​​are large, resulting in misdetection.

[0083] Case 2: When the distance to the bridge pier is close, the height and width of the bridge pier captured by the radar are smaller, resulting in the bridge pier being missed.

[0084] In this embodiment, by filtering the area characteristics of the point cloud interval, in the detection scenario of a large bridge, when the intelligent bridge inspection vehicle travels to the middle position, the bridge piers are invisible, resulting in the possibility that real obstacles may be missed, thereby improving the safety of the intelligent bridge inspection vehicle's obstacle avoidance control and the accuracy of bridge pier detection.

[0085] In one embodiment, if Figure 5 As shown, after step 204, the following steps are further included:

[0086] Step 502: performing cluster analysis on the point cloud data in the point cloud interval based on a preset cluster analysis algorithm.

[0087] Step 504: Segment the point cloud interval based on the result of the cluster analysis to obtain candidate targets that meet preset conditions, and form a set of candidate targets.

[0088] In this embodiment, cluster analysis is used to effectively suppress the problem of misdetection caused by sparse point clouds, thereby improving the accuracy of pier target detection.

[0089] In one embodiment, if Figure 6 As shown, step 208 includes:

[0090] Step 602: Determine the geometric area and the scanning area of ​​the candidate target according to the point cloud data, and use the product of the geometric area and the scanning area as a positioning feature value.

[0091] Step 604: Determine the location information of the target bridge pier according to the point cloud data corresponding to the maximum value of the positioning feature value.

[0092] Exemplarily, at this time, the area of ​​the candidate target is calculated. The geometric area of ​​the candidate target is calculated by finding the maximum and minimum values ​​of the y-axis and z-axis in the set, and the width and height of the candidate target are calculated according to the difference between the maximum and minimum values ​​of each axis. At this time, the misdetection problem caused by sparse point clouds can be effectively suppressed. In addition, the scanning area occupied by the point cloud in the original bin is calculated according to the following formula:

[0093]

[0094] In the above formula, represents the area of ​​the i-th target, ɑ represents the radian value of the horizontal angular resolution, β represents the radian value of the vertical angular resolution, Represents the jth point cloud of the i-th candidate target. This area describes the actual area of ​​the candidate target scan point cloud, which can be used to characterize the distance of the target and magnify the distant target.

[0095] The final candidate target area calculation formula can be shown as follows:

[0096]

[0097] The reason for direct multiplication is to take into account that the bridge is not photographed comprehensively at close distance, and the simple scanning area or geometric area does not have an absolute advantage. The multiplication will have more obvious characteristics.

[0098] The final candidate target area calculation formula can also be shown as follows:

[0099]

[0100] As the distance from the bridge pier increases, the scanning area meets the requirements more. Therefore, the first calculation formula is modified. Where x is the current position of the bridge inspection vehicle, l is the total length of the bridge, It means that the closer the bridge inspection vehicle is to the two ends, the greater the gain of the geometric area to the scanning area. When it is closer to the middle of the bridge, the geometric area gain is close to 1.

[0101] In this embodiment, the distance value of the target pier is determined by the product of the geometric area and the scanning area, thereby improving the accuracy of the pier position detection.

[0102] In one embodiment, if Figure 7 As shown, the method also includes:

[0103] Step 702: Acquire auxiliary positioning feature information on the target bridge.

[0104] Among them, the auxiliary positioning feature can be a reflective sticker or the like.

[0105] Step 704: Determine the pier information of the target bridge based on the point cloud data and the auxiliary positioning feature information.

[0106] Exemplarily, considering the defect that the stability of the geometric area feature is poor when close to the pier, a reflective sticker feature is added.

[0107] The specific process is as follows:

[0108] Paste two reflective stickers with a size of m*m on the pier, and assume the spacing between the reflective stickers is d. (Size requirement: within the range of n meters from the pier inspection vehicle to the pier, if the pier point cloud is severely missing, it is necessary to ensure that the reflective stickers can be stably detected)

[0109] Within the range of n meters from the pier inspection vehicle to the pier, filter out the reflective sticker point cloud through the reflection intensity, use clustering means to segment the target, pair the segmented targets pairwise, calculate the spacing d1, and judge whether d1 - d < T. If it meets the condition, the pairing is successful. Further judge whether the absolute value of the depth difference between the two reflective stickers is less than the threshold abs(x1 - x2) < T2. When all the above conditions are met, the depth value is determined as the pier position.

[0110] When outside the range of n meters from the pier inspection vehicle to the pier, geometric feature detection is used.

[0111] In this embodiment, by setting auxiliary positioning features such as reflective stickers on the target bridge, the problem of missing pier point cloud can be overcome through the auxiliary positioning feature information, thereby realizing the auxiliary positioning of the pier and improving the stability of pier detection.

[0112] It should be understood that although the steps in the flowcharts involved in the above-described embodiments are sequentially shown according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-described embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps in other steps.

[0113] Based on the same inventive concept, the embodiment of the present application also provides a laser radar-based intelligent bridge inspection vehicle bridge pier identification device for implementing the above-mentioned laser radar-based intelligent bridge inspection vehicle bridge pier identification method. The implementation scheme for solving the problem provided by the device is similar to the implementation scheme recorded in the above-mentioned method, so the specific limitations in one or more embodiments of the laser radar-based intelligent bridge inspection vehicle bridge pier identification device provided below can refer to the above-mentioned limitations on the laser radar-based intelligent bridge inspection vehicle bridge pier identification method, and will not be repeated here.

[0114] In one embodiment, Figure 8 As shown, a bridge pier recognition device for an intelligent bridge inspection vehicle based on laser radar is provided, comprising: a point cloud data module, an interval division module, a histogram feature module and a bridge pier information module, wherein:

[0115] A point cloud data module, used for acquiring point cloud data of the target bridge;

[0116] An interval division module, used to divide the point cloud data into a plurality of point cloud intervals based on a preset reference direction and interval;

[0117] A histogram feature module, used for traversing and counting the histogram features of the point cloud data in each point cloud interval, and screening the point cloud interval based on the histogram features;

[0118] The pier information module is used to determine the pier information of the target bridge according to the screened target point cloud intervals.

[0119] In one embodiment, the histogram feature module includes:

[0120] A size feature module, used to obtain the height feature and width feature of the point cloud interval;

[0121] The first interference filtering module is used to filter the point cloud interval based on a preset height threshold and a width threshold to filter out interference targets in the point cloud interval.

[0122] In one embodiment, the histogram feature module includes:

[0123] An area feature module, used to determine the area feature value of the point cloud interval based on the height feature and the width feature;

[0124] The second interference filtering module is used to filter the point cloud interval based on a preset area threshold to filter out interference targets in the point cloud interval.

[0125] In one embodiment, after the interval division module, it also includes:

[0126] A cluster analysis module, used for performing cluster analysis on the point cloud data in the point cloud interval based on a preset cluster analysis algorithm;

[0127] The target segmentation module is used to segment the point cloud interval based on the result of the cluster analysis to obtain candidate targets that meet preset conditions and form a set of candidate targets.

[0128] In one embodiment, the pier information module includes:

[0129] A positioning feature value module, used to determine the geometric area and the scanning area of ​​the candidate target according to the point cloud data, and use the product of the geometric area and the scanning area as the positioning feature value;

[0130] The pier positioning module is used to determine the position information of the target pier according to the point cloud data corresponding to the maximum value of the positioning characteristic value.

[0131] In one embodiment, the device further comprises:

[0132] An auxiliary positioning feature module, used to obtain auxiliary positioning feature information on the target bridge;

[0133] The auxiliary positioning module is used to determine the pier information of the target bridge based on the point cloud data and the auxiliary positioning feature information.

[0134] Each module in the above-mentioned intelligent bridge inspection vehicle bridge pier identification device based on laser radar can be fully or partially implemented by software, hardware and their combination. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, or can be stored in the memory of the computer device in the form of software, so that the processor can call and execute the corresponding operations of the above modules.

[0135] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Fig. 9As shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit and an input device. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface, the display unit and the input device are connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used to exchange information between the processor and the external device. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be realized through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a bridge pier identification method for an intelligent bridge inspection vehicle based on laser radar is realized. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad set on the computer device shell, or an external keyboard, touchpad or mouse.

[0136] Those skilled in the art will understand that Fig. 9 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0137] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0138] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0139] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0140] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant laws, regulations and standards of relevant countries and regions.

[0141] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0142] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0143] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A bridge pier recognition method for an intelligent bridge inspection vehicle based on laser radar, characterized in that: The method comprises: Acquire point cloud data of the target bridge; Dividing the point cloud data into a plurality of point cloud intervals based on a preset reference direction and interval; Traversing and counting the histogram features of the point cloud data in each of the point cloud intervals, and screening the point cloud intervals based on the histogram features; The pier information of the target bridge is determined according to the screened target point cloud intervals.

2. The method according to claim 1, characterized in that The traversing and counting the histogram features of the point cloud data in each of the point cloud intervals, and screening the point cloud intervals based on the histogram features comprises: Obtaining height features and width features of the point cloud interval; The point cloud interval is filtered based on a preset height threshold and a width threshold to filter out interference targets in the point cloud interval.

3. The method according to claim 2, characterized in that The traversing and counting the histogram features of the point cloud data in each of the point cloud intervals, and screening the point cloud intervals based on the histogram features comprises: Determine an area characteristic value of the point cloud interval based on the height feature and the width feature; The point cloud interval is filtered based on a preset area threshold to filter out interference targets in the point cloud interval.

4. The method according to claim 1, characterized in that: After dividing the point cloud data into a plurality of point cloud intervals based on the preset reference direction and interval, the method further includes: Performing cluster analysis on the point cloud data in the point cloud interval based on a preset cluster analysis algorithm; The point cloud interval is segmented based on the result of the cluster analysis to obtain candidate targets that meet preset conditions, thereby forming a set of candidate targets.

5. The method according to claim 4, characterized in that Determining the pier information of the target bridge according to the plurality of target point cloud intervals obtained by screening includes: Determine the geometric area and the scanning area of ​​the candidate target according to the point cloud data, and use the product of the geometric area and the scanning area as a positioning feature value; The location information of the target bridge pier is determined according to the point cloud data corresponding to the maximum value of the positioning feature value.

6. The method according to claim 1, characterized in that The method further comprises: Acquiring auxiliary positioning feature information on the target bridge; The pier information of the target bridge is determined based on the point cloud data and the auxiliary positioning feature information.

7. A bridge pier recognition device for an intelligent bridge inspection vehicle based on laser radar, characterized in that: The device comprises: A point cloud data module, used for acquiring point cloud data of the target bridge; An interval division module, used to divide the point cloud data into a plurality of point cloud intervals based on a preset reference direction and interval; A histogram feature module, used for traversing and counting the histogram features of the point cloud data in each point cloud interval, and screening the point cloud interval based on the histogram features; The pier information module is used to determine the pier information of the target bridge according to the screened target point cloud intervals.

8. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product, comprising a computer program, characterized in that When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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