Intelligent bridge inspection vehicle laser radar interference point filtering method and device

Abstract

The invention relates to the technical field of signal processing, in particular to an intelligent bridge inspection vehicle laser radar interference point filtering method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring point cloud data of a target bridge, wherein the point cloud data comprises environmental information in a target equipment detection area; identifying and removing interference point data in the point cloud data to obtain application point cloud data; and performing advancing control and bridge detection of the target equipment based on the application point cloud data. By adopting the method, the interference in the data detected by the radar can be reduced, the data accuracy can be improved, the defects caused by the existence of interference points can be overcome, the accuracy of the detection result of the sensing system can be improved, the unnecessary alarm or braking action can be reduced, and the availability of the system can be improved.

Description

Technical Field

[0001] The present application relates to the field of signal processing technology, and in particular to a method, device, computer equipment, storage medium and computer program product for filtering interference points of a laser radar of an intelligent bridge inspection vehicle. 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 technologies, traditional bridge inspection often requires inspectors to control intelligent bridge inspection vehicles and approach bridge structures for observation and measurement. During the real-time inspection process, bridge inspection vehicles need to quickly process a large amount of data in order to detect problems in a timely manner and respond.

[0004] However, if the data in the current bridge inspection method contains a large number of interference points, it will not only increase the complexity and time of data processing, but also increase the difficulty of identification and judgment, and may even lead to misjudgment, resulting in invalid bridge inspection results. Summary of the invention

[0005] Based on this, it is necessary to provide a method, device, computer equipment, computer-readable storage medium and computer program product for filtering interference points of an intelligent bridge inspection vehicle laser radar, which can reduce the interference in the data detected by the radar and improve the data accuracy, in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for filtering interference points of a laser radar of an intelligent bridge inspection vehicle. The method comprises:

[0007] Acquire point cloud data of a target bridge, wherein the point cloud data includes environmental information within a detection area of ​​the target device;

[0008] Identify and remove interference point data in the point cloud data to obtain application point cloud data;

[0009] The target device is controlled to move and detects bridges based on the application point cloud data.

[0010] In one embodiment, the identifying and removing interference point data in the point cloud data to obtain application point cloud data includes:

[0011] Identify and remove static interference point data existing in the point cloud data to obtain the application point cloud data, wherein the static interference point data includes static interference point clouds associated with the target device and specific environmental facilities;

[0012] The dynamic interference point data existing in the point cloud data is identified and eliminated to obtain the application point cloud data, wherein the dynamic interference point data includes a dynamic interference point cloud associated with a dynamic interference object in the detection area.

[0013] In one embodiment, the identifying and removing static interference point data in the point cloud data to obtain the application point cloud data includes:

[0014] Acquire structural information of the target device, wherein the structural information includes geometric properties of a main body and components of the target device;

[0015] constructing a geometric model corresponding to the target device based on the geometric attributes;

[0016] Based on the geometric model, the structure interference point cloud associated with the geometric model is removed.

[0017] In one embodiment, the controlling the movement of the target device and the bridge detection based on the application point cloud data comprises:

[0018] Based on the environmental information of the target bridge, the geometric properties of the geometric model are optimized to maintain the coverage of the detection area of ​​the target device and eliminate the interference of the environmental facilities;

[0019] The travel control and bridge detection of the target device are realized based on the optimized geometric model.

[0020] In one embodiment, the identifying and removing dynamic interference point data in the point cloud data to obtain the application point cloud data includes:

[0021] Traversing and calculating neighborhood values ​​of point clouds in the point cloud data based on a preset dynamic radius filtering algorithm;

[0022] If the neighborhood value is greater than a preset neighborhood threshold, the associated point cloud is determined to be a dynamic interference point.

[0023] In one embodiment, the identifying and removing dynamic interference point data in the point cloud data to obtain the application point cloud data includes:

[0024] Filtering the point cloud data based on a preset reflection intensity threshold;

[0025] If the reflection intensity of the target point cloud is lower than the reflection intensity threshold, and the neighborhood value is greater than the preset neighborhood threshold, the associated point cloud is determined to be a dynamic interference point.

[0026] In a second aspect, the present application also provides a device for filtering interference points of a laser radar of an intelligent bridge inspection vehicle. The device comprises:

[0027] A point cloud data module, used to obtain point cloud data of a target bridge, wherein the point cloud data includes environmental information within a detection area of ​​the target device;

[0028] An interference point module is used to identify and remove interference point data existing in the point cloud data to obtain application point cloud data;

[0029] An application module is used to perform travel control and bridge inspection detection of the target device based on the application point cloud data.

[0030] In one embodiment, the interference point module includes:

[0031] A static interference filtering module, used to identify and remove static interference point data existing in the point cloud data to obtain the application point cloud data, wherein the static interference point data includes static interference point clouds associated with the target device and specific environmental facilities;

[0032] The dynamic interference filtering module is used to identify and remove dynamic interference point data existing in the point cloud data to obtain the application point cloud data, wherein the dynamic interference point data includes dynamic interference point clouds associated with dynamic interference objects in the detection area.

[0033] In one embodiment, the static interference filtering module includes:

[0034] A structure information module, used to obtain the structure information of the target device, wherein the structure information includes geometric properties of the main body and components of the target device;

[0035] A geometric model module, used for constructing a geometric model corresponding to the target device based on the geometric attributes;

[0036] The structure interference point module is used to remove the structure interference point cloud associated with the geometric model based on the geometric model.

[0037] In one embodiment, the application module includes:

[0038] A property optimization module, configured to optimize the geometric properties of the geometric model based on the environmental information of the target bridge, so as to maintain the coverage of the detection area of ​​the target device and eliminate the interference of the environmental facilities;

[0039] The optimization application module is used to realize the travel control and bridge detection of the target device based on the optimized geometric model.

[0040] In one embodiment, the dynamic interference filtering module includes:

[0041] A neighborhood value module, used for traversing and calculating neighborhood values ​​of point clouds in the point cloud data based on a preset dynamic radius filtering algorithm;

[0042] The dynamic interference point module is used to determine the associated point cloud as a dynamic interference point if the neighborhood value is greater than a preset neighborhood threshold.

[0043] In one embodiment, the dynamic interference filtering module includes:

[0044] A reflection filtering module, used for filtering the point cloud data based on a preset reflection intensity threshold;

[0045] The filtering and verification module is used to determine the associated point cloud as a dynamic interference point if the reflection intensity of the target point cloud is lower than the reflection intensity threshold and the neighborhood value is greater than a preset neighborhood threshold.

[0046] 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 executes the computer program to implement the steps in the intelligent bridge inspection vehicle laser radar interference point filtering method as described in any embodiment of the first aspect.

[0047] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for filtering interference points of a laser radar for an intelligent bridge inspection vehicle as described in any one of the embodiments in the first aspect.

[0048] 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 intelligent bridge inspection vehicle laser radar interference point filtering method as described in any one of the embodiments in the first aspect are implemented.

[0049] The above-mentioned intelligent bridge inspection vehicle laser radar interference point filtering method, device, computer equipment, storage medium and computer program product can achieve the beneficial effects of the technical problems in the corresponding background technology by deducing the technical features in the claims:

[0050] The present application provides a method for filtering interference points of a laser radar of an intelligent bridge inspection vehicle, the steps mainly include obtaining point cloud data of the target bridge, the point cloud data includes environmental information within the detection area of ​​the target device; identifying and eliminating interference point data existing in the point cloud data to obtain application point cloud data; and performing travel control and bridge inspection detection of the target device based on the application point cloud data. In implementation, by filtering out interference points in the point cloud data, it is helpful to obtain more accurate point cloud data, while reducing the overall data volume of the point cloud data and improving the data validity of the point cloud data. On the one hand, it can improve the accuracy of the bridge inspection detection results, and on the other hand, it can assist in achieving more accurate and efficient precise control of the intelligent bridge inspection vehicle. In this way, it helps to overcome the defects caused by the existence of interference points and improve the accuracy of the detection results of the perception system, thereby reducing unnecessary alarms or braking actions and improving the availability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a diagram of the application environment of a method for filtering interference points of a laser radar of an intelligent bridge inspection vehicle in one embodiment;

[0052] Figure 2 It is a schematic diagram of a first process of a method for filtering interference points of a laser radar of an intelligent bridge inspection vehicle in one embodiment;

[0053] Figure 3 It is a second flow chart of a method for filtering interference points of a laser radar of an intelligent bridge inspection vehicle in another embodiment;

[0054] Figure 4 A third flow chart of a method for filtering interference points of a laser radar of an intelligent bridge inspection vehicle in another embodiment;

[0055] Figure 5 It is a schematic diagram of a fourth process of a method for filtering interference points of a laser radar of an intelligent bridge inspection vehicle in another embodiment;

[0056] Figure 6 It is a fifth flow chart of a method for filtering interference points of a laser radar of an intelligent bridge inspection vehicle in another embodiment;

[0057] Figure 7 It is a sixth flow chart of a method for filtering interference points of a laser radar of an intelligent bridge inspection vehicle in another embodiment;

[0058] Figure 8 It is a structural block diagram of an interference point filtering device of a laser radar of an intelligent bridge inspection vehicle in one embodiment;

[0059] Fig. 9 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0060] 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.

[0061] 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.

[0062] In the related technologies, traditional bridge inspection often requires inspectors to control intelligent bridge inspection vehicles and approach bridge structures for observation and measurement. During the real-time inspection process, bridge inspection vehicles need to quickly process a large amount of data in order to detect problems in a timely manner and respond.

[0063] However, if the data in the current bridge inspection method contains a large number of interference points, it will not only increase the complexity and time of data processing, but also increase the difficulty of identification and judgment, and may even lead to misjudgment, resulting in invalid bridge inspection results.

[0064] Specifically, bridge inspection vehicles need to quickly process a large amount of data during real-time inspections in order to detect problems and respond in a timely manner. If the data contains a large number of interference points, it will not only increase the complexity and time of data processing, but also increase the difficulty of identification and judgment, and may even lead to misjudgment.

[0065] Radar interference points can be divided into static and dynamic interference points. Static interference points include the point cloud of the bridge inspection vehicle body scanned by the lidar, the point cloud of the bottom of the bridge and the installed sensors or lamps; dynamic interference points include but are not limited to noise points generated by rain, snow and dust in bad weather.

[0066] Based on the above problems, the embodiments of the present application provide a method, apparatus, computer equipment, storage medium and computer program product for filtering interference points of an intelligent bridge inspection vehicle laser radar.

[0067] The embodiment of the present application provides a method for filtering interference points of a laser radar for intelligent bridge inspection vehicles, 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.

[0068] In one embodiment, Figure 2 As shown in the figure, a method for filtering interference points of intelligent bridge inspection vehicle laser radar is provided. Figure 1The terminal in is used as an example to illustrate, including the following steps:

[0069] Step 202: Acquire point cloud data of the target bridge, wherein the point cloud data includes environmental information within a detection area of ​​the target device.

[0070] Step 204: Identify and remove interference point data in the point cloud data to obtain application point cloud data.

[0071] Step 206: Perform travel control and bridge detection of the target device based on the application point cloud data.

[0072] In the above-mentioned intelligent bridge inspection vehicle laser radar interference point filtering method, reasonable deduction is carried out in combination with the technical features in the embodiment, which can achieve the beneficial effect of solving the technical problems raised in the background technology:

[0073] The present application provides a method for filtering interference points of a laser radar of an intelligent bridge inspection vehicle, the steps mainly include obtaining point cloud data of the target bridge, the point cloud data includes environmental information within the detection area of ​​the target device; identifying and eliminating interference point data existing in the point cloud data to obtain application point cloud data; and performing travel control and bridge inspection detection of the target device based on the application point cloud data. In implementation, by filtering out interference points in the point cloud data, it is helpful to obtain more accurate point cloud data, while reducing the overall data volume of the point cloud data and improving the data validity of the point cloud data. On the one hand, it can improve the accuracy of the bridge inspection detection results, and on the other hand, it can assist in achieving more accurate and efficient precise control of the intelligent bridge inspection vehicle. In this way, it helps to overcome the defects caused by the existence of interference points and improve the accuracy of the detection results of the perception system, thereby reducing unnecessary alarms or braking actions and improving the availability of the system.

[0074] In one embodiment, if Figure 3 As shown, step 204 includes:

[0075] Step 302: Identify and remove static interference point data in the point cloud data to obtain the application point cloud data, where the static interference point data includes static interference point clouds associated with the target device and specific environmental facilities.

[0076] Step 304: Identify and remove dynamic interference point data in the point cloud data to obtain the application point cloud data, where the dynamic interference point data includes dynamic interference point clouds associated with dynamic interference objects in the detection area.

[0077] In this embodiment, in the process of identifying and filtering the interference point data, static interference points and dynamic interference points are processed separately, which helps to filter out interference points in different dimensions and scenarios and improve the availability of the system.

[0078] In one embodiment, if Figure 4 As shown, step 302 includes:

[0079] Step 402: Acquire structural information of the target device, where the structural information includes geometric properties of the main body and components of the target device.

[0080] Step 404: construct a geometric model corresponding to the target device based on the geometric attributes.

[0081] Step 406: Using the geometric model as a reference, remove the structural interference point cloud associated with the geometric model.

[0082] For example, the point cloud of the bridge inspection vehicle body scanned by the laser radar and the point cloud of the bridge bottom and installed sensors or lamps will cause the perception system to detect incorrectly, so they need to be removed. The present invention replaces the curve with a straight line to split the bridge inspection vehicle structure into a rectangular structure to achieve virtual assembly of the bridge inspection vehicle outline. Based on the virtual bridge inspection vehicle outline, the point cloud of the bridge inspection vehicle itself and other structural interference points at the bottom of the bridge are removed.

[0083] In this embodiment, in the processing of static interference points, the intelligent bridge inspection vehicle and other facilities and structures of the bridge are modeled, which helps to simplify the complex curve structure and improve the processing efficiency of the static interference points.

[0084] In one embodiment, if Figure 5 As shown, step 206 includes:

[0085] Step 502: Based on the environmental information of the target bridge, the geometric properties of the geometric model are optimized to maintain the coverage of the detection area of ​​the target equipment and eliminate the interference of the environmental facilities.

[0086] Step 504: Implementing travel control and bridge detection of the target device based on the optimized geometric model.

[0087] For example, the specific process is as follows:

[0088] 1. According to the plan of the bridge inspection vehicle, divide it into two parts: the main body and the components. Each part is written into the configuration file in a cube structure according to the center point, length, width and height.

[0089] 2. According to the actual on-site environment of the bridge inspection vehicle, optimize the geometric properties of the main body and components in step 1 to ensure the coverage of the sensing area and not be interfered by the structure of the facilities at the bottom of the bridge.

[0090] 3. Read the configuration parameters and execute the virtual stitching algorithm. First, load the main body and component structure into the program, divide the geometric contour into upper and lower edge points according to the front view, sort the upper and lower edge points along the y-axis, sort the upper contour points clockwise, and sort the lower contour points counterclockwise, and connect them end to end to form an edge point set.

[0091] 4. According to step 4, a virtual 2D outline of the bridge inspection vehicle is formed and solved into three-dimensional geometric dimensions according to the width.

[0092] 5. According to the actual point cloud data, solve the external parameters of the virtual contour and the actual point cloud coordinate system so that the virtual contour contains the actual bridge inspection vehicle body point cloud and removes the body point cloud.

[0093] 6. Traverse each point cloud to determine whether it is inside the virtual contour polygon. In the direction of travel of the bridge inspection vehicle, only the point cloud within the virtual contour polygon will be used to sense obstacles and filter the point cloud of the bottom of the bridge and other equipment.

[0094] In this embodiment, in the processing of static interference points, the geometric model is optimized, so that the intelligent bridge inspection vehicle can not only perceive obstacles, but also filter the point cloud of the bottom of the bridge and other equipment.

[0095] In one embodiment, if Figure 6 As shown, step 304 includes:

[0096] Step 602: traverse and calculate neighborhood values ​​of point clouds in the point cloud data based on a preset dynamic radius filtering algorithm.

[0097] Step 604: If the neighborhood value is greater than a preset neighborhood threshold, the associated point cloud is determined to be a dynamic interference point.

[0098] For example, during the automatic inspection of bridge inspection vehicles, rain, snow, dust and other situations may occur suddenly. In these bad weather conditions, laser radar usually generates noise points, which may cause misjudgment of the obstacle avoidance system. The present invention proposes a fast dynamic radius filtering algorithm, which is faster than the traditional dynamic radius filtering. Filtering out these interference points can improve the point cloud quality and the reliability of the perception system.

[0099] The specific process is as follows:

[0100] 1. Obtain lidar point cloud data, enable the dynamic radius filtering algorithm, and set the search radius w (pixels), radius factor k, and neighborhood threshold T.

[0101] 2. The point cloud is converted from the 3D coordinate system to the rangeImg image coordinate system according to the vertical and horizontal angular resolution. The conversion is shown below:

[0102]

[0103] In the above formula, x and y represent the image column and row index corresponding to the current point cloud; atan2 is the inverse tangent function, and the output value is radians; hor represents the horizontal scanning angle of the point; ver represents the vertical scanning angle of the point; fov_hor max and fov_hor min Respectively represent the maximum and minimum angles of the laser radar horizontal field of view; fov_ver max and fov_ver min They represent the maximum and minimum angles of the vertical field of view of the laser radar respectively.

[0104] Each pixel value consists of two parts, one is its corresponding 3D coordinate index value, and the other is the number of point clouds whose neighborhood point distance is within the radius threshold R. The calculation formula of the radius threshold R is as follows.

[0105]

[0106] In the formula, They represent the radian values ​​of the horizontal and vertical angular resolutions of the lidar respectively, and dis represents the distance value of the current point.

[0107] 3. Except for the first column in rangeImg, the remaining pixel points only need to calculate the distance between the current point and some pixels in the lower right corner based on w and the formula below, and calculate the number of its neighborhood points based on the radius threshold. Compared with traditional dynamic radius filtering, the present invention can basically ignore the time-consuming repeated calculation of distance values.

[0108]

[0109] Among them, p y,x Indicates the current point; q y',x' represents the points in the neighborhood of w; d y',x' Represents the Euclidean distance between the neighboring point and the current point; n_pts represents the number of neighboring points.

[0110] 4. According to step 3, after calculating the neighborhood value of each point, compare it with the neighborhood point threshold T. If it is greater than the threshold, it is considered a valid point, otherwise it is an interference point.

[0111] In this embodiment, in the process of dynamic interference point filtering, processing is performed based on a dynamic radius filtering algorithm, which helps to reduce the computational burden caused by repeated calculation of distance values, thereby improving data processing efficiency and enhancing the response performance of the system.

[0112] In one embodiment, if Figure 7 As shown, step 304 includes:

[0113] Step 702: filtering the point cloud data based on a preset reflection intensity threshold;

[0114] Step 704: If the reflection intensity of the target point cloud is lower than the reflection intensity threshold, and the neighborhood value is greater than a preset neighborhood threshold, the associated point cloud is determined to be a dynamic interference point.

[0115] For example, in order to reduce the probability of valid points being wrongly filtered out, the reflection intensity threshold AMP is introduced, which can be added before the fast radius filtering. Since the interference points usually have low reflection intensity, the suspected interference points can be filtered out by reflection intensity first, and then further confirmed by fast radius filtering. Alternatively, after the fast radius filtering, the reflection intensity threshold can be used for secondary confirmation.

[0116] In this embodiment, by setting reflection intensity filtering, a secondary verification is performed on the dynamic interference point, which helps to improve the accuracy of dynamic filtering.

[0117] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0118] Based on the same inventive concept, the embodiment of the present application also provides a device for filtering interference points of a laser radar interference point of an intelligent bridge inspection vehicle for realizing the above-mentioned method for filtering interference points of a laser radar interference point of an intelligent bridge inspection vehicle. 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 device for filtering interference points of an intelligent bridge inspection vehicle provided below can be referred to the limitations of the method for filtering interference points of a laser radar interference point of an intelligent bridge inspection vehicle above, and will not be repeated here.

[0119] In one embodiment, Figure 8 As shown, a device for filtering interference points of a laser radar for an intelligent bridge inspection vehicle is provided, comprising: a point cloud data module, an interference point module and an application module, wherein:

[0120] A point cloud data module, used to obtain point cloud data of a target bridge, wherein the point cloud data includes environmental information within a detection area of ​​the target device;

[0121] An interference point module is used to identify and remove interference point data existing in the point cloud data to obtain application point cloud data;

[0122] An application module is used to perform travel control and bridge inspection detection of the target device based on the application point cloud data.

[0123] In one embodiment, the interference point module includes:

[0124] A static interference filtering module, used to identify and remove static interference point data existing in the point cloud data to obtain the application point cloud data, wherein the static interference point data includes static interference point clouds associated with the target device and specific environmental facilities;

[0125] The dynamic interference filtering module is used to identify and remove dynamic interference point data existing in the point cloud data to obtain the application point cloud data, wherein the dynamic interference point data includes dynamic interference point clouds associated with dynamic interference objects in the detection area.

[0126] In one embodiment, the static interference filtering module includes:

[0127] A structure information module, used to obtain the structure information of the target device, wherein the structure information includes geometric properties of the main body and components of the target device;

[0128] A geometric model module, used for constructing a geometric model corresponding to the target device based on the geometric attributes;

[0129] The structure interference point module is used to remove the structure interference point cloud associated with the geometric model based on the geometric model.

[0130] In one embodiment, the application module includes:

[0131] A property optimization module, configured to optimize the geometric properties of the geometric model based on the environmental information of the target bridge, so as to maintain the coverage of the detection area of ​​the target device and eliminate the interference of the environmental facilities;

[0132] The optimization application module is used to realize the travel control and bridge detection of the target device based on the optimized geometric model.

[0133] In one embodiment, the dynamic interference filtering module includes:

[0134] A neighborhood value module, used for traversing and calculating neighborhood values ​​of point clouds in the point cloud data based on a preset dynamic radius filtering algorithm;

[0135] The dynamic interference point module is used to determine the associated point cloud as a dynamic interference point if the neighborhood value is greater than a preset neighborhood threshold.

[0136] In one embodiment, the dynamic interference filtering module includes:

[0137] A reflection filtering module, used for filtering the point cloud data based on a preset reflection intensity threshold;

[0138] The filtering and verification module is used to determine the associated point cloud as a dynamic interference point if the reflection intensity of the target point cloud is lower than the reflection intensity threshold and the neighborhood value is greater than a preset neighborhood threshold.

[0139] Each module in the above-mentioned intelligent bridge inspection vehicle laser radar interference point filtering device 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 each of the above modules.

[0140] 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. 9 As 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 method for filtering interference points of an intelligent bridge inspection vehicle 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.

[0141] Those skilled in the art will understand that Fig. 9The 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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 method for filtering interference points of a laser radar for intelligent bridge inspection vehicles, characterized in that: The method comprises: Acquire point cloud data of a target bridge, wherein the point cloud data includes environmental information within a detection area of ​​the target device; Identify and remove interference point data in the point cloud data to obtain application point cloud data; The target device is controlled to move and detects bridges based on the application point cloud data.

2. The method according to claim 1, characterized in that The identifying and removing the interference point data in the point cloud data to obtain the application point cloud data includes: Identify and remove static interference point data existing in the point cloud data to obtain the application point cloud data, wherein the static interference point data includes static interference point clouds associated with the target device and specific environmental facilities; The dynamic interference point data existing in the point cloud data is identified and eliminated to obtain the application point cloud data, wherein the dynamic interference point data includes a dynamic interference point cloud associated with a dynamic interference object in the detection area.

3. The method according to claim 2, characterized in that The identifying and eliminating static interference point data in the point cloud data to obtain the application point cloud data includes: Acquire structural information of the target device, wherein the structural information includes geometric properties of a main body and components of the target device; constructing a geometric model corresponding to the target device based on the geometric attributes; Based on the geometric model, the structure interference point cloud associated with the geometric model is removed.

4. The method according to claim 3, characterized in that The performing of the travel control and bridge inspection of the target device based on the application point cloud data comprises: Based on the environmental information of the target bridge, the geometric properties of the geometric model are optimized to maintain the coverage of the detection area of ​​the target device and eliminate the interference of the environmental facilities; The travel control and bridge detection of the target device are realized based on the optimized geometric model.

5. The method according to claim 2, characterized in that: The identifying and eliminating the dynamic interference point data in the point cloud data to obtain the application point cloud data includes: Traversing and calculating neighborhood values ​​of point clouds in the point cloud data based on a preset dynamic radius filtering algorithm; If the neighborhood value is greater than a preset neighborhood threshold, the associated point cloud is determined to be a dynamic interference point.

6. The method according to claim 5, characterized in that The identifying and eliminating the dynamic interference point data in the point cloud data to obtain the application point cloud data includes: Filtering the point cloud data based on a preset reflection intensity threshold; If the reflection intensity of the target point cloud is lower than the reflection intensity threshold, and the neighborhood value is greater than the preset neighborhood threshold, the associated point cloud is determined to be a dynamic interference point.

7. An intelligent bridge inspection vehicle laser radar interference point filtering device, characterized in that: The device comprises: A point cloud data module, used to obtain point cloud data of a target bridge, wherein the point cloud data includes environmental information within a detection area of ​​the target device; An interference point module is used to identify and remove interference point data existing in the point cloud data to obtain application point cloud data; An application module is used to perform travel control and bridge inspection detection of the target device based on the application point cloud data.

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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