Point cloud data acquisition methods, equipment, storage media, and software products

Abstract

This application provides a method, device, storage medium, and program product for acquiring point cloud data. The method includes acquiring engineering data corresponding to multiple engineering tasks for a target acquisition area. The engineering data is obtained by collecting data from the target acquisition area based on the corresponding engineering tasks. The laser point cloud data from the engineering data corresponding to the multiple engineering tasks is fused to obtain target laser point cloud data for the target acquisition area. The method proposed in this embodiment divides the acquisition of point cloud map data for the target acquisition area into multiple projects, ensuring that the multiple projects completely cover the target acquisition area. After completing the acquisition of multiple projects, the point cloud data acquired from the multiple projects is fused to obtain complete point cloud data of the features in the target acquisition area. Compared to acquiring the target acquisition area through a single project, this significantly reduces redundant data, improves acquisition efficiency, and lowers acquisition costs.

Description

Technical Field

[0001] This application relates to the field of high-precision map technology, and in particular to a method, device, storage medium and program product for collecting point cloud data. Background Technology

[0002] High-precision point cloud map data for the target acquisition area is usually collected in a single project, which is beneficial for the complete collection of ground features within the target acquisition area.

[0003] However, in order to complete the collection of point cloud map data of ground features in the target area in the above-mentioned method, there will be a lot of redundant collection trips due to traffic rules. In addition, urban road congestion will result in extremely low collection efficiency and high collection cost. Summary of the Invention

[0004] This application provides a method, device, storage medium, and program product for acquiring point cloud data, so as to improve acquisition efficiency and reduce acquisition costs.

[0005] In a first aspect, embodiments of this application provide a method for acquiring point cloud data, including:

[0006] Obtain engineering data corresponding to multiple engineering tasks for the target acquisition area; the engineering data is obtained by collecting data from the target acquisition area based on the corresponding engineering tasks.

[0007] The laser point clouds in the engineering data corresponding to the multiple engineering tasks are fused to obtain the target laser point cloud data of the target acquisition area.

[0008] In one possible design, the step of fusing the laser point clouds from the engineering data corresponding to multiple engineering tasks to obtain target laser point cloud data of the target acquisition area includes:

[0009] The laser point cloud in the engineering data corresponding to the multiple engineering tasks is partitioned to obtain multiple laser partitions that do not overlap with each other.

[0010] Perform point cloud consistency alignment processing on multiple laser partitions to obtain aligned laser partitions;

[0011] By performing edge-joining processing on multiple aligned laser partitions, target laser point cloud data of the target acquisition area is obtained.

[0012] In one possible design, the step of partitioning the laser point cloud in the engineering data corresponding to the multiple engineering tasks to obtain multiple non-overlapping laser partitions includes:

[0013] For each engineering task, the laser point cloud in the engineering data is divided into multiple laser point cloud segments corresponding to different time periods.

[0014] Multiple laser point cloud segments of the multiple engineering data are partitioned to obtain multiple laser partitions that do not overlap with each other.

[0015] In one possible design, dividing the laser point cloud in the engineering data into multiple laser point cloud segments corresponding to different time periods includes:

[0016] The POS trajectory in the engineering data is divided into multiple trajectory segments according to length;

[0017] Based on the time correspondence between the POS trajectory and the laser point cloud data, the laser point cloud in the engineering data is divided to obtain multiple laser point cloud segments corresponding to the trajectory segments respectively.

[0018] In one possible design, partitioning multiple laser point cloud segments of the multiple engineering data to obtain multiple non-overlapping laser partitions includes:

[0019] For each of the multiple laser point cloud segments in the multiple engineering data, determine the minimum outer bound of the laser point cloud segment;

[0020] Laser point cloud segments with overlapping minimum outer regions among multiple laser point cloud segments of multiple engineering data are divided into the same partition to obtain multiple laser partitions that do not overlap with each other.

[0021] In one possible design, after dividing the laser point cloud segments with overlapping minimum bounding ranges among the multiple laser point cloud segments of the multiple engineering data into the same partition to obtain multiple non-overlapping laser partitions, the method further includes:

[0022] The isolated laser point cloud segment whose minimum outer bound does not overlap with other minimum outer bounds among the multiple laser point cloud segments of the multiple engineering data is added to the laser partition with the shortest distance to the isolated laser point cloud segment.

[0023] In one possible design, the edge-joining process for the multiple aligned laser partitions includes:

[0024] The multiple aligned laser partitions are sequentially joined according to their adjacent relationships;

[0025] For the currently processed partition among the multiple aligned laser partitions, the deviation between the currently processed partition and the previously processed partition, as well as the edge length of the edge region of the currently processed partition, are determined. The coordinates of the laser points in the edge region are updated according to the deviation and the edge length to complete the edge processing between the currently processed partition and the previously processed partition.

[0026] Secondly, embodiments of this application provide a point cloud data acquisition device, comprising:

[0027] The acquisition module is used to acquire engineering data corresponding to multiple engineering tasks in the target acquisition area; the engineering data is obtained by acquiring data from the target acquisition area based on the corresponding engineering tasks.

[0028] The fusion module is used to fuse the laser point clouds in the engineering data corresponding to multiple engineering tasks to obtain the target laser point cloud data of the target acquisition area.

[0029] Thirdly, embodiments of this application provide a point cloud data acquisition device, including: at least one processor and a memory;

[0030] The memory stores computer-executed instructions;

[0031] The at least one processor executes computer execution instructions stored in the memory, causing the at least one processor to perform the method described in the first aspect above and various possible designs of the first aspect.

[0032] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer-executable instructions, which, when executed by a processor, implement the method described in the first aspect and various possible designs of the first aspect.

[0033] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements the method described in the first aspect and various possible designs of the first aspect.

[0034] This embodiment provides a point cloud data acquisition method, device, storage medium, and program product. The method includes acquiring engineering data corresponding to multiple engineering tasks for a target acquisition area. The engineering data is obtained by collecting data from the target acquisition area based on the corresponding engineering tasks. The laser point cloud data from the engineering data corresponding to the multiple engineering tasks is fused to obtain the target laser point cloud data of the target acquisition area. This embodiment divides the acquisition of point cloud map data of the target acquisition area into multiple projects, ensuring that the multiple projects completely cover the target acquisition area. After completing the acquisition of multiple projects, the point cloud data acquired from the multiple projects is fused to obtain the complete point cloud data of the ground features in the target acquisition area. Compared to acquiring the target acquisition area through a single project, this significantly reduces redundant data, improves acquisition efficiency, and lowers acquisition costs. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0036] Figure 1 This is a schematic diagram illustrating an application scenario of the point cloud data acquisition method provided in the embodiments of this application;

[0037] Figure 2 A flowchart illustrating the point cloud data acquisition method provided in this application embodiment;

[0038] Figure 3 This is a schematic diagram of the structure of the point cloud data acquisition device provided in the embodiments of this application;

[0039] Figure 4 A schematic diagram of the hardware structure of the point cloud data acquisition device provided in the embodiments of this application. Detailed Implementation

[0040] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0041] High-precision point cloud map data for the target acquisition area is typically collected in a single project. This ensures complete data collection of ground features within the target area. One project corresponds to one continuous data collection run by a data acquisition vehicle. For example, if the target acquisition area is road segment AB (starting point A, ending point B), road segment AB is collected in a single project, meaning one continuous data collection run by a single data acquisition vehicle. If road segment AB includes side roads, intersections, and two-way lanes, the data acquisition vehicle typically needs to travel back and forth on the two-way lanes and side roads to ensure complete ground feature data collection. Furthermore, multiple turns and trips are required for complex road conditions such as intersections.

[0042] However, the above methods, in order to complete the collection of point cloud map data of the target area in a single project, are constrained by traffic regulations, resulting in many redundant collection trips. Coupled with urban road congestion, the collection efficiency is extremely low and the collection cost is high. Moreover, redundant collection trips lead to data redundancy, occupy storage resources, and interfere with subsequent data processing. In addition, if a single project is too large, it cannot be processed in parallel, resulting in low data processing efficiency.

[0043] To address the aforementioned technical problems, the inventors of this application have discovered that the collection of point cloud map data for a target collection area can be divided into multiple projects, allowing these projects to completely cover the target collection area. After completing the collection of multiple projects, the point cloud data collected from these projects can be fused to obtain complete point cloud data of the features in the target collection area. Compared to collecting the target collection area in a single project, this significantly reduces redundant data, improves collection efficiency, and lowers collection costs. Based on this, embodiments of this application provide a method for collecting point cloud data.

[0044] Figure 1 This is a schematic diagram illustrating an application scenario for the point cloud data acquisition method provided in this application embodiment. For example... Figure 1 As shown, the target data collection area is a crossroads, where both roads intersect and are two-way lanes.

[0045] In the specific implementation process, the acquisition tasks of the target acquisition area can first be divided into eight projects, numbered 1 to 8. Then, a data acquisition vehicle collects data from the target acquisition area based on multiple project tasks, obtaining project data corresponding to each project task. A terminal device or server acquires the project data corresponding to each project task and performs fusion processing on the laser point cloud data from these project data to obtain the target laser point cloud data of the target acquisition area. The point cloud data acquisition method provided in this application divides the acquisition of point cloud map data of the target acquisition area into multiple projects, ensuring that multiple projects completely cover the target acquisition area. After completing the acquisition of multiple projects, the point cloud data acquired from these projects is fused to obtain the complete point cloud data of the ground features in the target acquisition area. Compared to acquiring the target acquisition area through a single project, this method significantly reduces redundant data, improves acquisition efficiency, and lowers acquisition costs.

[0046] It should be noted that, Figure 1 The schematic diagram shown is merely an example. The point cloud data acquisition method and scenario described in the embodiments of this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided in the embodiments of this application. As those skilled in the art will know, with the evolution of the system and the emergence of new business scenarios, the technical solutions provided in the embodiments of this application are also applicable to similar technical problems.

[0047] The technical solutions of this application will be described in detail below with specific embodiments. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0048] Figure 2 This is a flowchart illustrating the point cloud data acquisition method provided in an embodiment of this application. Figure 2 As shown, the method includes:

[0049] 201. Obtain the engineering data corresponding to multiple engineering tasks for the target acquisition area; the engineering data is obtained by collecting data from the target acquisition area based on the corresponding engineering tasks.

[0050] The execution subject in this embodiment can be a terminal device or a server.

[0051] In this embodiment, an engineering task, or project, refers to a continuous data collection operation performed by a data collection vehicle. The target data collection area can be a road segment, a location, etc. Multiple engineering tasks can be executed in parallel using different data collection vehicles, or they can be executed multiple times using the same data collection vehicle. The specific method can be determined according to actual needs, and this embodiment does not impose any limitations on this.

[0052] Specifically, multiple data acquisition vehicles, equipped with sensors such as GNSS, INS, and LiDAR, will collect data on multiple projects within the target acquisition area. The goal is to ensure complete overall coverage of the acquisition area; complete data acquisition of features within a single project is not required. This reduces redundant data acquisition trips and improves acquisition efficiency. For example, suppose the target acquisition area contains features such as... Figure 1 The intersection shown can be used to collect data from 8 data collection vehicles (8 data collection vehicles are just an example, but 2, 3, 4, etc. can also be used). Each data collection vehicle contains one route at the intersection, and finally the 8 data collection vehicles completely cover the intersection.

[0053] In some embodiments, obtaining engineering data corresponding to multiple engineering tasks corresponding to a target acquisition area may include: dividing the target acquisition area into engineering sections to obtain multiple engineering tasks; and obtaining engineering data corresponding to multiple engineering tasks obtained by the acquisition vehicle collecting data from the target acquisition area based on the multiple engineering tasks.

[0054] Specifically, the target acquisition area can be designed into multiple engineering tasks using terminal devices or servers, for example... Figure 1 There are 8 engineering tasks. The principle for dividing the data can be that multiple engineering tasks can completely cover the target acquisition area, meaning the engineering data obtained from the completion of multiple engineering tasks includes complete point cloud data of the target acquisition area, thus ensuring the integrity of the acquired data. Alternatively, the acquisition vehicle can avoid reciprocating during the acquisition process, which can minimize the occurrence of redundant data.

[0055] 202. Perform fusion processing on the laser point clouds in the engineering data corresponding to multiple engineering tasks to obtain the target laser point cloud data of the target acquisition area.

[0056] Specifically, the engineering data corresponding to multiple engineering tasks may have certain deviations. Therefore, multiple engineering data can be fused to eliminate deviations and finally obtain the target laser point cloud data of the target acquisition area.

[0057] In some embodiments, the laser point clouds in the engineering data corresponding to multiple engineering tasks are fused to obtain target laser point cloud data of the target acquisition area. This may include: in response to the user's touch operation, the terminal device or server performs deviation correction and deduplication operations on the laser point clouds in each engineering data, thereby realizing the fusion processing of the laser point clouds in each engineering data to obtain target laser point cloud data of the target acquisition area.

[0058] In some embodiments, fusing laser point clouds in engineering data corresponding to multiple engineering tasks to obtain target laser point cloud data of the target acquisition area may include: partitioning the laser point clouds in engineering data corresponding to multiple engineering tasks to obtain multiple non-overlapping laser partitions; performing point cloud consistency alignment processing on the multiple laser partitions to obtain aligned laser partitions; and performing edge-connection processing on the multiple aligned laser partitions to obtain target laser point cloud data of the target acquisition area.

[0059] Specifically, in the laser point cloud data fusion processing, the laser point clouds in multiple engineering data in the acquisition area are first partitioned; then, the consistency alignment of the laser point cloud data is performed within each partition; and finally, the edge processing between partitions is performed.

[0060] In some embodiments, partitioning the laser point cloud in the engineering data corresponding to multiple engineering tasks to obtain multiple non-overlapping laser partitions may include: dividing the laser point cloud in the engineering data corresponding to each engineering task into multiple laser point cloud segments corresponding to different time periods; partitioning the multiple laser point cloud segments of the multiple engineering data to obtain multiple non-overlapping laser partitions.

[0061] Specifically, during the data partitioning process, the laser point clouds in each engineering data can be divided into multiple time periods according to preset rules, resulting in laser point cloud segments corresponding to each time period. The duration of different time periods can be the same or different. For example, road segments where the data acquisition vehicle travels at a constant speed can be divided according to a first duration, while road segments where the data acquisition vehicle is paused can be divided according to a second duration. The second duration is longer than the first duration.

[0062] In some embodiments, dividing the laser point cloud in the engineering data into multiple laser point cloud segments corresponding to different time periods may include: dividing the POS trajectory in the engineering data into multiple trajectory segments according to length; and dividing the laser point cloud in the engineering data based on the time correspondence between the POS trajectory and the laser point cloud data to obtain the laser point cloud segments corresponding to the multiple trajectory segments. The Position and Orientation System (POS) is an airborne reference sensor in aerial photogrammetry equipment, consisting of a satellite navigation system and a POS system computer, capable of acquiring real-time information such as the carrier's speed, attitude, and position. The POS trajectory is trajectory data collected by the Position and Orientation System (POS).

[0063] In some embodiments, partitioning multiple laser point cloud segments of multiple engineering data to obtain multiple laser partitions that do not overlap with each other may include: determining the minimum outer bound of each laser point cloud segment in the multiple laser point cloud segments of multiple engineering data; dividing laser point cloud segments in the multiple laser point cloud segments of multiple engineering data whose minimum outer bounds overlap into the same partition to obtain multiple laser partitions that do not overlap with each other.

[0064] In some embodiments, after dividing the laser point cloud segments with overlapping minimum bounding ranges among the multiple laser point cloud segments of multiple engineering data into the same partition to obtain multiple laser partitions that do not overlap with each other, it may further include: adding isolated laser point cloud segments whose minimum bounding ranges do not overlap with other minimum bounding ranges among the multiple laser point cloud segments of multiple engineering data into the laser partition with the shortest distance to the isolated laser point cloud segments.

[0065] In some embodiments, dividing the POS trajectory in the engineering data into multiple trajectory segments according to length may include: dividing the POS trajectory in the engineering data into multiple trajectory segments of equal length based on a preset length.

[0066] Specifically, the data is processed in partitions. A single project's data includes POS trajectory and laser point cloud data. The POS trajectory includes the coordinates of the INS origin and the acquisition time (x, y, z, t). The laser point cloud data includes the coordinates of the ground features and the acquisition time (x, y, z, t). i y i , z i , t i All of the above coordinates can be in the world coordinate system.

[0067] First, each engineering data point can be segmented into POS trajectory segments and laser point cloud data segments. During segmentation, the POS trajectory can be segmented based on distance (both the POS trajectory and the laser point cloud have a time 't' field). Then, the time intervals obtained from the POS trajectory segmentation are used to segment the laser point cloud data based on time, thus obtaining segmented laser point cloud segments corresponding to the segmented POS trajectory segments. During POS trajectory segmentation, the POS trajectory point mileage can be calculated, and the POS trajectory can be segmented at equal intervals, for example, 15 meters per segment. The distance can be set within the range of 10 meters to 40 meters, and the specific data can be set according to actual needs.

[0068] Secondly, partitioning and clustering can be performed. For multiple laser point cloud segments across all projects, overlapping laser point cloud segments are calculated for each segment. Specifically, the bounding box (minimum bounding area) of each laser point cloud segment is first calculated. Each laser point cloud segment contains several points, each with corresponding coordinate values ​​x, y, and z. The maximum and minimum values ​​in each x, y, and z direction are calculated to obtain the bounding box of the point cloud. If the bounding boxes of two laser point cloud segments intersect, it indicates that these two laser point cloud segments overlap. All laser point cloud segments with intersecting bounding boxes (i.e., overlapping) form a laser partition.

[0069] Furthermore, there may be isolated laser point cloud segments whose bounding boxes do not intersect with the bounding boxes of any other laser point cloud segments. For isolated laser point cloud segments, they can be merged into adjacent partitions (e.g., where the distance between bounding boxes is shortest). At this point, there are no longer overlapping laser point cloud segments between partitions, and they are independent of each other.

[0070] Furthermore, consistent alignment of laser point cloud data can be performed within each laser partition. Overlapping laser point cloud segments in each laser partition can be aligned using the Iterative Closest Point (ICP) point cloud matching algorithm.

[0071] In some embodiments, edge-joining processing of multiple aligned laser partitions may include: sequentially performing edge-joining processing on the multiple aligned laser partitions according to their adjacent relationships; for the currently processed partition among the multiple aligned laser partitions, determining the deviation between the currently processed partition and the previously processed partition, as well as the edge-joining length of the edge-joining area of ​​the currently processed partition, and updating the coordinates of the laser points in the edge-joining area according to the deviation and the edge-joining length, so as to complete the edge-joining processing between the currently processed partition and the previously processed partition.

[0072] Specifically, after the laser partitions undergo internal consistency alignment processing, deviations will appear in the connection point clouds between laser partitions (determining deviations can include importing the connection point clouds into a point cloud rendering tool and visually measuring whether deviations occur). Therefore, it is necessary to perform edge-joining processing on adjacent laser partitions to eliminate the deviations at the joints. For example, when performing edge-joining processing on partitions A and B, laser partition A can be fixed, while laser partition B's deviation can be adjusted. Laser partition B needs to be adjusted so that the starting point of the laser point connecting with laser partition A is taken as the starting point. The deviation of the connection point clouds between laser partitions A and B is Δ = (Δ... x ,Δ y ,Δ z The edge length (referring to the total length of the laser point cloud that needs to be adjusted from the starting point) is s. The coordinates of the connecting point cloud of partition B are corrected according to the following formula.

[0073]

[0074]

[0075]

[0076] Among them, (x i ,y i ,z i ) is t i Before the correction, (x) i ′,y i ′,z i ′) is t i The coordinates after time correction, l i It is t i The distance from the trajectory point corresponding to the moment to the trajectory point corresponding to the starting point of the partition connection cloud.

[0077] The point cloud data acquisition method provided in this embodiment divides the acquisition of point cloud map data of the target acquisition area into multiple projects, so that multiple projects completely cover the target acquisition area. After the acquisition of multiple projects is completed, the point cloud data acquired by multiple projects are fused to obtain the complete point cloud data of the ground features in the target acquisition area. Compared with the acquisition of the target acquisition area in a single project, it can greatly reduce redundant data, improve acquisition efficiency, and reduce acquisition costs.

[0078] Figure 3 This is a schematic diagram of the structure of a point cloud data acquisition device provided in an embodiment of this application. Figure 3 As shown, the point cloud data acquisition device 30 includes an acquisition module 301 and a fusion module 302.

[0079] The acquisition module 301 is used to acquire engineering data corresponding to multiple engineering tasks in the target acquisition area; the engineering data is obtained by acquiring data from the target acquisition area based on the corresponding engineering tasks.

[0080] The fusion module 302 is used to fuse the laser point clouds in the engineering data corresponding to multiple engineering tasks to obtain the target laser point cloud data of the target acquisition area.

[0081] The point cloud data acquisition device provided in this application divides the acquisition of point cloud map data of the target acquisition area into multiple projects, so that multiple projects completely cover the target acquisition area. After the acquisition of multiple projects is completed, the point cloud data acquired by multiple projects can be fused to obtain the complete point cloud data of the ground features in the target acquisition area. Compared with the acquisition of the target acquisition area in a single project, it can greatly reduce redundant data, improve acquisition efficiency, and reduce acquisition cost.

[0082] In some embodiments, the fusion module 302 is specifically used to partition the laser point cloud in the engineering data corresponding to multiple engineering tasks to obtain multiple laser partitions that do not overlap with each other; perform point cloud consistency alignment processing on the multiple laser partitions to obtain aligned laser partitions; and perform edge-joining processing on the multiple aligned laser partitions to obtain target laser point cloud data of the target acquisition area.

[0083] In some embodiments, the fusion module 302 is specifically used to divide the laser point cloud in the engineering data corresponding to each engineering task into multiple laser point cloud segments corresponding to different time periods; and to partition the multiple laser point cloud segments of multiple engineering data to obtain multiple laser partitions that do not overlap with each other.

[0084] In some embodiments, the fusion module 302 is specifically used to divide the POS trajectory in the engineering data into multiple trajectory segments according to the length; based on the time correspondence between the POS trajectory and the laser point cloud data, the laser point cloud in the engineering data is divided to obtain the laser point cloud segments corresponding to the multiple trajectory segments respectively.

[0085] In some embodiments, the fusion module 302 is specifically used to determine the minimum outer bound of each laser point cloud segment in multiple laser point cloud segments of multiple engineering data; and to divide the laser point cloud segments in multiple laser point cloud segments of multiple engineering data whose minimum outer bounds overlap into the same partition, thereby obtaining multiple laser partitions that do not overlap with each other.

[0086] In some embodiments, the fusion module 302 is specifically used to add isolated laser point cloud segments whose minimum outer bounding range does not overlap with other minimum outer bounding ranges from multiple laser point cloud segments of multiple engineering data into the laser partition with the shortest distance to the isolated laser point cloud segment.

[0087] In some embodiments, the fusion module 302 is specifically used to perform edge-joining processing on multiple aligned laser partitions in sequence according to their adjacent relationship; for the currently processed partition among the multiple aligned laser partitions, the deviation between the currently processed partition and the previously processed partition, as well as the edge-joining length of the edge-joining area of ​​the currently processed partition, are determined, and the coordinates of the laser points in the edge-joining area are updated according to the deviation and the edge-joining length, so as to complete the edge-joining processing between the currently processed partition and the previously processed partition.

[0088] The point cloud data acquisition device provided in this application embodiment can be used to execute the above method embodiment. Its implementation principle and technical effect are similar, and will not be described again here.

[0089] Figure 4This is a schematic diagram of the hardware structure of a point cloud data acquisition device provided in an embodiment of this application. The device can be a terminal device such as a computer, a message transceiver, a tablet device, a medical device, or a server.

[0090] Device 40 may include one or more of the following components: processing component 401, memory 402, power supply component 403, multimedia component 404, audio component 405, input / output (I / O) interface 406, sensor component 407, and communication component 408.

[0091] Processing component 401 typically controls the overall operation of device 40, such as operations associated with display, telephone calls, data communication, camera operation, and recording. Processing component 401 may include one or more processors 409 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 401 may include one or more modules to facilitate interaction between processing component 401 and other components. For example, processing component 401 may include a multimedia module to facilitate interaction between multimedia component 404 and processing component 401.

[0092] Memory 402 is configured to store various types of data to support the operation of device 40. Examples of such data include instructions for any application or method operating on device 40, contact data, phonebook data, messages, pictures, videos, etc. Memory 402 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0093] Power supply component 403 provides power to the various components of device 40. Power supply component 403 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to device 40.

[0094] Multimedia component 404 includes a screen that provides an output interface between device 40 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of touch or swipe actions but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 404 includes a front-facing camera and / or a rear-facing camera. When device 40 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0095] Audio component 405 is configured to output and / or input audio signals. For example, audio component 405 includes a microphone (MIC) configured to receive external audio signals when device 40 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 402 or transmitted via communication component 408. In some embodiments, audio component 405 also includes a speaker for outputting audio signals.

[0096] I / O interface 406 provides an interface between processing component 401 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0097] Sensor assembly 407 includes one or more sensors for providing state assessments of various aspects of device 40. For example, sensor assembly 407 may detect the on / off state of device 40, the relative positioning of components such as the display and keypad of device 40, changes in the position of device 40 or a component of device 40, the presence or absence of user contact with device 40, the orientation or acceleration / deceleration of device 40, and temperature changes of device 40. Sensor assembly 407 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 407 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 407 may also include an accelerometer, a gyroscope, a magnetometer, a pressure sensor, or a temperature sensor.

[0098] Communication component 408 is configured to facilitate wired or wireless communication between device 40 and other devices. Device 40 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 408 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 408 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0099] In an exemplary embodiment, device 40 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0100] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 402 including instructions, which can be executed by a processor 409 of device 40 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0101] The aforementioned computer-readable storage medium can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk. The readable storage medium can be any available medium accessible to a general-purpose or special-purpose computer.

[0102] An exemplary readable storage medium is coupled to a processor, enabling the processor to read information from and write information to the readable storage medium. Of course, the readable storage medium can also be a component of the processor. The processor and the readable storage medium can reside in an Application Specific Integrated Circuit (ASIC). Alternatively, the processor and the readable storage medium can exist as discrete components in the device.

[0103] Those skilled in the art will understand that all or part of the steps of the above-described method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When executed, the program performs the steps of the above-described method embodiments; and the aforementioned storage medium includes various media capable of storing program code, such as ROM, RAM, magnetic disks, or optical disks.

[0104] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the point cloud data acquisition method executed by the point cloud data acquisition device described above.

[0105] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A method for acquiring point cloud data, characterized in that, include: Acquire engineering data corresponding to multiple engineering tasks within the target acquisition area; The engineering data is obtained by collecting data from the target collection area based on the corresponding engineering task; The laser point clouds in the engineering data corresponding to the multiple engineering tasks are fused to obtain the target laser point cloud data of the target acquisition area. The step of acquiring engineering data corresponding to multiple engineering tasks for the target acquisition area includes: dividing the target acquisition area into engineering sections to obtain multiple engineering tasks; acquiring engineering data corresponding to multiple engineering tasks obtained by the acquisition vehicle based on the multiple engineering tasks to acquire data from the target acquisition area, wherein the engineering data includes complete point cloud data of the target acquisition area; The process of fusing laser point clouds from engineering data corresponding to multiple engineering tasks to obtain target laser point cloud data for the target acquisition area includes: The laser point cloud in the engineering data corresponding to the multiple engineering tasks is partitioned to obtain multiple laser partitions that do not overlap with each other. Perform point cloud consistency alignment processing on multiple laser partitions to obtain aligned laser partitions; By performing edge-joining processing on multiple aligned laser partitions, target laser point cloud data of the target acquisition area is obtained.

2. The method according to claim 1, characterized in that, The step of partitioning the laser point cloud in the engineering data corresponding to the multiple engineering tasks to obtain multiple non-overlapping laser partitions includes: For each engineering task, the laser point cloud in the engineering data is divided into multiple laser point cloud segments corresponding to different time periods. Multiple laser point cloud segments of the multiple engineering data are partitioned to obtain multiple laser partitions that do not overlap with each other.

3. The method according to claim 2, characterized in that, The step of dividing the laser point cloud in the engineering data into multiple time periods corresponding to laser point cloud segments includes: The POS trajectory in the engineering data is divided into multiple trajectory segments according to length; Based on the time correspondence between the POS trajectory and the laser point cloud data, the laser point cloud in the engineering data is divided to obtain multiple laser point cloud segments corresponding to the trajectory segments respectively.

4. The method according to claim 2, characterized in that, The step of partitioning multiple laser point cloud segments of multiple engineering data to obtain multiple non-overlapping laser partitions includes: For each of the multiple laser point cloud segments in the multiple engineering data, determine the minimum outer bound of the laser point cloud segment; Laser point cloud segments with overlapping minimum outer regions among multiple laser point cloud segments of multiple engineering data are divided into the same partition to obtain multiple laser partitions that do not overlap with each other.

5. The method according to claim 4, characterized in that, After dividing the laser point cloud segments with overlapping minimum outer regions among the multiple laser point cloud segments of the multiple engineering data into the same partition to obtain multiple non-overlapping laser partitions, the method further includes: The isolated laser point cloud segment whose minimum outer bound does not overlap with other minimum outer bounds among the multiple laser point cloud segments of the multiple engineering data is added to the laser partition with the shortest distance to the isolated laser point cloud segment.

6. The method according to any one of claims 1-4, characterized in that, The process of joining multiple aligned laser partitions includes: The multiple aligned laser partitions are sequentially joined according to their adjacent relationships; For the currently processed partition among the multiple aligned laser partitions, the deviation between the currently processed partition and the previously processed partition, as well as the edge length of the edge region of the currently processed partition, are determined. The coordinates of the laser points in the edge region are updated according to the deviation and the edge length to complete the edge processing between the currently processed partition and the previously processed partition.

7. A point cloud data acquisition device, characterized in that, include: The acquisition module is used to acquire engineering data corresponding to multiple engineering tasks in the target acquisition area. The engineering data is obtained by collecting data from the target collection area based on the corresponding engineering task; The fusion module is used to fuse the laser point clouds in the engineering data corresponding to multiple engineering tasks to obtain the target laser point cloud data of the target acquisition area. The acquisition module is specifically used to: divide the target acquisition area into engineering tasks to obtain multiple engineering tasks; acquire the engineering data corresponding to the multiple engineering tasks obtained by the acquisition vehicle from data acquisition of the target acquisition area based on the multiple engineering tasks, wherein the engineering data includes the complete point cloud data of the target acquisition area; The fusion module is specifically used for: partitioning the laser point cloud in the engineering data corresponding to multiple engineering tasks to obtain multiple non-overlapping laser partitions; performing point cloud consistency alignment processing on the multiple laser partitions to obtain aligned laser partitions; and performing edge-joining processing on the multiple aligned laser partitions to obtain target laser point cloud data of the target acquisition area.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when executed by the processor, implement the point cloud data acquisition method as described in any one of claims 1 to 6.

9. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the point cloud data acquisition method according to any one of claims 1 to 6.

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