Point cloud map updating method and device for digging and loading operation and engineering vehicle

Abstract

The invention discloses a point cloud map updating method and device for digging and loading operation and an engineering vehicle. The method comprises the steps that an original laser radar point cloud data set and a getting-on and getting-off rotation angle data set of an excavator rotating by one circle are obtained; performing spatial transformation on the point cloud data in the original laser radar point cloud data set to obtain global laser radar point cloud data under a getting-off coordinate system; performing regional index management on the global laser radar point cloud data based on the get-on and get-off rotation angle data set; constructing a laser radar point cloud map based on mining and loading area information and global laser radar point cloud data; performing real-time updating on the laser radar point cloud map by utilizing regional index management according to the current boarding and alighting rotation angle and angular velocity and the operation state of the excavator in the digging operation cycle; according to the method, the real-time point cloud map can be constructed and updated in combination with the rotary motion of the excavator, so that the accuracy and efficiency of updating the point cloud map are improved.

Description

Technical Field

[0001] The present invention relates to a point cloud map updating method, device and engineering vehicle for excavation and loading operations, and belongs to the field of intelligent engineering machinery. Background Art

[0002] As a core engineering equipment, excavators are widely used in construction and mining. Under the challenge of harsh working environment and high labor intensity, excavator operation may cause safety problems; at the same time, the continuous increase in labor costs also brings challenges to companies in the industry. Therefore, the development of automatically operated excavators has a wide market demand and practical value.

[0003] Point cloud maps play a vital role in the process of realizing excavator automation. They are not only a reflection of technological progress, but also the key to improving the efficiency and safety of engineering operations. Its core function - creating a three-dimensional map of the site, provides detailed and accurate environmental information for automatic excavators, enabling the machine to intelligently perform a series of complex tasks such as excavation and loading.

[0004] Automatic excavators usually rotate in a relatively fixed area most of the time to perform excavation and loading tasks. In this environment, only a small range of updates are required for the map of the operating area. Current technology mainly uses multi-line laser radar to create point cloud maps through SLAM technology. However, the problem with this technology is that the accuracy is not high if the number of radar lines is small, and the cost increases if the number of radar lines is large; another problem is that it will cause data and calculation redundancy, and lack a response strategy to changes in the operating area during the update process. For example, the patent "Point Cloud Map Update Method and Device, Engineering Vehicle and Storage Medium" with application number 202311718181.7 requires the installation of multiple vehicle-mounted radars to cover 360-degree operating scenes, including non-excavation and loading areas, which increases hardware costs and brings a large amount of unnecessary data calculations. When the point cloud map is updated, the first predetermined period and the second predetermined period need to be manually determined based on actual working conditions and experience, and after determination, the map is updated at this fixed frequency, which has a large uncertainty and affects the accuracy and efficiency of map updates. Summary of the invention

[0005] The purpose of the present invention is to overcome the shortcomings of the prior art and provide a point cloud map updating method, device and engineering vehicle for excavation and loading operations, which can combine the rotational motion of the excavator to perform real-time point cloud map construction and updating, thereby improving the accuracy and efficiency of point cloud map updating, and this method only requires one laser radar and has a low cost.

[0006] To achieve the above object, the present invention is implemented by adopting the following technical solutions:

[0007] In a first aspect, the present invention provides a point cloud map updating method for excavation and loading operations, comprising:

[0008] Obtain the original LiDAR point cloud dataset and the vehicle rotation angle dataset for one rotation of the excavator;

[0009] Based on the original laser radar point cloud data set and the vehicle getting on and off rotation angle data set, spatially transforming the point cloud data in the original laser radar point cloud data set to obtain global laser radar point cloud data in the vehicle getting off coordinate system;

[0010] Based on the vehicle boarding and disembarking rotation angle dataset, the global LiDAR point cloud data is indexed and managed by region;

[0011] Construct a lidar point cloud map based on the excavation and loading area information and the global lidar point cloud data;

[0012] By utilizing regional index management, the laser radar point cloud map is updated in real time according to the current vehicle rotation angle and angular velocity, as well as the operating status of the excavator in the excavation and loading operation cycle.

[0013] Optionally, based on the original laser radar point cloud data set and the vehicle getting on and off rotation angle data set, spatially transforming the data in the original laser radar point cloud data set to obtain global laser radar point cloud data in the vehicle getting off coordinate system includes:

[0014] Performing time stamp synchronization on the point cloud data in the original laser radar point cloud data set and the angle data in the vehicle getting on and off rotation angle data set to obtain time stamp synchronized point cloud data and angle data;

[0015] Based on the point cloud data and angle data after time stamp synchronization, the spatial transformation is performed to obtain the laser radar point cloud data in the off-vehicle coordinate system. The formula is as follows:

[0016]

[0017] Among them: PC under PC is the homogeneous coordinates of the laser radar point cloud in the coordinate system of the vehicle getting off, that is, the laser radar point cloud data in the coordinate system of the vehicle getting off; raw is the homogeneous coordinates of the point cloud after timestamp synchronization, that is, the point cloud data after timestamp synchronization; is the homogeneous transformation matrix from the radar coordinate system to the vehicle coordinate system; is the homogeneous transformation matrix from the boarding coordinate system to the getting-off coordinate system;

[0018] The laser radar point cloud data in the get-off-car coordinate system are combined to obtain the global laser radar point cloud data in the get-off-car coordinate system.

[0019] Optionally, the region-based index management of the global LiDAR point cloud data based on the vehicle boarding and disembarking rotation angle data set includes:

[0020] According to the rotation angle resolution and the farthest detection radius of the laser radar, the global laser radar point cloud data in the off-vehicle coordinate system is partitioned with the excavator's rotation center as the center of the circle to obtain multiple sector areas; the rotation angle resolution and the farthest detection radius of the laser radar are selected according to the task requirements; the angle range of each sector area is determined by the on-vehicle rotation angle data set;

[0021] The center line of the fan-shaped area is used as the index of the fan-shaped area.

[0022] Optionally, constructing a lidar point cloud map based on the excavation and loading area information and the global lidar point cloud data includes:

[0023] Acquire the set excavation area information and loading area information; the excavation area information includes: the shortest radius, the maximum radius, the left boundary turning angle and the right boundary turning angle of the excavation area; the loading area information includes: the shortest radius, the maximum radius, the left boundary turning angle and the right boundary turning angle of the loading area;

[0024] Based on the set excavation area information and loading area information, the excavation area and loading area are defined, and based on the global lidar point cloud data in the off-vehicle coordinate system, a lidar point cloud map is spliced ​​to obtain the lidar point cloud map; wherein the lidar point cloud map includes: an excavation area, a loading area, and a non-excavation and loading area.

[0025] Optionally, the use of regional index management and real-time updating of the laser radar point cloud map according to the current vehicle rotation angle and angular velocity, and the operating state of the excavator in the excavation and loading operation cycle include:

[0026] Based on the current angular velocity of the vehicle getting on and off, determine whether the LiDAR point cloud map needs to be updated;

[0027] If an update is required, determine whether the current vehicle rotation angle is in the excavation area or loading area;

[0028] If the current rotation angle of the loading and unloading vehicle is in the excavation area, the index is θ0±Δ dig / 2 point cloud map area is updated, where θ0 is the current boarding and alighting rotation angle, Δ dig Update the opening angle for the excavation area point cloud, based on the width of the bucket;

[0029] If the current boarding and alighting rotation angle is in the loading area, the index is θ0±Δ unload / 2 point cloud map area is updated, where Δ unloadUpdates the opening angle for the loading area point cloud, based on the loading target determination.

[0030] Optionally, the step of determining whether the laser radar point cloud map needs to be updated includes:

[0031] If the current angular velocity direction of the vehicle on and off changes, the laser radar point cloud map is updated;

[0032] If the current angular velocity direction of the vehicle getting on and off does not change, it is determined that the laser radar point cloud map will not be updated.

[0033] Optionally, the step of determining whether the laser radar point cloud map needs to be updated further includes:

[0034] If the current vehicle getting on and off the vehicle continues to decrease and is less than the threshold, it is determined that the laser radar point cloud map is updated; otherwise, it is determined that the laser radar point cloud map is not updated.

[0035] In a second aspect, the present invention provides a point cloud map updating device for excavation and loading operations, comprising:

[0036] The acquisition module is used to collect the original laser radar point cloud data, as well as the rotation angle and angular velocity of the vehicle on and off in real time;

[0037] A control module, including a data acquisition unit, a space transformation unit, an index management unit, a map construction unit and a point cloud map update unit;

[0038] The data acquisition unit is used to receive the data collected by the acquisition module and obtain the original laser radar point cloud data set and the on-board rotation angle data set of the excavator during one rotation;

[0039] A spatial transformation unit is used to perform spatial transformation on the point cloud data in the original laser radar point cloud data set based on the original laser radar point cloud data set and the vehicle getting on and off rotation angle data set to obtain global laser radar point cloud data in the vehicle getting off coordinate system;

[0040] An index management unit is used to perform regional index management on the global LiDAR point cloud data based on the vehicle boarding and disembarking rotation angle data set;

[0041] A map construction unit, for constructing a lidar point cloud map based on the excavation and loading area information and the global lidar point cloud data;

[0042] The point cloud map updating unit is used to utilize the regional index management and to perform real-time updating of the laser radar point cloud map according to the current vehicle loading and unloading rotation angle and angular velocity, and the operating status of the excavator in the excavation and loading operation cycle.

[0043] Optionally, the acquisition module includes: a laser radar arranged on the top of the excavator and an angle sensor placed on the excavator turntable.

[0044] In a third aspect, the present invention provides an engineering vehicle, comprising the point cloud map updating device described in the second aspect.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] The present invention proposes a point cloud map updating method for excavation and loading operations, which utilizes the rotary motion characteristics of the excavator in the excavation and loading operation, and performs real-time point cloud map construction and updating based on the rotary angle and angular velocity of the upper and lower vehicles, as well as the operating status of the excavator in the excavation and loading operation cycle, without the need for manual determination of updates, thereby improving the accuracy and efficiency of map updates;

[0047] The present invention proposes a point cloud map updating method for excavation and loading operations. Based on the cyclic nature of excavation and loading operations, the method utilizes the fact that when the material changes (i.e., the point cloud state changes) during excavation and loading operations, the excavator and the excavator do not rotate relative to each other. The point cloud map update area is determined according to the rotation angles of the upper and lower vehicles. In addition, the method utilizes the fact that the upper and lower vehicles rotate in the opposite direction after the excavation and loading operations are completed. The point cloud map update timing is determined according to the rotation angular velocity of the upper and lower vehicles, thereby improving the accuracy and efficiency of the point cloud map update and improving the precision and quality of the point cloud map.

[0048] The present invention proposes a point cloud map updating method, device and engineering vehicle for excavation and loading operations, which only requires a laser radar to be set on the top of the excavator to complete the construction and update of the point cloud map, so the cost is lower than that of the existing point cloud map updating method. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative labor, among which:

[0050] Figure 1 The flowchart of the point cloud map updating method for excavation and loading operations in an embodiment of the present invention is shown;

[0051] Figure 2 The schematic diagram of the excavation and loading operation in the embodiment of the present invention is shown;

[0052] Figure 3 The schematic diagram of the sector-shaped area of ​​the laser radar point cloud in an embodiment of the present invention is shown;

[0053] Figure 4The schematic diagram of the excavation area and the unloading area in the embodiment of the present invention is shown;

[0054] Figure 5 The figure shows a schematic diagram of the decision process of the point cloud map update strategy in an embodiment of the present invention. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present disclosure to clearly and completely describe the technical solutions in the embodiments of the present disclosure. Obviously, the described embodiments are only part of the embodiments of the present disclosure, rather than all of the embodiments. The following description of at least one exemplary embodiment is actually only illustrative and is by no means intended to limit the present disclosure and its application or use. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0056] Unless specifically stated otherwise, the relative arrangement of components and steps, the numerical expressions and numerical values ​​set forth in these embodiments do not limit the scope of the present disclosure.

[0057] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship.

[0058] Technologies, methods, and apparatus known to ordinary technicians in the relevant field may not be discussed in detail, but where appropriate, such technologies, methods, and apparatus should be considered part of the authorization specification.

[0059] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limiting. Therefore, other examples of the exemplary embodiments may have different values.

[0060] The application principle of the present invention is described in detail below in conjunction with the accompanying drawings.

[0061] Example 1

[0062] like Figure 1 As shown, an embodiment of the present invention provides a point cloud map updating method for excavation and loading operations, comprising the following steps:

[0063] S01: Obtain the original LiDAR point cloud dataset and the vehicle rotation angle dataset for one rotation of the excavator;

[0064] S02: Based on the original laser radar point cloud data set and the vehicle getting on and off rotation angle data set, spatially transform the point cloud data in the original laser radar point cloud data set to obtain global laser radar point cloud data in the vehicle getting off coordinate system;

[0065] S03: Based on the vehicle boarding and disembarking rotation angle dataset, the global LiDAR point cloud data is indexed and managed by region;

[0066] S04: Construct a lidar point cloud map based on the excavation and loading area information and the global lidar point cloud data;

[0067] S05: Using regional index management, and according to the current vehicle loading and unloading rotation angle and angular velocity, and the operating status of the excavator in the excavation and loading operation cycle, the laser radar point cloud map is updated in real time.

[0068] Specifically, Figure 2 As shown, the upper and lower vehicle rotation angle data set is obtained by a posture sensor (angle sensor) placed on the excavator turntable; the upper and lower vehicle rotation angle data set includes angle data collected in real time when the upper vehicle of the excavator rotates one circle relative to the lower vehicle;

[0069] Specifically, the original lidar point cloud dataset is obtained by a lidar placed on top of the excavator, wherein the field of view and direction of the lidar are set manually; the original lidar point cloud dataset includes point cloud data collected in real time when the lidar rotates one circle (since the lidar is fixed on the excavator, it is achieved by rotating the excavator).

[0070] Specifically, based on the current rotation angle and angular velocity of the vehicle getting on and off, as well as the operating status of the excavator in the digging and loading cycle, determine whether the lidar point cloud map needs to be updated and the updated area; among which, the current rotation angle and angular velocity of the vehicle getting on and off are also obtained through the angle sensor.

[0071] The present invention proposes a point cloud map updating method for excavation and loading operations. The method utilizes the rotational motion characteristics of the excavator in the excavation and loading operations, and constructs and updates the point cloud map in real time based on the rotation angle data of the upper and lower vehicles and the operating status of the excavator in the excavation and loading operation cycle. There is no need to manually determine the update, thereby improving the accuracy and efficiency of the map update.

[0072] In this embodiment, in step S02, based on the original laser radar point cloud data set and the vehicle getting on and off rotation angle data set, the point cloud data in the original laser radar point cloud data set is spatially transformed to obtain the global laser radar point cloud data in the vehicle getting off coordinate system, specifically including:

[0073] The timestamps of the original laser radar point cloud data set and the vehicle boarding and disembarking rotation angle data set are synchronized for multi-source data to ensure the time consistency of the point cloud and the rotation angle in the single frame data;

[0074] Specifically, timestamp synchronization can use time synchronization algorithms such as Kalman filtering, dynamic time warping, timestamp sliding window correction, and timestamp sliding window correction;

[0075] Based on the original laser radar point cloud data set after timestamp synchronization and the vehicle getting on and off rotation angle data set, the laser radar point cloud data in the vehicle getting off coordinate system is calculated and the formula is as follows:

[0076]

[0077] Among them: PC under PC is the homogeneous coordinates of the laser radar point cloud in the coordinate system of the vehicle getting off, that is, the laser radar point cloud data in the coordinate system of the vehicle getting off; raw is the homogeneous coordinate of the point cloud after timestamp synchronization, that is, the point cloud data in the original lidar point cloud dataset after timestamp synchronization; is the homogeneous transformation matrix from the radar coordinate system to the vehicle coordinate system; is the homogeneous transformation matrix from the boarding coordinate system to the getting-off coordinate system;

[0078] The laser radar point cloud data in the get-off-car coordinate system are combined to obtain the global laser radar point cloud data in the get-off-car coordinate system.

[0079] Specifically, the homogeneous transformation matrix from the radar coordinate system to the vehicle coordinate system is Calculated based on the position and angle of the laser radar; because the installation position of the laser radar can be adjusted, there is no fixed homogeneous transformation matrix Among them, the homogeneous transformation matrix The calculation method is to first determine the two coordinates, and then use an algorithm such as ICP to iterate and automatically solve. I will not go into details here.

[0080] Specifically, the homogeneous transformation matrix from the boarding coordinate system to the disembarking coordinate system is It is expressed as follows:

[0081]

[0082] Among them, θ is the rotation angle of the boarding and getting-on vehicle, and h is the vertical displacement distance of the boarding and getting-on vehicle coordinate system.

[0083] Specifically, Figure 2 As mentioned above, the original laser radar point cloud data set collected by the laser radar is based on the radar coordinate system, so it is necessary to combine the vehicle getting on and off the vehicle rotation angle data set to convert the point cloud data in the original laser radar point cloud data set in the radar coordinate system into the laser radar point cloud data in the vehicle getting off the vehicle coordinate system, and finally obtain the global laser radar point cloud data in the vehicle getting off the vehicle coordinate system;

[0084] In this embodiment, in step S03, the global laser radar point cloud data is indexed and managed by region based on the vehicle boarding and disembarking rotation angle data set, including:

[0085] According to the rotation angle resolution α and the farthest detection radius R of the laser radar, the laser radar point cloud data in the off-vehicle coordinate system is partitioned with the rotation center of the excavator as the center of the circle to obtain multiple fan-shaped areas; Figure 3 As shown, the rotation angle resolution α is set to 15°, and the laser radar point cloud data in the off-vehicle coordinate system is divided into 24 sector areas;

[0086] The rotation angle resolution α and the maximum detection radius R of the laser radar are selected according to the task requirements; the angle range of each sector area is determined by the rotation angle data set of the boarding and disembarking vehicles;

[0087] The center line of the fan-shaped area is used as the index of the fan-shaped area.

[0088] In this embodiment, if Figure 4 As shown, in step S04, a lidar point cloud map is constructed based on the excavation and loading area information and the global lidar point cloud data, including:

[0089] Obtain excavation area information and loading area information; the excavation area information and loading area information are independently determined by the upstream decision-making and planning module, or manually specified by the upstream remote operation module; wherein the excavation area information and loading area information both include the shortest radius r proximal , maximum radius r distal 、Left boundary rotation angle θ left 、Right boundary rotation angle θ right ,like Figure 4 As shown, the excavation area (excavation area and loading area) can be defined by these four information parameters; specifically, the shortest radius r proximal The shortest distance between the excavation area and the excavator's rotation center, and the maximum radius r distal The longest distance between the excavation area and the rotation center of the excavator, combined with the excavation range / loading range, can determine the boundary of the excavation area, thereby defining the excavation area; the excavation range / loading range can be determined by the left boundary rotation angle θ left and the right boundary rotation angle θ right To determine, first define an initial angle line (0° line), which should be the line where the excavator's boom and arm are mapped on the ground when the excavator is stationary. Based on the initial angle line (0° line), define the left boundary rotation angle θ left and the right boundary rotation angle θ right .

[0090] According to the defined excavation area and loading area, combined with the global lidar point cloud data in the vehicle coordinate system, a lidar point cloud map is spliced ​​to obtain; wherein, the lidar point cloud map includes: an excavation area, a loading area and a non-excavation area, and the non-excavation area is an area where the global lidar point cloud data does not define the excavation area and the loading area.

[0091] In this embodiment, in step S05, the laser radar point cloud map is updated in real time by using regional index management and according to the current vehicle rotation angle and angular velocity, and the operating state of the excavator in the excavation and loading operation cycle, specifically including:

[0092] S051: Get the current boarding and exiting rotation angle and angular velocity;

[0093] Specifically, this step should be to capture the rotation of the excavator on and off the vehicle in real time through the angular velocity sensor after the lidar point cloud map is generated;

[0094] S052: Based on the current vehicle boarding and disembarking rotation angular velocity, determine whether the laser radar point cloud map needs to be updated;

[0095] Specifically, if the current angular velocity direction of getting on and off the vehicle changes, that is, the current angular velocity direction of getting on and off the vehicle and the angular velocity direction of the vehicle at the last moment change, then it is determined to update the lidar point cloud map; if the current angular velocity direction of getting on and off the vehicle does not change, that is, the current angular velocity direction of getting on and off the vehicle and the angular velocity direction of the vehicle at the last moment do not change, then it is determined not to update the lidar point cloud map.

[0096] Specifically, we observed that if the material is relatively dry, a lot of dust will be generated during excavation and loading, and the point cloud at this time may not truly reflect the shape of the material. To address this issue, in addition to using the rotation angular velocity direction of the upper and lower vehicles to determine whether the lidar point cloud map needs to be updated, the rotation angular velocity size and state of the upper and lower vehicles can also be used to determine;

[0097] If the current vehicle getting on and off the vehicle continues to decrease and is less than the threshold, it is determined that the laser radar point cloud map is updated; otherwise, it is determined that the laser radar point cloud map is not updated.

[0098] S053: Based on the judgment result of step S053, the excavation area or the loading area is updated. The specific steps are as follows:

[0099] If step S053 determines that the laser radar point cloud map needs to be updated, it is then determined whether the current vehicle rotation angle is in the excavation area or the loading area;

[0100] S0531: If the current vehicle rotation angle is in the excavation area, the index is θ0±Δ dig / 2 point cloud map area is updated, where θ0 is the current boarding and alighting rotation angle, Δ dig Update the opening angle for the excavation area point cloud, based on the width of the bucket;

[0101] Specifically, the mining area point cloud updates the angle Δ digThe calculation formula is as follows:

[0102]

[0103] Among them, l bucket_width is the width of the excavator bucket, l min_dig_distance It is the projection of the distance from the excavation end point to the excavator's rotation center in the horizontal plane.

[0104] S0532: If the current boarding and alighting rotation angle is in the loading area, the index is θ0±Δ unload / 2 point cloud map area is updated, where Δ unload Update the opening angle for the loading area point cloud, based on the loading target determination;

[0105] Specifically, when loading the target truck bucket, the loading area point cloud updates the angle Δ unload The calculation formula is as follows:

[0106]

[0107] Among them, l trunk_width is the width of the truck bed, l min_unload_distance It is the projection of the distance from the near end of the truck bed to the rotation center of the excavator in the horizontal plane.

[0108] Example 2

[0109] The embodiment of the present invention provides a point cloud map updating device for excavation and loading operations, comprising: a collection module and a control module;

[0110] The acquisition module is used to collect the original laser radar point cloud data, as well as the rotation angle and angular velocity of the vehicle on and off in real time;

[0111] A control module, including a data acquisition unit, a space transformation unit, an index management unit, a map construction unit and a point cloud map update unit;

[0112] The data acquisition unit is used to receive the data collected by the acquisition module and obtain the original laser radar point cloud data set and the on-board rotation angle data set of the excavator during one rotation;

[0113] A spatial transformation unit is used to perform spatial transformation on the point cloud data in the original laser radar point cloud data set based on the original laser radar point cloud data set and the vehicle getting on and off rotation angle data set to obtain global laser radar point cloud data in the vehicle getting off coordinate system;

[0114] An index management unit is used to perform regional index management on the global LiDAR point cloud data based on the vehicle boarding and disembarking rotation angle data set;

[0115] A map construction unit, for constructing a lidar point cloud map based on the excavation and loading area information and the global lidar point cloud data;

[0116] The point cloud map updating unit is used to utilize the regional index management and to perform real-time updating of the laser radar point cloud map according to the current vehicle loading and unloading rotation angle and angular velocity, and the operating status of the excavator in the excavation and loading operation cycle.

[0117] In this embodiment, the acquisition module includes: a laser radar arranged on the top of the excavator and an angle sensor placed on the turntable of the excavator.

[0118] It should be understood that the modules to modules recorded in the point cloud map updating device for excavation and loading operations provided in the embodiment of the present invention respectively correspond to the steps in the method described in Example 1. Therefore, the operations and features described above for the point cloud map updating method for excavation and loading operations are also applicable to the device and the modules contained therein, and will not be repeated here.

[0119] Example 3

[0120] An embodiment of the present invention provides a point cloud map update and installation system for excavation and installation operations. This set of electronic equipment may include the following parts: one or more processors, storage devices, one or more communication interfaces and one or more communication buses, which together provide communication functions between the components of the device. Specifically, the function of the processor is to execute the program modules or instructions described in the present disclosure. These program modules require the processor to be implemented through program modules stored in the memory. In addition, the memory can also be connected to the memory of a remote cloud computing center through a communication interface to receive control instructions related to excavator operations, such as excavation, grabbing and walking control. The processor may also further integrate hardware chips, such as programmable logic devices, application-specific integrated circuits, or any combination of these chips.

[0121] Specifically, the memory may include a non-volatile memory and a high-speed random access memory (RAM), or any combination thereof. In addition, the communication interface may include a display screen, a keyboard, and a standard wired or wireless interface. It can also realize the communication connection between the unmanned excavator task switching system and the remote cloud computing system, and the connection medium may be a local area network, the Internet, etc. The communication bus may be an industrial standard architecture (ISA) bus, an extended industrial standard structure (EISA) bus, etc. So far, this document has fully explained the disclosure. In order to ensure that the core concept of the present disclosure is not obscured by the details, certain details known in the industry are not involved. Industry professionals can easily understand and implement the technical solutions introduced in this disclosure based on the information provided.

[0122] Example 3

[0123] An embodiment of the present invention provides an engineering vehicle, including a point cloud map updating device;

[0124] In some embodiments, the construction vehicle includes an excavator.

[0125] By implementing the above embodiments of the present disclosure, the following beneficial effects can be obtained:

[0126] (1) Before the excavator starts digging and loading, it can automatically load the original LiDAR point cloud map;

[0127] (2) When the bucket is performing an excavation operation in the excavation area, or when the bucket is performing a loading operation on top of a truck

[0128] After that, the shape of the materials in the excavation area will change. When the excavator completes the operation and starts the next operation, the excavator starts to rotate. The point cloud map of the excavation area is updated dynamically in real time through the rotation of the excavator and the angle sensor.

[0129] The embodiments of the present invention are described above in conjunction with the accompanying drawings, but the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the enlightenment of the present invention, ordinary technicians in this field can also make many forms without departing from the scope of protection of the purpose of the present invention and the claims, which all fall within the protection of the present invention.

Claims

1. A point cloud map updating method for excavation and loading operations, characterized in that: include: Obtain the original LiDAR point cloud dataset and the vehicle rotation angle dataset for one rotation of the excavator; Based on the original laser radar point cloud data set and the vehicle getting on and off rotation angle data set, spatially transforming the point cloud data in the original laser radar point cloud data set to obtain global laser radar point cloud data in the vehicle getting off coordinate system; Based on the vehicle boarding and disembarking rotation angle dataset, the global LiDAR point cloud data is indexed and managed by region; Construct a lidar point cloud map based on the excavation and loading area information and the global lidar point cloud data; By utilizing regional index management, the laser radar point cloud map is updated in real time according to the current vehicle rotation angle and angular velocity, as well as the operating status of the excavator in the excavation and loading operation cycle.

2. The point cloud map updating method for excavation and loading operations according to claim 1, characterized in that: Based on the original laser radar point cloud data set and the vehicle getting on and off rotation angle data set, the data in the original laser radar point cloud data set is spatially transformed to obtain global laser radar point cloud data in the vehicle getting off coordinate system, including: Performing time stamp synchronization on the point cloud data in the original laser radar point cloud data set and the angle data in the vehicle getting on and off rotation angle data set to obtain time stamp synchronized point cloud data and angle data; Based on the point cloud data and angle data after time stamp synchronization, the spatial transformation is performed to obtain the laser radar point cloud data in the off-vehicle coordinate system. The formula is as follows: ； in: is the homogeneous coordinates of the laser radar point cloud in the coordinate system of the vehicle getting off, that is, the laser radar point cloud data in the coordinate system of the vehicle getting off; is the homogeneous coordinates of the point cloud after timestamp synchronization, that is, the point cloud data after timestamp synchronization; is the homogeneous transformation matrix from the radar coordinate system to the vehicle coordinate system; is the homogeneous transformation matrix from the boarding coordinate system to the getting-off coordinate system; The laser radar point cloud data in the get-off-car coordinate system are combined to obtain the global laser radar point cloud data in the get-off-car coordinate system.

3. The point cloud map updating method for excavation and loading operations according to claim 2, characterized in that: The regional index management of the global laser radar point cloud data based on the vehicle boarding and disembarking rotation angle data set includes: According to the rotation angle resolution and the farthest detection radius of the laser radar, the global laser radar point cloud data in the off-vehicle coordinate system is partitioned with the excavator's rotation center as the center of the circle to obtain multiple sector areas; the rotation angle resolution and the farthest detection radius of the laser radar are selected according to the task requirements; the angle range of each sector area is determined by the on-vehicle rotation angle data set; The center line of the fan-shaped area is used as the index of the fan-shaped area.

4. The point cloud map updating method for excavation and loading operations according to claim 3, characterized in that: The method of constructing a LiDAR point cloud map based on the excavation and loading area information and the global LiDAR point cloud data includes: Acquire the set excavation area information and loading area information; the excavation area information includes: the shortest radius, the maximum radius, the left boundary turning angle and the right boundary turning angle of the excavation area; the loading area information includes: the shortest radius, the maximum radius, the left boundary turning angle and the right boundary turning angle of the loading area; Based on the set excavation area information and loading area information, the excavation area and loading area are defined, and based on the global lidar point cloud data in the off-vehicle coordinate system, a lidar point cloud map is spliced ​​to obtain the lidar point cloud map; wherein the lidar point cloud map includes: an excavation area, a loading area, and a non-excavation and loading area.

5. The point cloud map updating method for excavation and loading operations according to claim 4, characterized in that: The use of regional index management and real-time updating of the laser radar point cloud map according to the current vehicle rotation angle and angular velocity, and the operating status of the excavator in the excavation and loading operation cycle include: Based on the current angular velocity of the vehicle getting on and off, determine whether the LiDAR point cloud map needs to be updated; If an update is required, determine whether the current vehicle rotation angle is in the excavation area or loading area; If the current rotation angle of the loading and unloading vehicle is in the excavation area, then the index is The point cloud map area is updated, where is the current boarding and alighting rotation angle, Update the opening angle for the excavation area point cloud, based on the width of the bucket; If the current boarding and alighting rotation angle is in the loading area, then the index is The point cloud map area is updated, where Updates the opening angle for the loading area point cloud, based on the loading target determination.

6. The point cloud map updating method for excavation and loading operations according to claim 5, characterized in that: The step of determining whether the laser radar point cloud map needs to be updated includes: If the current angular velocity direction of the vehicle on and off changes, the laser radar point cloud map is updated; If the current angular velocity direction of the vehicle getting on and off does not change, it is determined that the laser radar point cloud map will not be updated.

7. The point cloud map updating method for excavation and loading operations according to claim 6, characterized in that: The step of determining whether the laser radar point cloud map needs to be updated also includes: If the current vehicle getting on and off the vehicle continues to decrease and is less than the threshold, it is determined that the laser radar point cloud map is updated; otherwise, it is determined that the laser radar point cloud map is not updated.

8. A point cloud map updating device for excavation and loading operations, characterized in that: include: The acquisition module is used to collect the original laser radar point cloud data, as well as the rotation angle and angular velocity of the vehicle on and off in real time; A control module, including a data acquisition unit, a space transformation unit, an index management unit, a map construction unit and a point cloud map update unit; The data acquisition unit is used to receive the data collected by the acquisition module and obtain the original laser radar point cloud data set and the on-board rotation angle data set of the excavator during one rotation; A spatial transformation unit is used to perform spatial transformation on the point cloud data in the original laser radar point cloud data set based on the original laser radar point cloud data set and the vehicle getting on and off rotation angle data set to obtain global laser radar point cloud data in the vehicle getting off coordinate system; An index management unit is used to perform regional index management on the global LiDAR point cloud data based on the vehicle boarding and disembarking rotation angle data set; A map construction unit, for constructing a lidar point cloud map based on the excavation and loading area information and the global lidar point cloud data; The point cloud map updating unit is used to utilize the regional index management and to perform real-time updating of the laser radar point cloud map according to the current vehicle loading and unloading rotation angle and angular velocity, and the operating status of the excavator in the excavation and loading operation cycle.

9. The point cloud map updating device for excavation and loading operations according to claim 8, characterized in that: The acquisition module includes: a laser radar arranged on the top of the excavator and an angle sensor placed on the excavator turntable.

10. An engineering vehicle, characterized in that: It comprises a point cloud map updating device as described in claim 8 or 9.

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