Point cloud map updating method and system, domain controller, excavator and storage medium

Abstract

The invention relates to a point cloud map updating method and system, a domain controller, an excavator and a storage medium. The point cloud map updating method comprises the following steps: constructing a point cloud map of an excavator rotation operation scene according to laser radar point cloud data and turntable attitude sensor data; dividing the point cloud map into a plurality of areas; determining an area index value of each area according to the rotation angle of the excavator; and updating the point cloud map according to the region index value. According to the method, the operation scene can be mapped and updated by utilizing the rotation operation motion of the excavator, the map index is established according to the rotation angle, the map is updated according to the index, and the map retrieval and updating efficiency is improved.

Description

Technical Field

[0001] The present disclosure relates to the field of intelligent unmanned engineering machinery, and in particular to a point cloud map updating method and system, a domain controller and an excavator, and a storage medium. Background Art

[0002] Excavators are important engineering machinery, widely used in construction, mining and other engineering construction industries. Excavators often encounter problems such as harsh operating environment and high workload for operators, which can easily lead to safety hazards. In addition, the difficulty in hiring and high costs will also limit the industry. Therefore, the development of excavators with unmanned operation functions has huge industry demand and practical significance. Summary of the invention

[0003] The inventors discovered through research that point cloud maps, as an important part of unmanned excavator operations, mainly function to construct three-dimensional point cloud maps of real scenes for unmanned excavators to plan excavation and loading tasks.

[0004] A typical operating condition of an unmanned excavator is to rotate on a relatively fixed platform to perform excavation and loading operations. For this typical working condition, only a small range of working surface updates need to be implemented during the excavation process. Relevant technical systems and methods mostly use multi-line laser radars with more than 16 lines to establish point cloud maps through SLAM, which has large data redundancy and calculation amount, and the boom is not processed during map construction, so the boom is separated from the material.

[0005] In view of at least one of the above technical problems, the present disclosure provides a point cloud map updating method and system, a domain controller and an excavator, and a storage medium, which utilize the movement of the excavator's rotation operation to map and update the work scene, establish a map index based on the rotation angle, and update the map based on the index, thereby improving the efficiency of map retrieval and update.

[0006] According to one aspect of the present disclosure, a point cloud map updating method is provided, comprising:

[0007] Construct a point cloud map of the excavator's rotary operation scene based on the lidar point cloud data and turntable attitude sensor data;

[0008] Dividing the point cloud map into a plurality of regions;

[0009] Determine the area index value of each area according to the rotation angle of the excavator;

[0010] The point cloud map is updated according to the area index value.

[0011] In some embodiments of the present disclosure, updating the point cloud map according to the region index value includes:

[0012] receiving a map update instruction, wherein the map update instruction includes an angle range of an area to be updated;

[0013] Determine the index interval of the area to be updated according to the angle range of the area to be updated;

[0014] The to-be-updated area of ​​the point cloud map is updated according to the index interval.

[0015] In some embodiments of the present disclosure, the area to be updated includes at least one of a loading area and an unloading area.

[0016] In some embodiments of the present disclosure, when the area to be updated includes the loading area, updating the area to be updated of the point cloud map according to the index interval includes:

[0017] Clear the point cloud data of the loading area according to the index interval;

[0018] The loading area is updated according to the index interval.

[0019] In some embodiments of the present disclosure, when the area to be updated includes the unloading area, updating the area to be updated of the point cloud map according to the index interval includes:

[0020] clearing the point cloud data of the unloading area according to the index interval;

[0021] The unloading area is updated according to the index interval.

[0022] In some embodiments of the present disclosure, updating the point cloud map according to the region index value includes:

[0023] The map of the area where the current excavator rotation angle is located is updated at a predetermined time interval.

[0024] In some embodiments of the present disclosure, updating the map of the area where the current excavator rotation angle is located includes:

[0025] Determine the region index value of the region where the current excavator rotation angle is located according to the current excavator rotation angle;

[0026] According to the area index value of the area where the current excavator rotation angle is located, the map of the area where the current excavator rotation angle is located is updated.

[0027] In some embodiments of the present disclosure, determining the region index value of the region where the current excavator rotation angle is located according to the current excavator rotation angle includes:

[0028] According to the current excavator rotation angle and the rotation angle range of a region, a region index value of the region where the current excavator rotation angle is located is determined.

[0029] In some embodiments of the present disclosure, updating the map of the area where the current excavator rotation angle is located according to the area index value of the area where the current excavator rotation angle is located includes:

[0030] Determine the cumulative number N according to the rotation angle frequency and the laser radar data frequency, where N is a natural number greater than 1;

[0031] In the area where the current excavator rotation angle is located, N point cloud data are accumulated to achieve map update.

[0032] In some embodiments of the present disclosure, dividing the point cloud map into a plurality of areas includes:

[0033] The number of regions K is determined according to the rotation angle range of a region, where K is a natural number greater than 1;

[0034] Taking the rotation center of the excavator as the center, the circular point cloud map is divided into K sector areas.

[0035] In some embodiments of the present disclosure, determining the region index value of each region according to the excavator rotation angle includes:

[0036] For each area, a region index value of the area is determined according to the excavator rotation angle of the area and the rotation angle range of the area.

[0037] According to another aspect of the present disclosure, a domain controller is provided, including:

[0038] A map construction module is configured to construct a point cloud map of the excavator rotation operation scene based on the laser radar point cloud data and the turntable attitude sensor data;

[0039] A region division module is configured to divide the point cloud map into a plurality of regions;

[0040] An index value determination module is configured to determine a region index value of each region according to a rotation angle of the excavator;

[0041] The map update module is configured to update the point cloud map according to the area index value.

[0042] According to another aspect of the present disclosure, a domain controller is provided, including:

[0043] A memory for storing instructions;

[0044] The processor is used to execute the instructions so that the domain controller implements the point cloud map updating method as described in any of the above embodiments.

[0045] According to another aspect of the present disclosure, a point cloud map updating system is provided, comprising a laser radar, a turntable attitude sensor, and a domain controller as described in any of the above embodiments.

[0046] According to another aspect of the present disclosure, there is provided an excavator, comprising a point cloud map updating system as described in any one of the above embodiments.

[0047] According to another aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the point cloud map updating method as described in any of the above embodiments is implemented.

[0048] According to another aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the point cloud map updating method as described in any of the above embodiments is implemented.

[0049] The present invention can utilize the movement of the excavator's rotary operation to map and update the operation scene, establish a map index according to the rotation angle, and update the map according to the index, thereby improving the efficiency of map retrieval and update. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0051] Figure 1 Schematic diagram of some embodiments of the point cloud map updating system disclosed in the present invention.

[0052] Figure 2 Schematic diagram of some embodiments of the point cloud map updating method disclosed in the present invention.

[0053] Figure 3 A schematic diagram of a point cloud map data structure in some embodiments of the present disclosure.

[0054] Figure 4 Schematic diagram of a point cloud map update method in some embodiments of the present disclosure.

[0055] Figure 5 A schematic diagram of a point cloud map update process in some embodiments of the present disclosure.

[0056] Figure 6 The diagram is a schematic diagram of the structure of some embodiments of the domain controller disclosed in the present invention.

[0057] Figure 7 Schematic diagram of the structure of some other embodiments of the domain controller disclosed in the present invention. DETAILED DESCRIPTION

[0058] The technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings 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.

[0059] 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.

[0060] 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.

[0061] 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.

[0062] 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.

[0063] It should be noted that like reference numerals and letters refer to similar items in the following figures, and therefore, once an item is defined in one figure, it need not be further discussed in subsequent figures.

[0064] The inventors found through research that the local map update method based on laser radar in the related art does not specifically consider the scene characteristics of the typical operation of the excavator, that is, repeated movement in a small range and continuous change of the surrounding environment. For the typical working conditions of the excavator's rotary operation, the related art has the following shortcomings:

[0065] A local point cloud map update and maintenance method of a related technology needs to update the local point cloud map according to the first data information corresponding to each point cloud in the newly acquired frame and adopt the least recently used algorithm. The number of point clouds is relatively large and the efficiency is not optimal.

[0066] Another related technology provides a fast query and matching method for lidar point clouds based on local sensitive hashing. The pseudo-Hilbert curve sequence obtained is hash-encoded using LSH (local sensitive hashing), an index is constructed, and the Euclidean distance between the query point cloud and the candidate point cloud is used to confirm the point cloud with the closest distance in the voxel. The calculation is relatively complex and the resource usage is relatively high.

[0067] In view of at least one of the above technical problems, the present disclosure provides a point cloud map updating method and system, a domain controller and an excavator, and a storage medium. The present disclosure is described below through specific embodiments.

[0068] Figure 1 Schematic diagram of some embodiments of the point cloud map updating system disclosed in the present invention. Figure 1 As shown, the point cloud map updating system disclosed in the present invention may include a laser radar 1, a turntable attitude sensor 2 and a domain controller 3.

[0069] In some embodiments of the present disclosure, Figure 1 As shown, the laser radar 1 is installed on the top of the excavator cab with the installation direction facing forward, and is configured to select the installation angle of the radar according to the expected work task for collecting environmental point clouds.

[0070] In some embodiments of the present disclosure, Figure 1 As shown, the data of the laser radar is Pointcloud (point cloud data), and the data is linked to the domain controller 3 via the UDP network protocol link.

[0071] In some embodiments of the present disclosure, Figure 1 As shown, the turntable attitude sensor 2 is installed at the upper vehicle turntable and is configured to measure the turning angle, turning angular velocity, and pitch angle and roll angle of the turntable relative to the lower vehicle.

[0072] In some embodiments of the present disclosure, Figure 1 As shown, the data of the turntable attitude sensor 2 is rpy (Euler angle), namely roll angle (roll), pitch angle (pitch), yaw angle (yaw), and yaw rate. The data is linked to the domain controller through the CAN bus protocol.

[0073] In some embodiments of the present disclosure, Figure 1 As shown, the domain controller 3 is a computing device used for data collection and computing processing.

[0074] In some embodiments of the present disclosure, the domain controller 3 can be configured to construct a point cloud map of the excavator rotation operation scene based on the lidar point cloud data and the turntable attitude sensor data; divide the point cloud map into multiple areas; determine the area index value of each area according to the excavator rotation angle; and update the point cloud map according to the area index value.

[0075] In some embodiments of the present disclosure, the domain controller 3 can be configured to receive low-frequency (e.g., 10 Hz) lidar point cloud data (including frame start time, frame length, frame point cloud) and high-frequency (e.g., 100 Hz) turntable attitude sensor data (including time, rotation angle, pitch angle, roll angle, and rotation angular velocity), and extract the material point cloud behind the boom from the lidar on-vehicle coordinate system through a point cloud map updating method, and spatially transform it to the excavator off-vehicle coordinate system (assuming that the excavator is not moving, and the off-vehicle coordinate system is a stationary coordinate system). The resulting point cloud is automatically spliced ​​in the stationary coordinate system as the excavator moves, so as to construct a point cloud map of the excavator operation scene.

[0076] The above-mentioned embodiment of the present disclosure is based on a petal-type scanning laser radar, which utilizes the movement of the excavator's rotation operation to map and update the work scene, establishes a map index according to the rotation angle, constructs a point cloud map data format, and updates the map according to the index, thereby improving the efficiency of map retrieval and update.

[0077] Figure 2 Schematic diagram of some embodiments of the point cloud map updating method disclosed in the present invention. Preferably, this embodiment can be executed by the domain controller disclosed in the present invention. Figure 2 As shown, the point cloud map updating method disclosed in the present invention may include at least one step from step 10 to step 40.

[0078] Step 10: construct a point cloud map of the excavator rotation operation scene based on the lidar point cloud data and the turntable attitude sensor data.

[0079] In some embodiments of the present disclosure, the laser radar point cloud data is environmental point cloud data, and the laser radar point cloud data includes frame start time, frame length and frame point cloud data.

[0080] In some embodiments of the present disclosure, the turntable attitude sensor data may include a rotation angle, a rotation angular velocity, a pitch angle, and a roll angle.

[0081] In some embodiments of the present disclosure, step 10 may include at least one of steps 11 to 13.

[0082] Step 11: In the excavator rotation operation scene, receive the lidar point cloud data and the turntable attitude sensor data.

[0083] Step 12, after extracting the boom point cloud data in the laser radar on-board coordinate system, the material point cloud data is obtained, and the material point cloud data is converted to the excavator off-board coordinate system through spatial transformation.

[0084] In some embodiments of the present disclosure, step 12 may include at least one of steps 121 to 124 .

[0085] Step 121, time compensation is performed on the turntable attitude sensor data.

[0086] In some embodiments of the present disclosure, step 121 may include: performing a first time compensation on the turntable attitude sensor data; and / or performing a second time compensation on the turntable attitude sensor data.

[0087] In some embodiments of the present disclosure, the step of performing a first time compensation on the turntable attitude sensor data may include: setting the timestamp of the turntable attitude sensor data received at the same time as the first timestamp, and the timestamp of the lidar point cloud data as the second timestamp; determining the time deviation based on the difference between the second timestamp and the first timestamp; determining the turntable attitude sensor time based on the lidar point cloud data time and the time deviation.

[0088] In some embodiments of the present disclosure, the step of determining the turntable attitude sensor time based on the laser radar point cloud data time and time deviation may include: judging whether the time deviation is greater than a first predetermined time; when the time deviation is greater than the first predetermined time, determining the turntable attitude sensor time based on the laser radar point cloud data time and time deviation, and performing a first time compensation.

[0089] In some embodiments of the present disclosure, the step of performing a second time compensation on the turntable attitude sensor data may include: when the lidar point cloud data is transformed into the excavator off-vehicle coordinate system through spatial synchronization, determining whether a ghost image appears on the same object when rotating left or right; in the event of a ghost image, performing a second time compensation on the turntable attitude sensor data based on the rotation angular velocity, ghost width and ghost center radius.

[0090] In some embodiments of the present disclosure, the step of performing a second time compensation on the turntable attitude sensor data based on the rotation angular velocity, ghost width and ghost center radius may include: performing a second time compensation on the turntable attitude sensor data based on the rotation angular velocity, ghost width and ghost center radius in a manual or automatic iterative manner; determining whether the ghost width is less than a predetermined error; and exiting the iteration if the ghost width is less than the predetermined error.

[0091] Step 122, performing point cloud preprocessing and frame point cloud splitting on the laser radar point cloud data.

[0092] In some embodiments of the present disclosure, the step of performing point cloud preprocessing on the laser radar point cloud data may include: performing validity filtering on the laser radar point cloud data to remove non-numerical data; performing range filtering according to the operating radius of the excavator to retain the point cloud data within the operating radius; converting the laser radar point cloud data to the excavator vehicle coordinate system according to the installation external parameters of the laser radar, wherein the installation external parameters are the translation and rotation of the laser radar coordinate system relative to the vehicle coordinate system; extracting the boom point cloud data according to the range of the boom relative to the first direction of the vehicle coordinate system, and retaining the material point cloud data.

[0093] In some embodiments of the present disclosure, the step of splitting the laser radar point cloud data into frames may include: determining the number of splits n according to the ratio of the attitude sensor data frequency to the point cloud data frequency, where n is a natural number greater than 1; and splitting a single frame point cloud into n parts.

[0094] In some embodiments of the present disclosure, the step of performing frame point cloud splitting on the lidar point cloud data may also include: setting the timestamp of the first frame after splitting; determining the timestamps of each frame after splitting according to the timestamp of the first frame after splitting, the frequency of the point cloud data, the frame sequence number after splitting, and the number of splits.

[0095] Step 123, time synchronization is performed on the turntable attitude sensor data after time compensation and the laser radar point cloud data after frame point cloud segmentation.

[0096] In some embodiments of the present disclosure, step 123 may include: passing the turntable attitude sensor data after time compensation from the end of the attitude sensor data buffer into the attitude sensor data buffer; passing the lidar point cloud data after frame point cloud splitting from the end of the lidar data buffer into the lidar data buffer; taking out the attitude sensor data frame from the head of the attitude sensor data buffer, and taking out the lidar point cloud data frame from the head of the lidar data buffer, and performing time comparison.

[0097] In some embodiments of the present disclosure, the step of taking out the attitude sensor data frame from the head of the attitude sensor data buffer, taking out the lidar point cloud data frame from the head of the lidar data buffer, and performing time comparison may include: determining the time error between the taken lidar point cloud data frame and the attitude sensor data frame; and finding the attitude sensor data frame and the lidar data frame whose time error is within a second predetermined time range.

[0098] Step 124, performing speed compensation and spatial synchronization on the time-synchronized laser radar point cloud data and turntable attitude sensor data.

[0099] In some embodiments of the present disclosure, the step of performing speed compensation on the time-synchronized lidar point cloud data and turntable attitude sensor data may include: compensating the rotation angle according to the rotation angular velocity of the turntable attitude sensor and the time error.

[0100] In some embodiments of the present disclosure, the step of spatially synchronizing the time-synchronized lidar point cloud data and the turntable attitude sensor data may include: constructing a transformation matrix of the excavator's on-board coordinate system relative to the excavator's off-board coordinate system based on the rotation angle of the turntable attitude sensor after speed compensation and the pitch angle and roll angle of the attitude sensor after time synchronization; determining the lidar point cloud data in the excavator's off-board coordinate system based on the transformation matrix and the lidar point cloud data in the excavator's on-board coordinate system.

[0101] Step 13: construct a point cloud map of the excavator rotation operation scene using the movement of the excavator rotation operation.

[0102] In some embodiments of the present disclosure, step 13 may include: automatically splicing the generated material point cloud data in a stationary excavator off-vehicle coordinate system as the excavator moves, to construct a point cloud map of the excavator rotation operation scene.

[0103] In some embodiments of the present disclosure, step 13 may include: superimposing and accumulating each frame of laser radar point cloud data in the coordinate system of the excavator getting off the vehicle, and saving it as a point cloud map in a point cloud data format.

[0104] Step 20: divide the point cloud map into multiple areas.

[0105] In some embodiments of the present disclosure, step 20 may include at least one of steps 21 to 22.

[0106] Step 21, determining the number of regions K according to the rotation angle range of a region (parameter ParUintYaw), where K is a natural number greater than 1.

[0107] In some embodiments of the present disclosure, step 21 may include: determining the number of regions K according to 360 degrees and a rotation angle range of one region.

[0108] In some embodiments of the present disclosure, step 21 may include: determining the number of regions K according to formula (1).

[0109] K = round(360 / ParUintYaw) (1)

[0110] In formula (1), ParUintYaw is the rotation angle range of a region, and round() is the rounding function.

[0111] Step 22, taking the rotation center of the excavator as the center of the circle, divide the circular point cloud map into K sector areas.

[0112] Figure 3 Schematic diagram of the point cloud map data structure in some embodiments of the present disclosure. Figure 3 As shown in the figure, in order to efficiently retrieve and update the partial area of ​​the point cloud map, the point cloud map of the entire rotation operation is divided into K parts, and the point cloud of each area corresponds to an index, and the point cloud map in PointCloudMap format is constructed accordingly. The point cloud map in this format consists of two parts: index and point cloud data. There are K indexes from Index1 to IndexK, and K is determined by formula (1) according to the parameter ParUintYaw. The point cloud data part does not exceed N point cloud data (Pointcloud).

[0113] The above-mentioned embodiments of the present disclosure associate the point cloud map with the area through an index, so that the point cloud map can be retrieved, cleared and updated according to the index.

[0114] Step 30, determining the region index value of each region according to the rotation angle of the excavator.

[0115] In some embodiments of the present disclosure, step 30 may include: for each area, determining an area index value of the area according to the excavator rotation angle of the area and the rotation angle range of the area.

[0116] In some embodiments of the present disclosure, step 30 may include: for each area, according to formula (2), according to the excavator rotation angle yaw of the area and the rotation angle range (ParUintYaw) of a region, determining the area index value Index of the area.

[0117] Index=round(yaw / ParUintYaw) (2)

[0118] Step 40: Update the point cloud map according to the area index value.

[0119] In some embodiments of the present disclosure, step 40 may include at least one of steps 41 to 43 .

[0120] Step 41: receiving a map update instruction, wherein the map update instruction includes an angle range of an area to be updated.

[0121] In some embodiments of the present disclosure, the area to be updated may include at least one of a loading area and an unloading area.

[0122] Step 42: determining an index interval of the area to be updated according to the angle range of the area to be updated.

[0123] Step 43: Update the to-be-updated area of ​​the point cloud map according to the index interval.

[0124] In some embodiments of the present disclosure, when the area to be updated includes the loading area, step 43 may include: clearing point cloud data of the loading area according to the index interval; and updating the loading area according to the index interval.

[0125] In some embodiments of the present disclosure, when the area to be updated includes the unloading area, step 43 may include: clearing point cloud data of the unloading area according to the index interval; and updating the unloading area according to the index interval.

[0126] In some embodiments of the present disclosure, step 40 may include: step 44, periodically updating the map of the area where the current excavator rotation angle is located at a predetermined time interval.

[0127] Figure 4 Schematic diagram of the point cloud map update method in some embodiments of the present disclosure. Figure 4 As shown, the point cloud map created by the map construction module needs to be sent to subsequent modules (such as the planning control module) for use as a client. In order to improve the sending efficiency, two map update modes, map service svr and periodic sending send, are established.

[0128] In some embodiments of the present disclosure, Figure 4 As shown in the figure, for the global map, since its data volume is relatively large and it does not need to be updated in real time, the global map is updated according to the client's request; for the loading area and unloading area, a map update service is built so that the map can be updated according to the planning control client's request. That is, for a large amount of data, it is updated on demand and at a low frequency.

[0129] In other embodiments of the present disclosure, Figure 4 As shown, the map of the area where the current excavator rotation angle is located is updated periodically so that the planning control module can obtain the current working area status in real time. That is, for a small amount of data, it is updated periodically and frequently.

[0130] In some embodiments of the present disclosure, step 44 may include at least one of steps 441 to 442 .

[0131] Step 441, determining the area index value of the area where the current excavator rotation angle is located according to the current excavator rotation angle.

[0132] In some embodiments of the present disclosure, step 441 may include: determining a region index value of a region where the current excavator rotation angle is located according to the current excavator rotation angle and a rotation angle range of a region.

[0133] In some embodiments of the present disclosure, step 441 may include: the current index Index is determined by the current excavator rotation angle yaw according to the parameter ParUintYaw according to formula (2).

[0134] Step 442, updating the map of the area where the current excavator rotation angle is located according to the area index value of the area where the current excavator rotation angle is located.

[0135] In some embodiments of the present disclosure, step 442 may include: determining a cumulative number N based on the rotation angle frequency and the lidar data frequency, where N is a natural number greater than 1; accumulating N point cloud data in the area where the current excavator rotation angle is located to achieve map update.

[0136] In some embodiments of the present disclosure, the cumulative number N is determined by formula (3) based on the rotation angle frequency Yaw Hz and the lidar data frequency PointCloud Hz.

[0137] N=round(YawHz / PointCloudHz) (3)

[0138] In some embodiments of the present disclosure, the step of accumulating N point cloud data in the area where the current excavator rotation angle is located to achieve map update may include adopting a periodic update method, calculating the map index according to the current excavator rotation angle yaw, accumulating N Pointclouds, and updating the PointcloudMap point cloud map according to the index using formula (4).

[0139]

[0140] The above-mentioned embodiment of the present disclosure accumulates the point cloud within a single index according to the rotation angle and the laser radar transmission frequency, thereby improving the map resolution.

[0141] The above-mentioned embodiments of the present disclosure can establish a map index according to the rotation angle, construct a PointcloudMap point cloud map data format, and update the map according to the index, thereby improving the efficiency of map retrieval and update.

[0142] Figure 5 Schematic diagram of the point cloud map update process in some embodiments of the present disclosure. Figure 5As shown, the point cloud map update process of the present disclosure is divided into three branches: loading area map update, unloading area map update and real-time update.

[0143] Loading area map update: When the client requests a loading area update command, it will also send the angle range of the loading area. When the map server receives the map update command, it calculates the index interval of the map based on the angle range, and then clears the Pointcloud of the loading area based on the Index interval; finally, it updates the Pointcloud of the loading area based on the Index interval.

[0144] Unloading area map update: When the client requests the unloading area update command, it will also send the angle range of the unloading area. When the map server receives the map update command, it calculates the index interval of the map based on the angle range, and then clears the Pointcloud of the unloading area based on the Index interval; finally, it updates the Pointcloud of the unloading area based on the Index interval.

[0145] When the map server does not receive a map update command, a periodic update method is used to calculate the map index based on the current excavator rotation angle yaw, accumulate N point cloud data (Pointcloud), and update the PointcloudMap point cloud map based on the index using formula (4).

[0146] The above-mentioned embodiments of the present disclosure adopt map services and real-time transmission methods, thereby reducing communication delays and improving system real-time performance.

[0147] Figure 6 FIG. 1 is a schematic diagram of the structure of some embodiments of the domain controller disclosed in the present invention. Figure 6 As shown, the domain controller of the present disclosure may include a map construction module 61 , a region division module 62 , an index value determination module 63 and a map update module 64 .

[0148] The map construction module 61 is configured to construct a point cloud map of the excavator rotation operation scene based on the laser radar point cloud data and the turntable attitude sensor data.

[0149] The area division module 62 is configured to divide the point cloud map into multiple areas.

[0150] In some embodiments of the present disclosure, the area division module 62 can be configured to determine the number of areas K according to the rotation angle range of an area, where K is a natural number greater than 1; and divide the circular point cloud map into K sector-shaped areas with the rotation center of the excavator as the center of the circle.

[0151] The index value determination module 63 is configured to determine the region index value of each region according to the rotation angle of the excavator.

[0152] In some embodiments of the present disclosure, the index value determination module 63 may be configured to determine, for each region, a region index value of the region according to the excavator rotation angle of the region and a rotation angle range of the region.

[0153] The map updating module 64 is configured to update the point cloud map according to the area index value.

[0154] In some embodiments of the present disclosure, the map update module 64 may be configured to receive a map update instruction, wherein the map update instruction includes an angle range of the area to be updated; determine an index interval of the area to be updated based on the angle range of the area to be updated; and update the area to be updated of the point cloud map based on the index interval.

[0155] In some embodiments of the present disclosure, the area to be updated includes at least one of a loading area and an unloading area.

[0156] In some embodiments of the present disclosure, the map update module 64, when the area to be updated includes the loading area and the area to be updated of the point cloud map is updated according to the index interval, can be configured to clear the point cloud data of the loading area according to the index interval; and update the loading area according to the index interval.

[0157] In some embodiments of the present disclosure, the map update module 64, when the area to be updated includes the unloading area and the area to be updated of the point cloud map is updated according to the index interval, can be configured to clear the point cloud data of the unloading area according to the index interval; and update the unloading area according to the index interval.

[0158] In some embodiments of the present disclosure, the map updating module 64 may also be configured to update the map of the area where the current excavator rotation angle is located at a predetermined time interval.

[0159] In some embodiments of the present disclosure, the map update module 64, when updating the map of the area where the current excavator rotation angle is located, can be configured to determine the area index value of the area where the current excavator rotation angle is located according to the current excavator rotation angle; and update the map of the area where the current excavator rotation angle is located according to the area index value of the area where the current excavator rotation angle is located.

[0160] In some embodiments of the present disclosure, the map update module 64, when determining the regional index value of the area where the current excavator rotation angle is located based on the current excavator rotation angle, can be configured to determine the regional index value of the area where the current excavator rotation angle is located based on the current excavator rotation angle and the rotation angle range of an area.

[0161] In some embodiments of the present disclosure, the map update module 64, when updating the map of the area where the current excavator rotation angle is located according to the area index value of the area where the current excavator rotation angle is located, can be configured to determine the cumulative number N based on the rotation angle frequency and the lidar data frequency, where N is a natural number greater than 1; and accumulate N point cloud data in the area where the current excavator rotation angle is located to achieve map update.

[0162] In some embodiments of the present disclosure, the domain controller of the present disclosure may be configured to execute the method described in any of the above embodiments.

[0163] Figure 7 FIG. 1 is a schematic diagram of the structure of some other embodiments of the domain controller disclosed in the present invention. Figure 7 As shown, the domain controller disclosed in the present invention includes a memory 71 and a processor 72 .

[0164] The memory 71 is used to store instructions, the processor 72 is coupled to the memory 71, and the processor 72 is configured to execute the point cloud map updating method as described in any of the above embodiments based on the instructions stored in the memory.

[0165] like Figure 7 As shown, the domain controller also includes a communication interface 73 for information exchange with other devices. At the same time, the domain controller also includes a bus 74, through which the processor 72, the communication interface 73, and the memory 71 communicate with each other.

[0166] The memory 71 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory. The memory 71 may also be a memory array. The memory 71 may also be divided into blocks, and the blocks may be combined into virtual volumes according to certain rules.

[0167] In addition, the processor 72 may be a central processing unit CPU, or may be an application specific integrated circuit ASIC, or may be configured to implement one or more integrated circuits of the embodiments of the present disclosure.

[0168] Figure 1 Also provided are schematic diagrams of some embodiments of the excavator disclosed in the present invention. Figure 1As shown, the excavator disclosed in the present invention may include a point cloud map updating system as described in any of the above embodiments.

[0169] The above-mentioned embodiment of the present disclosure is based on a petal-type scanning laser radar, and uses the movement of the excavator's rotation operation to map and update the working scene, establishes a map index according to the rotation angle, constructs a PointcloudMap point cloud map data format, and updates the map according to the index, thereby improving the efficiency of map retrieval and update; the above-mentioned embodiment of the present disclosure accumulates the point cloud within a single index according to the rotation angle and the laser radar transmission frequency, thereby improving the map resolution; the above-mentioned embodiment of the present disclosure adopts map services and real-time transmission methods to reduce communication delays and improve the real-time performance of the system.

[0170] According to one aspect of the present disclosure, a computer program product is provided, including a computer program, wherein when the computer program is executed by a processor, the point cloud map updating method as described in any of the above embodiments is implemented.

[0171] According to one aspect of the present disclosure, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the point cloud map updating method as described in any of the above embodiments is implemented.

[0172] In some embodiments of the present disclosure, the computer-readable storage medium may be a non-transitory computer-readable storage medium.

[0173] The above embodiments of the present disclosure provide a point cloud map updating method and system, which can update a three-dimensional point cloud map in real time and have the following advantages:

[0174] 1. The above embodiment of the present disclosure establishes a map index according to the rotation angle, constructs the PointcloudMap point cloud map data format, updates the map according to the index, and improves the efficiency of map retrieval and update;

[0175] 2. The above-mentioned embodiment of the present disclosure accumulates the point cloud in a single index according to the rotation angle and the laser radar transmission frequency, thereby improving the map resolution;

[0176] 3. The above-mentioned embodiments of the present disclosure adopt map services and real-time transmission methods to reduce communication delays and improve system real-time performance.

[0177] Those skilled in the art will appreciate that the embodiments of the present disclosure may be provided as methods, devices, or computer program products. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable non-transient storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0178] The present disclosure is described with reference to flowcharts and / or block diagrams of methods, devices (systems) and computer program products according to embodiments of the present disclosure. It should be understood that each process and / or block in the flowchart and / or block diagram and the combination of processes and / or blocks in the flowchart and / or block diagram can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0179] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0180] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0181] The domain controller, map construction module, region division module, index value determination module and map update module described above can be implemented as a general-purpose processor, a programmable logic controller (PLC), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component or any appropriate combination thereof for performing the functions described in this application.

[0182] So far, the present disclosure has been described in detail. In order to avoid obscuring the concept of the present disclosure, some details known in the art are not described. Based on the above description, those skilled in the art can fully understand how to implement the technical solution disclosed here.

[0183] A person skilled in the art will understand that all or part of the steps to implement the above embodiments may be accomplished by hardware, or may be accomplished by instructing the relevant hardware through a program, and the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a disk or an optical disk, etc.

[0184] The description of the present disclosure is given for the purpose of illustration and description, and is not intended to be exhaustive or to limit the present disclosure to the disclosed form. Many modifications and variations will be apparent to those of ordinary skill in the art. The embodiments are selected and described in order to better illustrate the principles and practical applications of the present disclosure, and to enable those of ordinary skill in the art to understand the present disclosure and thereby design various embodiments with various modifications suitable for specific uses.

Claims

1. A point cloud map updating method, comprising: Construct a point cloud map of the excavator's rotary operation scene based on the lidar point cloud data and turntable attitude sensor data; Dividing the point cloud map into a plurality of regions; Determine the area index value of each area according to the rotation angle of the excavator; The point cloud map is updated according to the area index value.

2. The point cloud map updating method according to claim 1, wherein: The updating of the point cloud map according to the area index value comprises: receiving a map update instruction, wherein the map update instruction includes an angle range of an area to be updated; Determine the index interval of the area to be updated according to the angle range of the area to be updated; The to-be-updated area of ​​the point cloud map is updated according to the index interval.

3. The point cloud map updating method according to claim 2, wherein: The area to be updated includes at least one of a loading area and an unloading area.

4. The point cloud map updating method according to claim 3, wherein: In a case where the area to be updated includes the loading area, updating the area to be updated of the point cloud map according to the index interval includes: Clear the point cloud data of the loading area according to the index interval; The loading area is updated according to the index interval.

5. The point cloud map updating method according to claim 3, wherein: In a case where the area to be updated includes the unloading area, updating the area to be updated of the point cloud map according to the index interval includes: clearing the point cloud data of the unloading area according to the index interval; The unloading area is updated according to the index interval.

6. The point cloud map updating method according to any one of claims 1 to 5, wherein: The updating of the point cloud map according to the area index value comprises: The map of the area where the current excavator rotation angle is located is updated at a predetermined time interval.

7. The point cloud map updating method according to claim 6, wherein: The updating of the map of the area where the current excavator rotation angle is located includes: Determine the region index value of the region where the current excavator rotation angle is located according to the current excavator rotation angle; According to the area index value of the area where the current excavator rotation angle is located, the map of the area where the current excavator rotation angle is located is updated.

8. The point cloud map updating method according to claim 7, wherein: The step of determining the region index value of the region where the current excavator rotation angle is located according to the current excavator rotation angle comprises: According to the current excavator rotation angle and the rotation angle range of a region, a region index value of the region where the current excavator rotation angle is located is determined.

9. The point cloud map updating method according to claim 7, wherein: The updating of the map of the area where the current excavator rotation angle is located according to the area index value of the area where the current excavator rotation angle is located comprises: Determine the cumulative number N according to the rotation angle frequency and the laser radar data frequency, where N is a natural number greater than 1; In the area where the current excavator rotation angle is located, N point cloud data are accumulated to achieve map update.

10. The point cloud map updating method according to any one of claims 1 to 5, wherein: The dividing the point cloud map into a plurality of areas comprises: The number of regions K is determined according to the rotation angle range of a region, where K is a natural number greater than 1; Taking the rotation center of the excavator as the center, the circular point cloud map is divided into K sector areas.

11. The point cloud map updating method according to any one of claims 1 to 5, wherein: Determining the region index value of each region according to the excavator rotation angle includes: For each area, a region index value of the area is determined according to the excavator rotation angle of the area and the rotation angle range of the area.

12. A domain controller, comprising: A map construction module is configured to construct a point cloud map of the excavator rotation operation scene based on the laser radar point cloud data and the turntable attitude sensor data; A region division module is configured to divide the point cloud map into a plurality of regions; An index value determination module is configured to determine a region index value of each region according to a rotation angle of the excavator; The map update module is configured to update the point cloud map according to the area index value.

13. A domain controller, comprising: A memory for storing instructions; A processor, configured to execute the instruction so that the domain controller implements the point cloud map updating method as described in any one of claims 1-11.

14. A point cloud map updating system, comprising a laser radar, a turntable attitude sensor and a domain controller as claimed in claim 12 or 13.

15. An excavator, comprising the point cloud map updating system according to claim 14.

16. A computer-readable storage medium, wherein: The computer-readable storage medium stores computer instructions, and when the instructions are executed by a processor, the point cloud map updating method according to any one of claims 1 to 11 is implemented.

17. A computer program product comprising a computer program, wherein: When the computer program is executed by a processor, the point cloud map updating method according to any one of claims 1 to 11 is implemented.

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