CN114280625A - Unmanned aerial vehicle-based three-dimensional laser radar underground map construction method and device - Google Patents

Abstract

The application provides a three-dimensional laser radar underground map construction method and device based on an unmanned aerial vehicle, wherein the method comprises the following steps: controlling a laser radar of an unmanned aerial vehicle to scan an underground operation area so as to obtain a scanning point cloud data set of the underground operation area; acquiring a measurement and calculation point cloud data set of the underground operation area according to the relative position of the unmanned aerial vehicle and a positioning base station of the underground operation area; and generating a target three-dimensional map of the underground operation area according to the scanning point cloud data set and the measuring and calculating point cloud data set. According to the method and the device, the target three-dimensional map is constructed based on the point cloud data set, the integrity of the target three-dimensional map is optimized, the accuracy of construction of the target three-dimensional map is improved, the unmanned aerial vehicle can realize high-precision real-time positioning based on the target three-dimensional map, and the positioning effect of the unmanned aerial vehicle is optimized.

Description

Unmanned aerial vehicle-based three-dimensional laser radar underground map construction method and device

Technical Field

The application relates to the field of image processing, in particular to a three-dimensional laser radar underground map building method and device based on an unmanned aerial vehicle.

Background

With the development of society, people's requirement to underground operation is increasing more and more, and people rely on unmanned aerial vehicle to carry out underground operation more and more.

In the correlation technique, the positioning of the unmanned aerial vehicle for underground operation is not accurate, and the accuracy is low. Therefore, how to realize the high accuracy location of unmanned aerial vehicle in the pit is the problem that needs to solve at present.

Disclosure of Invention

The present application is directed to solving, to some extent, one of the technical problems in the related art. Therefore, an object of the present application is to provide a three-dimensional lidar underground map construction method and apparatus based on an unmanned aerial vehicle, so as to solve the problems of low positioning accuracy and poor real-time performance of the unmanned aerial vehicle during underground operation to a certain extent. The technical scheme of the application is as follows:

the application provides a three-dimensional laser radar underground map construction method based on an unmanned aerial vehicle in a first aspect, and the method comprises the following steps: controlling a laser radar of an unmanned aerial vehicle to scan an underground operation area so as to obtain a scanning point cloud data set of the underground operation area; acquiring a measurement point cloud data set of the underground operation area according to the relative position of the unmanned aerial vehicle and a positioning base station of the underground operation area; and generating a target three-dimensional map of the underground operation area according to the scanning point cloud data set and the measuring point cloud data set.

The three-dimensional laser radar underground map building method based on the unmanned aerial vehicle, provided by the first aspect of the application, further has the following additional characteristics:

according to an embodiment of the application, control unmanned aerial vehicle's laser radar scanning borehole operation district, in order to obtain the scanning point cloud data set in borehole operation district, include: acquiring a scanning point set of the laser radar, and determining the curvature of each scanning point in the scanning point set; and according to the curvature of each scanning point, effective characteristic points are extracted from the scanning point set, and the scanning point cloud data set of the underground operation area is generated based on the point cloud data of the effective characteristic points.

According to an embodiment of the application, the extracting effective feature points from the scanning point set according to the curvature of each scanning point includes: and determining edge points and plane points from the scanning point set based on the curvature of the scanning points as the effective characteristic points.

According to one embodiment of the application, the generating the scanning point cloud data set of the downhole operation area based on the point cloud data of the effective feature points comprises: acquiring a scanning frame acquired by the laser radar; determining effective characteristic points matched with the previous scanning frame in the scanning frames based on the curvatures of the effective characteristic points, wherein the effective characteristic points are used as target characteristic points of the scanning frames; determining point cloud data of the target feature points, and determining the point cloud data of the effective feature points of the scanning frame according to the relative positions of the target feature points and the residual effective feature points; and generating the scanning point cloud data set of the underground operation area based on the point cloud data of the effective characteristic points of each scanning frame.

According to an embodiment of the present application, before determining, as the target feature point of the scan frame, a valid feature point in the scan frame that matches a previous scan frame based on a curvature of the valid feature point, the method further includes: judging whether the scanning frame is a distorted frame; and responding to the scanning frame as the distorted frame, and compensating the distorted frame based on the acquired data corresponding to the inertial measurement unit to obtain the compensated scanning frame.

According to an embodiment of the present application, the compensating the distorted frame based on the collected data corresponding to the inertial measurement unit to obtain a compensated scan frame includes: determining a distortion timestamp for the distorted frame; acquiring the acquired data of the inertial measurement unit at the distortion time stamp; performing pose resolving on the acquired data of the distorted timestamp to generate a compensation attitude track corresponding to the distorted frame; and generating the compensated scanning frame corresponding to the distorted frame according to the effective characteristic point of the last scanning frame corresponding to the distorted frame and the compensation posture track.

According to an embodiment of the present application, the generating the compensated scan frame corresponding to the distorted frame according to the valid feature point of the previous scan frame corresponding to the distorted frame and the compensated pose trajectory includes: controlling the effective characteristic point of the last scanning frame to move along the compensation posture track, and acquiring a target compensation point formed after the effective characteristic point moves; determining a target distortion point corresponding to the target compensation point on the distortion frame according to the compensation posture track, and determining a residual compensation point corresponding to the residual distortion point according to the relative position of the residual distortion point and the target distortion point; and generating the compensated scanning frame corresponding to the distorted frame based on the target compensation point and the residual compensation point.

According to an embodiment of the application, the generating a target three-dimensional map of the downhole working area according to the scanning coordinate set and the calculating coordinate set comprises: acquiring a point cloud data union set of the scanning point cloud data set and the measuring point cloud data set; and generating the target three-dimensional map of the underground operation area based on the point cloud data union.

According to an embodiment of the application, the method further comprises: acquiring a first operation coordinate set of the unmanned aerial vehicle according to the scanning point cloud data set; acquiring a second operation coordinate set of the unmanned aerial vehicle according to the relative position of the unmanned aerial vehicle and the positioning base station; and determining the operation position of the unmanned aerial vehicle in the underground operation area according to the first operation coordinate set and the second operation coordinate set.

This application second aspect provides a three-dimensional laser radar map device under well based on unmanned aerial vehicle, includes: the scanning module is used for controlling a laser radar of the unmanned aerial vehicle to scan the underground operation area so as to obtain a scanning point cloud data set of the underground operation area; the measuring and calculating module is used for acquiring a measuring and calculating point cloud data set of the underground operation area according to the relative position of the unmanned aerial vehicle and the positioning base station of the underground operation area; and the generating module is used for generating a target three-dimensional map of the underground operation area according to the scanning point cloud data set and the measuring point cloud data set.

Wherein, the three-dimensional lidar is map construction device in pit based on unmanned aerial vehicle that this application second aspect provided still has following additional characteristic:

according to an embodiment of the present application, the scanning module is further configured to: acquiring a scanning point set of the laser radar, and determining the curvature of each scanning point in the scanning point set; and according to the curvature of each scanning point, effective characteristic points are extracted from the scanning point set, and the scanning point cloud data set of the underground operation area is generated based on the point cloud data of the effective characteristic points.

According to an embodiment of the present application, the scanning module is further configured to: and determining edge points and plane points from the scanning point set based on the curvature of the scanning points as the effective characteristic points.

According to an embodiment of the present application, the base scan module is further configured to: acquiring a scanning frame acquired by the laser radar; determining effective characteristic points matched with the previous scanning frame in the scanning frames based on the curvatures of the effective characteristic points, wherein the effective characteristic points are used as target characteristic points of the scanning frames; determining point cloud data of the target feature points, and determining the point cloud data of the effective feature points of the scanning frame according to the relative positions of the target feature points and the residual effective feature points; and generating the scanning point cloud data set of the underground operation area based on the point cloud data of the effective characteristic points of each scanning frame.

According to an embodiment of the present application, the scanning module is further configured to: judging whether the scanning frame is a distorted frame; and responding to the scanning frame as the distorted frame, and compensating the distorted frame based on the acquired data corresponding to the inertial measurement unit to obtain the compensated scanning frame.

According to an embodiment of the present application, the scanning module is further configured to: determining a distortion timestamp for the distorted frame; acquiring the acquired data of the inertial measurement unit at the distortion time stamp; performing pose resolving on the acquired data of the distorted timestamp to generate a compensation attitude track corresponding to the distorted frame; and generating the compensated scanning frame corresponding to the distorted frame according to the effective characteristic point of the last scanning frame corresponding to the distorted frame and the compensation posture track. According to an embodiment of the present application, the scanning module is further configured to: controlling the effective characteristic point of the last scanning frame to move along the compensation posture track, and acquiring a target compensation point formed after the effective characteristic point moves; determining a target distortion point corresponding to the target compensation point on the distortion frame according to the compensation posture track, and determining a residual compensation point corresponding to the residual distortion point according to the relative position of the residual distortion point and the target distortion point; and generating the compensated scanning frame corresponding to the distorted frame based on the target compensation point and the residual compensation point.

According to an embodiment of the present application, the generating module is further configured to: acquiring a point cloud data union set of the scanning point cloud data set and the measuring point cloud data set; and generating the target three-dimensional map of the underground operation area based on the point cloud data union.

According to an embodiment of the application, the apparatus further comprises a positioning module for: acquiring a first operation coordinate set of the unmanned aerial vehicle according to the scanning point cloud data set; acquiring a second operation coordinate set of the unmanned aerial vehicle according to the relative position of the unmanned aerial vehicle and the positioning base station; and determining the operation position of the unmanned aerial vehicle in the underground operation area according to the first operation coordinate set and the second operation coordinate set.

A third aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method for building a three-dimensional lidar downhole map based on a drone according to any of the first aspect.

According to the three-dimensional laser radar underground map building method and device based on the unmanned aerial vehicle, the unmanned aerial vehicle laser radar is controlled to scan an underground operation area, and a corresponding scanning point cloud data set is obtained. Meanwhile, the relative position between a point to be positioned and the base station in the underground operation area is obtained through the UWB technology, the point cloud data of each point to be positioned is obtained through calculation according to the point cloud data and the relative position of the base station, a measuring and calculating point cloud data set corresponding to the underground operation area is further obtained, and a target three-dimensional map of the underground operation area is generated based on the scanning point cloud data set and the measuring and calculating point cloud data set. According to the method and the device, the target three-dimensional map is constructed based on the point cloud data set, the integrity of the target three-dimensional map is optimized, the accuracy of construction of the target three-dimensional map is improved, the unmanned aerial vehicle can realize high-precision real-time positioning based on the target three-dimensional map, and the positioning effect of the unmanned aerial vehicle is optimized.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.

Drawings

The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the application and are not to be construed as limiting the application.

Fig. 1 is a schematic flow chart of a three-dimensional lidar underground map construction method based on an unmanned aerial vehicle according to an embodiment of the application;

FIG. 2 is a schematic flow chart of a three-dimensional lidar underground map construction method based on an unmanned aerial vehicle according to another embodiment of the application;

FIG. 3 is a schematic flow chart of a three-dimensional lidar underground map construction method based on an unmanned aerial vehicle according to another embodiment of the application;

FIG. 4 is a schematic flow chart of a three-dimensional lidar underground map construction method based on an unmanned aerial vehicle according to another embodiment of the application;

FIG. 5 is a schematic flow chart of a three-dimensional lidar underground map construction method based on an unmanned aerial vehicle according to another embodiment of the application;

FIG. 6 is a schematic flow chart of a three-dimensional lidar underground map construction method based on an unmanned aerial vehicle according to another embodiment of the application;

fig. 7 is a schematic structural diagram of a three-dimensional lidar underground map construction device based on an unmanned aerial vehicle according to an embodiment of the application;

fig. 8 is a schematic structural diagram of a three-dimensional lidar underground map building device based on an unmanned aerial vehicle according to another embodiment of the present application.

Detailed Description

In order to make the technical solutions of the present application better understood by those of ordinary skill in the art, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functionality throughout. The embodiments described below with reference to the drawings are exemplary and intended to be used for explaining the present application and should not be construed as limiting the present application.

Fig. 1 is a schematic flow chart of a three-dimensional lidar underground map construction method based on an unmanned aerial vehicle according to an embodiment of the present application, and as shown in fig. 1, the method includes:

s101, controlling a laser radar of the unmanned aerial vehicle to scan the underground operation area so as to obtain a scanning point cloud data set of the underground operation area.

In the realization, can carry out the operation in the operation district in the pit through controlling unmanned aerial vehicle, consequently, need realize the accurate positioning of unmanned aerial vehicle in the operation district in the pit.

In order to realize accurate positioning of the unmanned aerial vehicle, a three-dimensional map can be constructed for the underground operation area based on the unmanned aerial vehicle, so that the unmanned aerial vehicle can realize accurate positioning through the constructed three-dimensional map in the subsequent operation process.

In the embodiment of the application, the area of the underground operation area can be coordinated, and the corresponding point cloud data set is constructed by acquiring the point cloud data of each position in the underground operation area, so that the construction of the three-dimensional map is realized.

Further, the laser radar of the unmanned aerial vehicle can be controlled to scan the underground operation area.

By controlling the unmanned aerial vehicle to move in the underground operation area, the laser radar device configured on the unmanned aerial vehicle can scan the underground operation area, so that a coordinate set of the underground operation area scanned by the laser radar is obtained, wherein the coordinate set can be a point cloud data set constructed based on a world coordinate system.

Alternatively, scanning acquisition of the point cloud dataset of the downhole operating area may be achieved by a 128-line lidar.

S102, obtaining a measurement point cloud data set of the underground operation area according to the relative position of the unmanned aerial vehicle and a positioning base station of the underground operation area.

In the realization, when the laser radar of unmanned aerial vehicle configuration is scanned the operation district in the pit, there is the scanning blind area that probably appears, and wherein, the scanning blind area can't obtain the point cloud data of wherein every position through laser radar.

Furthermore, the point cloud data set corresponding to the underground operation area can be obtained in other modes while the laser radar scans the underground operation area.

Optionally, the related distance measurement and the calculation may be performed by an Ultra Wide Band (UWB) technology, and a point cloud data set corresponding to the downhole operation area is obtained according to a result of the calculation.

The system comprises an unmanned aerial vehicle, a plurality of base stations and a point cloud data set, wherein the base stations with known coordinate positions can be configured in the underground operation area, correlation calculation is carried out based on the relative positions between the positioning tags configured on the unmanned aerial vehicle and the base stations, and the point cloud data set of the underground operation area is obtained according to the calculation result.

The method includes the steps that the unmanned aerial vehicle acquires corresponding points to be located of the underground operation area, the relative position relation between the points to be located and the base stations can be acquired through interaction of request signals and response signals between the base stations, further, conversion and calculation are conducted on the relative position relation through known point cloud data of the base stations, accordingly point cloud data corresponding to each point to be located are determined, and all point cloud data of all the points to be located are determined to be a measured point cloud data set.

And S103, generating a target three-dimensional map of the underground operation area according to the scanning point cloud data set and the measuring point cloud data set.

In the embodiment of the application, the point cloud data in the point cloud data set is measured and calculated and may be repeated with the point cloud data scanned by the laser radar. Further, the scanning point cloud data set and the measuring point cloud data set can be integrated, and the scanning point cloud data set and the measuring point cloud data set are subjected to de-overlapping and merging, so that a corresponding point cloud data collection set of the underground operation area is generated.

Optionally, the integration of the measurement point cloud data set and the scan point cloud data set may be implemented based on an extended kalman filter algorithm, or may be implemented based on other algorithms that may integrate the measurement point cloud data set and the scan point cloud data set, which is not limited herein.

The point cloud data collection comprises point cloud data of a scanning point cloud data set and also comprises point cloud data of a measuring and calculating point cloud data set.

And further, generating a target three-dimensional map based on the integrated point cloud data collection.

The three-dimensional laser radar underground map building method based on the unmanned aerial vehicle controls the unmanned aerial vehicle laser radar to scan an underground operation area and obtains a corresponding scanning point cloud data set. Meanwhile, the relative position between a point to be positioned and the base station in the underground operation area is obtained through the UWB technology, the point cloud data of each point to be positioned is obtained through calculation according to the point cloud data and the relative position of the base station, a measuring and calculating point cloud data set corresponding to the underground operation area is further obtained, and a target three-dimensional map of the underground operation area is generated based on the scanning point cloud data set and the measuring and calculating point cloud data set. According to the method and the device, the target three-dimensional map is constructed based on the point cloud data set, the integrity of the target three-dimensional map is optimized, the accuracy of construction of the target three-dimensional map is improved, the unmanned aerial vehicle can realize high-precision real-time positioning based on the target three-dimensional map, and the positioning effect of the unmanned aerial vehicle is optimized.

In the above embodiment, scanning of the downhole operation area is performed by the laser radar, and point cloud data of a plurality of feature points may be obtained, where invalid feature points may exist, and therefore, the feature points obtained by scanning of the laser radar need to be extracted, which can be further understood by referring to fig. 2, where fig. 2 is a schematic flow diagram of a three-dimensional laser radar downhole map building method based on an unmanned aerial vehicle according to another embodiment of the present application, and as shown in fig. 2, the method includes:

s201, acquiring a scanning point set of the laser radar, and determining the curvature of each scanning point in the scanning point set.

In implementation, the points scanned by the laser radar may be determined as a corresponding scanning point set, and in the scanning point set, there is a possibility that the feature difference between adjacent feature points is small.

Further, the adjacent feature points with small feature difference can be further processed, and effective feature points can be extracted.

Wherein, it can be realized by the attribute parameter of each scanning point.

In the embodiment of the application, a target three-dimensional map of an underground operation area needs to be constructed through point cloud data corresponding to each scanning point, and effective feature points can be judged and extracted through appearance attributes of each scanning point.

Alternatively, each scanning point may be determined by a curvature, wherein the curvature corresponding to the scanning point may represent the degree of curvature of the plane to which the scanning point belongs.

Further, the curvature of each scanning point may be acquired based on a setting algorithm.

S202, according to the curvature of each scanning point, effective characteristic points are extracted from the scanning point set, and based on the point cloud data of the effective characteristic points, a scanning point cloud data set of the underground operation area is generated.

In the embodiment of the application, the laser radar can acquire a plurality of scanning points in the same scanning range.

Wherein, the edge point and the plane point can be determined from the scanning point set as the effective characteristic point based on the curvature of the scanning point.

In implementation, the edge points and the plane points in the scanning points can be determined through the curvature.

Alternatively, an edge point curvature threshold and a plane point curvature threshold may be acquired.

Further, the curvature of the scanning point is compared with the edge point curvature threshold, and when the curvature of the scanning point is greater than or equal to the edge point curvature threshold, the scanning point can be judged to be an edge point.

Accordingly, the curvature of the scanning point may be compared with the threshold of the curvature of the planar point, and if the curvature of the scanning point is less than or equal to the threshold of the curvature of the planar point, the scanning point may be determined to be the planar point.

The edge points can represent the edge characteristics of the plane to which the scanning points belong, and the plane points can represent the plane characteristics of the plane to which the scanning points belong, so that the edge points and the plane points can be determined as effective characteristic points in the scanning point set.

Further, based on the point cloud data of each effective characteristic point, a point cloud data set corresponding to the underground operation area is generated and determined as a scanning point cloud data set.

In the embodiment of the application, the scanning result of the laser radar to the underground working area is output based on the scanning frames, and the point cloud data of the effective characteristic points in the underground working area can be acquired by determining the point cloud data of the effective characteristic points in each scanning frame.

Optionally, a scan frame of the lidar acquisition is acquired.

The scanning image acquired by the output device based on the laser radar can be acquired, wherein the scanning image comprises all scanning points in the coverage area of the scanning image. The effective characteristic points in the scanned image can be extracted from the scanned points in the scanned image, so that the effective characteristic points in the scanned image are obtained, and a scanning frame collected by the laser radar is generated.

Further, based on the curvature of the effective feature point, the effective feature point in the scanning frame, which is matched with the previous scanning frame, is determined as the target feature point of the scanning frame.

In implementation, the lidar implements scanning of the downhole operating area based on movement of the drone, where each scanning frame has its corresponding scanning position.

In order to improve the scanning coverage of the underground operation area, the distance between adjacent scanning positions of the laser radar is short, and effective characteristic points are overlapped between adjacent scanning frames acquired by the laser radar.

It should be noted that, when the laser radar on the unmanned aerial vehicle is controlled to scan the downhole operation area, the point cloud data of the scanning point acquired at the initial scanning position is determined.

Further, by matching the effective feature points between the adjacent scanning frames, the point cloud data of the effective feature points in the scanning frames can be determined based on the matching result.

Alternatively, matching of valid feature points between adjacent scan frames may be performed based on curvature.

For example, the point cloud data of the effective feature points in the second scanning frame is set to be acquired.

The effective feature points in the second scanning frame and the effective feature points in the first scanning frame can be matched based on the curvature, two effective feature points with the same curvature in the two scanning frames are determined as two matched effective feature points, and then target feature points with matched effective feature points in the second scanning frame are obtained.

It is understood that when a valid feature point in the second scan frame matches a valid feature point in the first scan frame, it can be determined that the valid feature point in the second scan frame and the valid feature point in the first scan frame that match the valid feature point in the second scan frame are the same valid feature point.

Further, point cloud data of the target feature points are determined, and point cloud data of the effective feature points of the scanning frame are determined according to the relative positions of the target feature points and the residual effective feature points.

In the embodiment of the application, the matched effective feature points in the two adjacent scanning frames can be judged to be the same effective feature point, and the point cloud data of the target feature point in the current scanning frame can be determined based on the point cloud data of the effective feature point in the previous scanning frame.

For example, based on the above example, the point cloud data of the target feature point in the second scanning frame is the same as the point cloud data of the valid feature point in the first scanning frame matched with the target feature point.

The target feature points are part of effective feature points in the scanning frame to which the target feature points belong, and for the remaining effective feature points which are not the target feature points, the corresponding point cloud data can be determined through the relative position relation between the target feature points and the target feature points.

Further, based on the point cloud data of the target feature points and the point cloud data of the remaining effective feature points, the point cloud data of all the effective feature points of the scanning frame is determined.

It should be noted that the point cloud data of the valid feature points in each scanning frame can be determined frame by frame. It can be understood that after the point cloud data of the effective feature point in the current scanning frame is acquired, the point cloud data of the effective feature point in the next scanning frame is determined.

And further, generating a scanning point cloud data set of the underground operation area based on the point cloud data of the effective characteristic points of each scanning frame.

In the embodiment of the application, after the point cloud data of the effective feature points in the scanning frames are acquired frame by frame, the point cloud data of the effective feature points in each scanning frame can be integrated, and all the point cloud data of all the integrated effective feature points are determined to be the scanning point cloud data set corresponding to the underground operation area.

According to the three-dimensional laser radar underground map building method based on the unmanned aerial vehicle, effective characteristic points of a scanning point set collected by the laser radar are extracted based on the curvature of each scanning point, point cloud data of the effective characteristic points in each scanning frame are determined according to matching between the scanning frame and the previous scanning frame and the relative position relation between the effective characteristic points in the scanning frame, and then the scanning point cloud data set of an underground operation area collected by the laser radar is obtained. In the method and the device, the effective characteristic points are extracted, the number of scanning points is reduced, the calculation amount of matching between subsequent scanning frames is effectively reduced, and the acquisition efficiency of the scanning point cloud data set is improved.

In implementation, a scanning frame acquired by a laser radar may be distorted, and therefore, before the scanning frame is matched with a previous scanning frame, the acquired distorted frame needs to be processed in a correlation manner, which can be further understood by referring to fig. 3, where fig. 3 is a schematic flow diagram of a three-dimensional laser radar downhole map building method based on an unmanned aerial vehicle according to another embodiment of the present application, and as shown in fig. 3, the method includes:

s301, judging whether the scanning frame is a distorted frame.

In the implementation, the laser radar moves along with the laser emission for scanning, and the laser scanning data of each angle is obtained in a non-instantaneous manner, so that motion distortion may occur during the scanning of the laser radar.

It can be understood that the scanning frame acquired by the laser radar has a corresponding timestamp, and between adjacent timestamps scanned by the laser radar, the position of the laser radar can move and pose changes can be generated, so that the laser radar has the possibility of motion distortion during scanning.

Further, the scan frame in which the motion distortion exists may be determined as a distorted frame of the lidar.

In the embodiment of the application, whether the current scanning frame has application distortion or not can be judged through the relevant set standard.

For example, because the scanning position difference between adjacent scanning frames is small, whether the current scanning frame has distortion or not can be judged based on the attribute parameter information in the previous scanning frame. Optionally, the relevant determination criterion may be stored in a set position, and called before the scan frame is matched with the previous scan frame, so as to determine whether the current scan frame is a distorted frame.

S302, in response to the fact that the scanning frame is a distorted frame, the distorted frame is compensated based on the collected data corresponding to the inertial measurement unit, and the compensated scanning frame is obtained.

In the embodiment of the application, deviation exists between the point cloud data of the distortion points in the distortion frame and the point cloud data of the actual effective characteristic points, so that the accuracy of the three-dimensional map constructed based on the distortion point cloud data of the distortion points is poor. In order to realize accurate construction of the target three-dimensional map, a correction compensation frame corresponding to the distortion frame can be obtained, and distortion compensation of the laser radar is realized.

Optionally, distortion compensation of the lidar scanning may be implemented by an Inertial Measurement Unit (IMU), where the IMU may be a 9-axis IMU, or may be other Measurement equipment that can obtain information of the lidar acceleration and angular velocity, and is not limited herein.

It should be noted that, the bearing device of the IMU and the lidar are relatively fixed, it can be understood that no relative displacement occurs between the IMU and the lidar, it can be understood that the IMU and the lidar are in a tight coupling relationship, and the acceleration and the angular velocity acquired by the IMU are the same as those of the lidar.

Optionally, pre-integration processing may be performed on data acquired by the IMU, so as to implement distortion compensation on the laser radar.

In the implementation, the operation frequency of the IMU may be 100-1000Hz, the scanning frequency of the lidar may be 10Hz, and the data acquisition frequency of the IMU is higher than the scanning frequency of the lidar to the downhole operation area. Thus, the acquisition data of the IMU may be pre-integrated based on the time interval between each scan frame of the lidar.

Further, a distortion timestamp for the distorted frame may be determined.

And the scanning frame output by the laser radar has a corresponding time stamp.

Alternatively, the distortion timestamp corresponding to the distorted frame may be determined by reading the attribute list of the distorted frame.

Further, acquiring data of the inertial measurement unit at the distorted time stamp.

In the embodiment of the application, the IMU can acquire and store data of acceleration and angular velocity in the set position through the configured gyroscope and accelerometer, wherein the acquired data of the IMU has a timestamp, and the acquired data of the timestamp which is the same as the distortion timestamp can be acquired from the IMU data storage position.

And further, carrying out pose calculation on the acquired data of the distorted timestamp to generate a compensation attitude track corresponding to the distorted frame.

In the embodiment of the application, the pose of the data acquired by the IMU is resolved, so that the compensation pose track corresponding to the distorted frame can be acquired.

It can be understood that after the pose is resolved by the IMU, the distortion points in the distorted frame can be corrected and compensated based on the compensated pose trajectory obtained by the resolution. The compensation attitude trajectory can be understood as a pose change trajectory of an effective feature point in a scanning frame on a distorted frame when motion distortion does not occur.

And further, generating a compensated scanning frame corresponding to the distorted frame according to the effective characteristic point and the compensation posture track of the last scanning frame corresponding to the distorted frame.

In the embodiment of the application, the compensated scanning frame can be acquired by the effective characteristic point in the last scanning frame of the distorted frame and the corresponding compensation posture track.

The effective characteristic points of the previous scanning frame can be controlled to move along the compensation posture track, and target compensation points formed after the effective characteristic point positions move are obtained.

In the embodiment of the present application, the effective feature points in the last scanning frame of the distorted frame may have corresponding compensation gesture tracks, and it can be understood that the effective feature points having the compensation gesture tracks have matched effective feature points in the distorted frame.

Further, the effective feature points of the compensation attitude trajectory in the previous scanning frame can be controlled to move along the corresponding compensation attitude trajectory, the feature points corresponding to the positions after the movement is finished are obtained, and the feature points are determined as the target compensation points.

It should be noted that the compensated pose trajectory corresponds to the valid feature point in the previous scan frame and the distorted feature point in the distorted frame. Effective feature points and distortion feature points corresponding to each compensation attitude trajectory can be determined based on the corresponding relationship.

Correspondingly, a target distortion point corresponding to the target compensation point on the distortion frame is determined according to the compensation posture track, and a residual compensation point corresponding to the residual distortion point is determined according to the relative position of the residual distortion point and the target distortion point.

In the embodiment of the present application, the target distortion point corresponding to each target compensation point may be determined based on the corresponding relationship between each compensation posture trajectory and the distortion point in the distortion frame.

In the implementation, the target compensation points are compensation points corresponding to part of distortion points in the distortion frame, and for the residual distortion points, the residual compensation points corresponding to the residual distortion points can be obtained based on the relative position relationship between the residual distortion points and the target distortion points.

And further, generating a compensated scanning frame corresponding to the distorted frame based on the target compensation point and the residual compensation point.

In the embodiment of the application, the target compensation points and the residual compensation points realize correction compensation of all distortion points in the distortion frame, and the compensated scanning frame corresponding to the distortion frame can be generated based on the target compensation points and the residual compensation points.

And further, replacing the compensated scanning frame with the distorted frame, matching the distorted frame with the previous scanning frame, further acquiring point cloud data of effective characteristic points in the compensated scanning frame, and determining the point cloud data of the effective characteristic points in the next scanning frame based on the point cloud data of the effective characteristic points in the compensated scanning frame.

According to the three-dimensional laser radar underground map building method based on the unmanned aerial vehicle, distortion compensation is carried out on a distortion frame with motion distortion of the laser radar according to the IMU, and a compensated scanning frame corresponding to the distortion frame is generated. The IMU is tightly coupled with the laser radar, so that the IMU can realize the data acquisition of the acceleration and the angular velocity of the laser radar, and the accuracy of distortion compensation is improved.

In the above embodiment, regarding the construction of the target three-dimensional map, it can be further understood by referring to fig. 4, where fig. 4 is a schematic flow chart of a three-dimensional lidar downhole map construction method based on an unmanned aerial vehicle according to another embodiment of the present application, and as shown in fig. 4, the method includes:

s401, a scanning point cloud data set and a point cloud data union set of a measuring point cloud data set are obtained.

According to the scanning point cloud data set and the measuring point cloud data set, the point cloud data of each effective characteristic point of the underground operation area can be highly covered, and the scanning point cloud data set and the measuring point cloud data set are integrated to obtain corresponding point cloud data and are collected.

The point cloud data union set comprises point cloud data in the scanning point cloud data set and also comprises point cloud data in the measuring and calculating point cloud data set.

S402, generating a target three-dimensional map of the underground operation area based on the point cloud data union set.

In the embodiment of the application, the point cloud data of each point in the point cloud data set can fall into a set coordinate system, and then a target three-dimensional map of an underground operation area is generated.

Alternatively, the world coordinate system can be determined as a set coordinate system corresponding to the point cloud data union, the point cloud data union falls into the world coordinate system, and a target three-dimensional map corresponding to the underground operation area is generated based on the world coordinate system and the point cloud data union.

According to the three-dimensional laser radar underground map construction method based on the unmanned aerial vehicle, the construction of the target three-dimensional map is realized through the point cloud data union set, the construction effect of the target three-dimensional map of the underground operation area is optimized, and the coverage rate of the target three-dimensional map to the underground operation area is improved.

To better understand the above embodiments, fig. 5 may be combined with fig. 5, where fig. 5 is a schematic flow chart of a three-dimensional lidar downhole mapping method based on an unmanned aerial vehicle according to another embodiment of the present application, as shown in fig. 5, where:

scanning is carried out on the underground operation area through a laser radar configured by the unmanned aerial vehicle, and effective characteristic points are extracted from the output scanning frame. And matching the adjacent scanning frames, determining the point cloud data of the effective characteristic points in each scanning frame, and generating a corresponding scanning point cloud data set. Distortion compensation may be implemented based on the IMU for distorted frames where motion distortion occurs, before each matching between adjacent scan frames. The IMU is tightly coupled with the laser radar, pose calculation is carried out based on the collected acceleration and angular velocity, a compensation attitude track corresponding to the distortion frame is obtained, distortion compensation of the distortion frame is achieved based on the compensation attitude track, and the compensated scanning frame is generated. And replacing the compensated scanning frame with the distorted frame, and acquiring point cloud data of compensation points. Meanwhile, the relative position relation between the to-be-positioned point and the base station is obtained through the UWB technology, the relative position between the to-be-positioned point and the base station is calculated according to the point cloud data of the base station, the point cloud data of each to-be-positioned point is further obtained, and the measuring and calculating point cloud data set of the underground operation area is further obtained.

And further, generating a target three-dimensional map of the underground operation area based on the scanning point cloud data set and the measuring point cloud data set.

The three-dimensional laser radar underground map building method based on the unmanned aerial vehicle controls the unmanned aerial vehicle laser radar to scan an underground operation area and obtains a corresponding scanning point cloud data set. Meanwhile, the relative position between a point to be positioned and the base station in the underground operation area is obtained through the UWB technology, the point cloud data of each point to be positioned is obtained through calculation according to the point cloud data and the relative position of the base station, a measuring and calculating point cloud data set corresponding to the underground operation area is further obtained, and a target three-dimensional map of the underground operation area is generated based on the scanning point cloud data set and the measuring and calculating point cloud data set. According to the method and the device, the target three-dimensional map is constructed based on the point cloud data set, the integrity of the target three-dimensional map is optimized, the accuracy of construction of the target three-dimensional map is improved, the unmanned aerial vehicle can realize high-precision real-time positioning based on the target three-dimensional map, and the positioning effect of the unmanned aerial vehicle is optimized.

Further, while constructing a target three-dimensional map, accurate real-time positioning of the unmanned aerial vehicle can be realized, which can be understood by combining fig. 6, where fig. 6 is a schematic flow diagram of a three-dimensional lidar underground map construction method based on an unmanned aerial vehicle according to another embodiment of the present application, and as shown in fig. 6, the method includes:

s601, acquiring a first operation coordinate set of the unmanned aerial vehicle according to the scanning point cloud data set.

In this application embodiment, through the removal of control unmanned aerial vehicle for the last lidar of configuration of unmanned aerial vehicle can realize the point cloud data acquisition to the scanning point in borehole operation area, consequently, need carry out accurate location and accurate mobility control to unmanned aerial vehicle.

Optionally, the scanning position of the laser radar may be calculated according to the point cloud data of each effective feature point in the scanning point cloud data set.

Furthermore, the laser radar outputs effective characteristic points in the underground operation area based on a scanning frame mode, so that the scanning position of the unmanned aerial vehicle can be obtained according to the point cloud data of the effective characteristic points in each scanning frame.

It can be understood that, according to any scanning frame, calculation can be performed according to the point cloud data of each effective feature point, and the effective feature points in the scanning frame are obtained according to the calculation result, and when the laser radar scans and acquires the three-dimensional coordinates of the position where the laser radar is located.

In the implementation, the laser radar is configured on the unmanned aerial vehicle, so that the three-dimensional coordinate of the position where the laser radar is located can be determined as the first operation coordinate of the unmanned aerial vehicle.

And further, integrating the three-dimensional coordinates of the scanning position of the laser radar corresponding to each scanning frame, and further acquiring a first operation coordinate set of the unmanned aerial vehicle.

And S602, acquiring a second operation coordinate set of the unmanned aerial vehicle according to the relative position of the unmanned aerial vehicle and the positioning base station.

In the implementation, errors may exist in resolving the three-dimensional coordinates of the scanning position of the laser radar according to the point cloud data scanned by the laser radar. Therefore, the work coordinates of the drone may be determined simultaneously in other ways.

In the embodiment of the application, the positioning base station can be arranged in the underground operation area, and the three-dimensional coordinates of the positioning base station are determined. The unmanned aerial vehicle and the base station have a relative position relationship, so that the coordinate of the position where the unmanned aerial vehicle is located can be resolved by acquiring the relative position relationship between the unmanned aerial vehicle and the base station and combining the three-dimensional coordinate of the positioning base station, and the resolved three-dimensional coordinate is determined as a second operation coordinate of the unmanned aerial vehicle.

In the realization, unmanned aerial vehicle is continuous removal when scanning the operation area in the pit, can obtain the relative position between unmanned aerial vehicle and the location basic station after removing at every turn to solve and acquire the three-dimensional coordinate that unmanned aerial vehicle corresponds.

Further, the acquired three-dimensional coordinates are resolved based on all the unmanned aerial vehicles after moving, and a second operation coordinate set of the unmanned aerial vehicles is generated.

Alternatively, the acquisition of the relative position between the positioning base station and the drone may be achieved by UWB technology.

S603, determining the operation position of the unmanned aerial vehicle in the underground operation area according to the first operation coordinate set and the second operation coordinate set.

In the embodiment of the application, in the first operation coordinate set and the second operation coordinate set, the first operation coordinate and the second operation coordinate have a corresponding relationship, and the first operation coordinate and the second operation coordinate having the corresponding relationship can be determined as the same three-dimensional coordinate of the unmanned aerial vehicle.

Further, filtering calculation can be performed on the first operation coordinate and the second operation coordinate which have the corresponding relation, and the result of the filtering calculation is determined as the target operation coordinate of the unmanned aerial vehicle in the underground operation area.

The compensation correction of the first operation coordinate can be realized through the filtering calculation of the second operation coordinate and the first operation coordinate when the first operation coordinate has errors, so that the accurate target operation coordinate of the unmanned aerial vehicle is determined.

Further, the working position of the unmanned aerial vehicle in the underground working area is determined based on the target working coordinate.

Alternatively, the first operation coordinate and the second operation coordinate of the drone may be world coordinates, and after the target operation coordinate of the drone is obtained, the operation position of the drone in the underground operation area may be determined based on the world coordinate system.

According to the three-dimensional laser radar underground map building method based on the unmanned aerial vehicle, a first operation coordinate set of the unmanned aerial vehicle is obtained through point cloud data obtained through laser radar scanning, meanwhile, a second operation coordinate set of the unmanned aerial vehicle is obtained according to the relative position relation between the unmanned aerial vehicle and a positioning base station, and a target operation coordinate of the unmanned aerial vehicle is obtained according to the first operation coordinate set and the second operation coordinate set, so that the operation position of the unmanned aerial vehicle is determined. In this application, confirm unmanned aerial vehicle's operation position through multiple mode, improved unmanned aerial vehicle's location rate of accuracy to improve unmanned aerial vehicle's control accuracy, improved unmanned aerial vehicle in the security and the efficiency of operation in operation district in the pit.

The three-dimensional laser radar underground map building method based on the unmanned aerial vehicle provided by the embodiment of the application is corresponding to the three-dimensional laser radar underground map building method based on the unmanned aerial vehicle provided by the several embodiments of the application, and the three-dimensional laser radar underground map building method based on the unmanned aerial vehicle provided by the embodiment of the application is corresponding to the three-dimensional laser radar underground map building method based on the unmanned aerial vehicle provided by the several embodiments of the application, so that the embodiment mode of the three-dimensional laser radar underground map building method based on the unmanned aerial vehicle provided by the embodiment of the application is also suitable for the three-dimensional laser radar underground map building method based on the unmanned aerial vehicle provided by the embodiment of the application, and detailed description is not needed in the embodiments of the invention.

Fig. 7 is a schematic structural diagram of the three-dimensional lidar downhole mapping device based on the unmanned aerial vehicle according to an embodiment of the present application, and as shown in fig. 7, the three-dimensional lidar downhole mapping device 700 based on the unmanned aerial vehicle includes a scanning module 71, a measuring module 72, and a generating module 73, where:

the scanning module 71 is configured to control a laser radar of the unmanned aerial vehicle to scan the downhole operation area to obtain a scanning point cloud data set of the downhole operation area;

the measuring and calculating module 72 is used for acquiring a measuring and calculating point cloud data set of the underground operation area according to the relative position of the unmanned aerial vehicle and a positioning base station of the underground operation area;

and the generating module 73 is used for generating a target three-dimensional map of the underground operation area according to the scanning point cloud data set and the measuring and calculating point cloud data set.

Fig. 8 is a schematic structural diagram of a three-dimensional lidar downhole mapping device based on an unmanned aerial vehicle according to another embodiment of the present application, and as shown in fig. 8, the three-dimensional lidar downhole mapping device 800 based on an unmanned aerial vehicle includes a scanning module 81, a measuring module 82, a generating module 83, and a positioning module 84, where:

the scanning module 71, the estimation module 72, and the generation module 73 have the same configuration and function as the scanning module 81, the estimation module 82, and the generation module 83.

In this embodiment of the application, the scanning module 81 is further configured to: acquiring a scanning point set of the laser radar, and determining the curvature of each scanning point in the scanning point set; and according to the curvature of each scanning point, effective characteristic points are extracted from the scanning point set, and a scanning point cloud data set of the underground operation area is generated based on the point cloud data of the effective characteristic points.

In this embodiment of the application, the scanning module 81 is further configured to: based on the curvature of the scanning points, edge points and plane points are determined from the scanning point set as effective feature points.

In this embodiment of the application, the scanning module 81 is further configured to: acquiring a scanning frame acquired by a laser radar; determining effective characteristic points matched with the previous scanning frame in the scanning frame based on the curvature of the effective characteristic points, and taking the effective characteristic points as target characteristic points of the scanning frame; determining point cloud data of the target feature points, and determining the point cloud data of the effective feature points of the scanning frame according to the relative positions of the target feature points and the residual effective feature points; and generating a scanning point cloud data set of the underground operation area based on the point cloud data of the effective characteristic points of each scanning frame.

In this embodiment of the application, the scanning module 81 is further configured to: judging whether the scanning frame is a distorted frame; and responding to the scanning frame as a distorted frame, and compensating the distorted frame based on the acquired data corresponding to the inertial measurement unit to obtain the compensated scanning frame.

In this embodiment of the application, the scanning module 81 is further configured to: determining a distortion timestamp for the distorted frame; acquiring data acquired by an inertial measurement unit at a distortion timestamp; performing pose resolving on the acquired data of the distorted timestamp to generate a compensation attitude track corresponding to the distorted frame; and generating a compensated scanning frame corresponding to the distorted frame according to the effective characteristic point and the compensation posture track of the last scanning frame corresponding to the distorted frame.

In this embodiment of the application, the scanning module 81 is further configured to: controlling the effective characteristic point of the previous scanning frame to move along the compensation attitude track, and acquiring a target compensation point formed after the effective characteristic point moves; determining a target distortion point corresponding to the target compensation point on the distortion frame according to the compensation attitude track, and determining a residual compensation point corresponding to the residual distortion point according to the relative position of the residual distortion point and the target distortion point; and generating a compensated scanning frame corresponding to the distorted frame based on the target compensation point and the residual compensation point.

In this embodiment of the application, the generating module 83 is further configured to: acquiring a point cloud data union set of a scanning point cloud data set and a measuring point cloud data set; and generating a target three-dimensional map of the underground operation area based on the point cloud data union set.

In an embodiment of the present application, the apparatus further comprises a positioning module 84 for: acquiring a first operation coordinate set of the unmanned aerial vehicle according to the scanning point cloud data set; acquiring a second operation coordinate set of the unmanned aerial vehicle according to the relative position of the unmanned aerial vehicle and the positioning base station; and determining the operation position of the unmanned aerial vehicle in the underground operation area according to the first operation coordinate set and the second operation coordinate set.

The application provides a three-dimensional laser radar map device in pit based on unmanned aerial vehicle controls unmanned aerial vehicle laser radar scanning borehole operation district, acquires the scanning point cloud data set that corresponds. Meanwhile, the relative position between a point to be positioned and the base station in the underground operation area is obtained through the UWB technology, the point cloud data of each point to be positioned is obtained through calculation according to the point cloud data and the relative position of the base station, a measuring and calculating point cloud data set corresponding to the underground operation area is further obtained, and a target three-dimensional map of the underground operation area is generated based on the scanning point cloud data set and the measuring and calculating point cloud data set. According to the method and the device, the target three-dimensional map is constructed based on the point cloud data set, the integrity of the target three-dimensional map is optimized, the accuracy of construction of the target three-dimensional map is improved, the unmanned aerial vehicle can realize high-precision real-time positioning based on the target three-dimensional map, and the positioning effect of the unmanned aerial vehicle is optimized.

To achieve the above embodiments, the present application also provides a computer-readable storage medium.

The computer-readable storage medium provided by the embodiment of the application stores a computer program thereon, and when the program is executed by a processor, the method for constructing the three-dimensional laser radar underground map based on the unmanned aerial vehicle provided by the embodiment is realized.

The computer-readable storage medium of the embodiment of the application controls the laser radar of the unmanned aerial vehicle to scan the underground operation area and obtain the corresponding scanning point cloud data set. Meanwhile, the relative position between a point to be positioned and the base station in the underground operation area is obtained through the UWB technology, the point cloud data of each point to be positioned is obtained through calculation according to the point cloud data and the relative position of the base station, a measuring and calculating point cloud data set corresponding to the underground operation area is further obtained, and a target three-dimensional map of the underground operation area is generated based on the scanning point cloud data set and the measuring and calculating point cloud data set. According to the method and the device, the target three-dimensional map is constructed based on the point cloud data set, the integrity of the target three-dimensional map is optimized, the accuracy of construction of the target three-dimensional map is improved, the unmanned aerial vehicle can realize high-precision real-time positioning based on the target three-dimensional map, and the positioning effect of the unmanned aerial vehicle is optimized.

In the description of the present application, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the present application and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the present application.

Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "a plurality" means two or more unless specifically limited otherwise.

In this application, unless expressly stated or limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can include, for example, fixed connections, removable connections, or integral parts; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meaning of the above terms in the present application can be understood by those of ordinary skill in the art as appropriate.

In this application, unless expressly stated or limited otherwise, the first feature "on" or "under" the second feature may be directly contacting the first and second features or indirectly contacting the first and second features through intervening media. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.

In the description herein, reference to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the application. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.

Although embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application, and that variations, modifications, substitutions and alterations may be made to the above embodiments by those of ordinary skill in the art within the scope of the present application.

Claims (10)

1. A three-dimensional laser radar underground map construction method based on an unmanned aerial vehicle is characterized by comprising the following steps:

controlling a laser radar of an unmanned aerial vehicle to scan an underground operation area so as to obtain a scanning point cloud data set of the underground operation area;

acquiring a measurement point cloud data set of the underground operation area according to the relative position of the unmanned aerial vehicle and a positioning base station of the underground operation area;

and generating a target three-dimensional map of the underground operation area according to the scanning point cloud data set and the measuring point cloud data set.

2. The method of claim 1, wherein the controlling the lidar of the drone to scan a downhole operating area to obtain a scan point cloud dataset of the downhole operating area comprises:

acquiring a scanning point set of the laser radar, and determining the curvature of each scanning point in the scanning point set;

and according to the curvature of each scanning point, effective characteristic points are extracted from the scanning point set, and the scanning point cloud data set of the underground operation area is generated based on the point cloud data of the effective characteristic points.

3. The method of claim 2, wherein the extracting effective feature points from the set of scan points according to the curvature of each scan point comprises:

and determining edge points and plane points from the scanning point set based on the curvature of the scanning points as the effective characteristic points.

4. The method of claim 3, wherein generating the scan point cloud dataset for the downhole operation zone based on the point cloud data for the valid feature points comprises:

acquiring a scanning frame acquired by the laser radar;

determining effective characteristic points matched with the previous scanning frame in the scanning frames based on the curvatures of the effective characteristic points, wherein the effective characteristic points are used as target characteristic points of the scanning frames;

determining point cloud data of the target feature points, and determining the point cloud data of the effective feature points of the scanning frame according to the relative positions of the target feature points and the residual effective feature points;

and generating the scanning point cloud data set of the underground operation area based on the point cloud data of the effective characteristic points of each scanning frame.

5. The method according to claim 4, wherein the determining the valid feature point in the scan frame matching with the previous scan frame based on the curvature of the valid feature point as the target feature point of the scan frame further comprises:

judging whether the scanning frame is a distorted frame;

and responding to the scanning frame as the distorted frame, and compensating the distorted frame based on the acquired data corresponding to the inertial measurement unit to obtain the compensated scanning frame.

6. The method of claim 5, wherein the compensating the distorted frame based on the corresponding acquired data of the inertial measurement unit to obtain a compensated scan frame comprises:

determining a distortion timestamp for the distorted frame;

acquiring the acquired data of the inertial measurement unit at the distortion time stamp;

performing pose resolving on the acquired data of the distorted timestamp to generate a compensation attitude track corresponding to the distorted frame;

and generating the compensated scanning frame corresponding to the distorted frame according to the effective characteristic point of the last scanning frame corresponding to the distorted frame and the compensation posture track.

7. The method of claim 6, wherein generating the compensated scan frame corresponding to the distorted frame according to the valid feature points of the previous scan frame corresponding to the distorted frame and the compensated pose trajectory comprises:

controlling the effective characteristic point of the last scanning frame to move along the compensation posture track, and acquiring a target compensation point formed after the effective characteristic point moves;

determining a target distortion point corresponding to the target compensation point on the distortion frame according to the compensation posture track, and determining a residual compensation point corresponding to the residual distortion point according to the relative position of the residual distortion point and the target distortion point;

and generating the compensated scanning frame corresponding to the distorted frame based on the target compensation point and the residual compensation point.

8. The method of claim 1, wherein generating the target three-dimensional map of the downhole operating zone from the scan coordinate set and the reckoning coordinate set comprises:

acquiring a point cloud data union set of the scanning point cloud data set and the measuring point cloud data set;

and generating the target three-dimensional map of the underground operation area based on the point cloud data union.

9. The method according to any one of claims 1-8, further comprising:

acquiring a first operation coordinate set of the unmanned aerial vehicle according to the scanning point cloud data set;

acquiring a second operation coordinate set of the unmanned aerial vehicle according to the relative position of the unmanned aerial vehicle and the positioning base station;

and determining the operation position of the unmanned aerial vehicle in the underground operation area according to the first operation coordinate set and the second operation coordinate set.

10. The utility model provides a three-dimensional lidar is map construction device in pit based on unmanned aerial vehicle which characterized in that includes:

the scanning module is used for controlling a laser radar of the unmanned aerial vehicle to scan the underground operation area so as to obtain a scanning point cloud data set of the underground operation area;

the measuring and calculating module is used for acquiring a measuring and calculating point cloud data set of the underground operation area according to the relative position of the unmanned aerial vehicle and the positioning base station of the underground operation area;

and the generating module is used for generating a target three-dimensional map of the underground operation area according to the scanning point cloud data set and the measuring point cloud data set.

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