CN110634183A - Map construction method and device and unmanned equipment - Google Patents
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Abstract
The disclosure provides a map construction method and device and unmanned equipment, and relates to the technical field of robots. The map construction method comprises the following steps: acquiring point cloud data of an environment where the unmanned equipment is located; determining a pitch angle change value of the unmanned equipment by using an Inertial Measurement Unit (IMU); correcting the rotation angle of the point cloud data according to the pitch angle change value; and constructing a map according to the corrected point cloud data. By the method, the point cloud data can be corrected by using the pitch angle change value detected by the IMU, and the map is constructed by using the corrected point cloud data, so that the influence of the terrain change on the map construction is reduced, and the accuracy of the map construction is improved.
Description
Technical Field
The disclosure relates to the technical field of robots, in particular to a map construction method and device and unmanned equipment.
Background
Currently, some unmanned devices need to autonomously operate tasks in an application process, for example, a rescue robot needs to travel from a starting point to a target point without depending on manual intervention in a rescue environment. In the process, the robot needs to complete the tasks of sensing information, processing information and active identification, the first step of completing the sensing information is the key step, and the accurate map is constructed, so that a high-quality data base can be improved for executing the tasks, and the success rate of the tasks is improved.
Disclosure of Invention
It is an object of the present disclosure to improve the accuracy of map construction.
According to an aspect of the present disclosure, a map construction method is provided, including: acquiring point cloud data of an environment where the unmanned equipment is located; determining a pitch angle change value of the unmanned equipment by using an Inertial Measurement Unit (IMU); correcting the rotation angle of the point cloud data according to the pitch angle change value; and constructing a map according to the corrected point cloud data.
Optionally, correcting the rotation angle of the point cloud data comprises: under the condition that the pitch angle change value of the IMU in a preset mapping period exceeds a preset threshold value, rotating the point cloud data according to the pitch angle change value; and under the condition that the pitch angle change value of the IMU in the preset mapping period does not exceed a preset threshold value, the point cloud data is not rotated.
Optionally, the point cloud data is acquired by using a three-dimensional laser detector according to a SLAM (Simultaneous Localization and Mapping) algorithm based on a distance sensor.
Optionally, the pitch angle change value is obtained by counting the pitch angle change condition of the unmanned equipment in a preset mapping period by the IMU.
Optionally, the map construction according to the corrected point cloud data includes: calculating the roughness of points in the point cloud in the same scanning plane, and selecting the points with the maximum roughness and the minimum roughness as a feature point alternative set; determining a line or a surface to which a point in the point cloud belongs through feature point matching; determining the pose change condition of the unmanned equipment according to the line or the plane to which the point belongs; and starting map construction according to the pose change condition and the point cloud data, or continuing to construct the map on the basis of the map constructed in the previous map construction period.
By the method, the point cloud data can be corrected by using the pitch angle change value detected by the IMU, and the map is constructed by using the corrected point cloud data, so that the influence of the terrain change on the map construction is reduced, and the accuracy of the map construction is improved.
According to another aspect of the present disclosure, a map construction apparatus is provided, including: the point cloud data acquisition module is configured to acquire point cloud data of an environment where the unmanned equipment is located; a pitch angle change value determination module configured to determine a pitch angle change value of the drone using an Inertial Measurement Unit (IMU); a point cloud correction module configured to correct a rotation angle of the point cloud data according to the pitch angle variation value; and the map building module is configured to build a map according to the corrected point cloud data.
Optionally, the point cloud correction module is configured to: under the condition that the pitch angle change value of the IMU in a preset mapping period exceeds a preset threshold value, rotating the point cloud data according to the pitch angle change value; and under the condition that the pitch angle change value of the IMU in the preset mapping period does not exceed a preset threshold value, the point cloud data is not rotated.
Optionally, the point cloud data is acquired by using a three-dimensional laser detector according to a map building SLAM algorithm and instant positioning based on a distance sensor.
Optionally, the pitch angle change value is obtained by counting the pitch angle change condition of the unmanned equipment in a preset mapping period by the IMU.
Optionally, the map building module comprises: the characteristic point determining unit is configured to calculate the roughness of points in point clouds in the same scanning plane, select the points with the maximum roughness and the minimum roughness as a characteristic point alternative set, use the points with the maximum roughness as angular points and use the points with the minimum roughness as plane points; a feature point matching unit configured to determine a line or a plane to which a point in the point cloud belongs by feature point matching; a pose change determination unit configured to determine a pose change situation of the unmanned aerial vehicle according to a line or a plane to which the point belongs; and the map generation unit is configured to start map construction according to the pose change condition and the point cloud data or continue map construction on the basis of the map constructed in the previous map construction period.
According to still another aspect of the present disclosure, a map construction apparatus is provided, including: a memory; and a processor coupled to the memory, the processor configured to perform any of the map construction methods mentioned above based on instructions stored in the memory.
The device can correct the point cloud data by using the pitch angle change value detected by the IMU, and map construction is performed by using the corrected point cloud data, so that the influence of the terrain change on the map construction is reduced, and the map construction accuracy is improved.
According to yet another aspect of the present disclosure, a computer-readable storage medium is proposed, on which computer program instructions are stored, which instructions, when executed by a processor, implement the steps of any of the map construction methods mentioned above.
By executing the instructions on the computer-readable storage medium, the point cloud data can be corrected by using the pitch angle change value detected by the IMU, and the map is constructed by using the corrected point cloud data, so that the influence of the terrain change on the map construction is reduced, and the accuracy of the map construction is improved.
In addition, according to an aspect of the present disclosure, there is also provided an unmanned aerial vehicle including any one of the map construction apparatuses mentioned above; the inertial measurement unit IMU is configured to acquire the pitch angle change condition of the unmanned equipment in real time; and the point cloud detection device is configured to detect the environment in which the unmanned equipment is located and collect point cloud data.
Optionally, the point cloud detection device comprises a three-dimensional laser detector.
The unmanned equipment can correct the point cloud data by using the pitch angle change value detected by the IMU, and map construction is performed by using the corrected point cloud data, so that the influence of the terrain change on the map construction is reduced, and the accuracy of the map construction is improved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
FIG. 1 is a flow chart of one embodiment of a mapping method of the present disclosure.
Fig. 2 is a flowchart of another embodiment of a mapping method of the present disclosure.
Fig. 3 is a schematic diagram of an embodiment of a mapping effect in the mapping method of the present disclosure.
Fig. 4 is a flowchart of an embodiment of a map construction according to corrected point cloud data in the map construction method of the present disclosure.
Fig. 5 is a schematic diagram of an embodiment of feature point matching in the map construction method of the present disclosure.
FIG. 6 is a schematic diagram of one embodiment of a mapping apparatus of the present disclosure.
FIG. 7 is a diagram of one embodiment of a mapping module in a mapping apparatus of the present disclosure.
Fig. 8 is a schematic diagram of another embodiment of a mapping apparatus of the present disclosure.
Fig. 9 is a schematic diagram of yet another embodiment of a mapping apparatus of the present disclosure.
Fig. 10 is a schematic diagram of one embodiment of an unmanned device of the present disclosure.
Detailed Description
The technical solution of the present disclosure is further described in detail by the accompanying drawings and examples.
A flow diagram of one embodiment of a mapping method of the present disclosure is shown in fig. 1.
In step 101, point cloud data of an environment where the unmanned device is located is acquired. In one embodiment, the point cloud data may be acquired according to a range sensor based SLAM algorithm using a three-dimensional laser detector.
In step 102, a pitch angle change value of the drone is determined using the IMU. In one embodiment, the IMU may obtain the pitch angle variation according to the detection frequency thereof, and perform statistics to obtain the pitch angle variation within a predetermined time period as the pitch angle variation value.
In step 103, the rotation angle of the point cloud data is corrected according to the pitch angle variation value. In one embodiment, the point cloud data may be rotated as a whole according to the pitch angle change value, so that the angle of the constructed map is adjusted but the shape is not changed.
In step 104, a map is constructed from the corrected point cloud data.
Due to discontinuous motion caused by the height unstructured terrain, matching errors of feature points can be caused under certain conditions, particularly, point cloud estimation in the pitch angle direction can be wrong due to the fact that the point cloud is subjected to severe pitch angle change in the junction position of an ascending slope and a descending slope, and due to the fact that the resolution ratio of a radar in the z-axis direction is low, errors of map construction are increased. By the method in the embodiment, the point cloud data can be corrected by using the pitch angle change value detected by the IMU, and the map is constructed by using the corrected point cloud data, so that the influence of the terrain change on the map construction is reduced, and the accuracy of the map construction is improved. Particularly, under the application scene of the rescue robot, the rescue robot can deal with relatively complex terrain change conditions, and synchronous positioning and map building of the rescue robot are realized.
In one embodiment, to reduce the amount of computation and the burden of adjusting the rotation angle of the point cloud, a certain threshold value may be set, and when the threshold value is exceeded, the rotation angle of the point cloud is modified, otherwise the angle change may be ignored, as shown in fig. 2:
in step 201, a pitch angle change value of the drone is determined using the IMU. In one embodiment, the IMU may be used to measure and count the pitch angle change of the unmanned device during a predetermined mapping period as a pitch angle change value. In one embodiment, the predetermined patterning period may correspond to a radar frame period of the lidar.
In step 202, point cloud data of an environment in which the unmanned device is located is acquired.
In step 203, it is determined whether the pitch angle variation value exceeds a predetermined threshold, where the predetermined threshold may be set as required, such as 3 to 7 °, and is preferably 5 °. If the predetermined threshold is exceeded, go to step 204; if the predetermined threshold is not exceeded, step 205 is executed.
In step 204, the point cloud data is rotated according to the pitch angle variation value.
In step 205, a map is constructed using the point cloud data. In one embodiment, as shown in FIG. 3, the solid rectangular area on the left is the map Q that has been builtk-1Pose status of lidar as Tk-1As shown. According to the point cloud data collected by laser detection (as shown by the open rectangular area in the left picture)
If directly according to
When the drawing is built, the pose changes suddenly, and the natural law in the use process is violated. Point cloud data
Performing rotation correction according to the pitch angle change value detected by the IMU to obtain QkThen the pose state of the laser radar is as TkThe method meets the characteristic of continuous change of the pose of the equipment, and improves the accuracy of pose determination and map construction of the unmanned equipment.
By the method, the point cloud data can not be rotated under the condition that the change value of the pitch angle is not larger than the preset threshold value, namely, certain tolerance is provided for the change of the pitch angle, the map construction accuracy is ensured to be acceptable, meanwhile, the phenomenon that the calculation amount is increased and the map construction efficiency is reduced due to the fact that the rotation angle of the point cloud data is repeatedly adjusted is avoided, and the efficiency requirement in practical application is met.
In one embodiment, because the data acquisition frequency of the IMU is far higher than the composition frequency, the pose estimation based on the IMU data and the map construction based on the point cloud data can be completed in two threads, meanwhile, the point cloud data is corrected by using the statistic value of the pose estimation based on the IMU data, the pose change of the unmanned equipment in a short time interval is obtained through high-frequency low-complexity registration, the matching of two parts with different frequencies is realized, and the pose registration accuracy in each map construction process is ensured.
A flowchart of an embodiment of the map construction method according to the corrected point cloud data of the present disclosure is shown in fig. 4.
In step 401, roughness calculation is performed on points in the point cloud within the same scanning plane, and the points with the largest and smallest roughness are selected as feature point candidate sets.
In one embodiment, roughness calculations may be performed on points within the same scan plane, and the points with the greatest and smallest roughness are selected as the feature point candidate set. The points with the largest roughness are defined as angular points, and the points with the smallest roughness are defined as plane points. In order to uniformly distribute the characteristic points in the environment, one scanning surface is divided into four sub-areas, and each sub-area can extract 2 angular points and 4 plane points at most. The angular point is the point with the largest sub-region roughness, the plane point is the point with the smallest sub-region roughness, and the two characteristic thresholds can be set to be 0.003, namely the angular point roughness is larger than 0.003 and the plane point roughness is smaller than 0.003.
Calculating the roughness of each point, selecting the maximum roughness point as an angular point, selecting the minimum roughness point as a plane point, and satisfying the following conditions:
(1) the number of angular points of each sub-area is less than 2, and the number of plane points is less than 4;
(2) the characteristic points are not adjacent to each other;
(3) the plane where the plane point is located is not parallel to the laser beam;
(4) the corner points are not occlusion region boundary points.
In step 402, a line or a plane to which a point in the point cloud belongs is determined through feature point matching, and a pose change condition of the unmanned device is determined according to the line or the plane to which the point belongs.
In one embodiment, it may be assumed that a three-dimensional point cloud P has been obtained within adjacent time periods k-1 and kk-1And PkDefinition of a set of points JkIs a point cloud PkCharacteristic of a central corner, point set SkIs a point cloud PkThe feature of the plane point in (1) is required to be in the point cloud Pk-1To find JkEdge line segment corresponding to center point and SkThe mid-plane point corresponds to a plane. As shown in fig. 5:
left graph point A ∈ JkIs a point cloud PkA certain corner point, and point B is point cloud Pk-1The closest point to the midpoint i, the dashed line represents the scan plane 2 at which point B lies, the solid line represents the adjacent scan plane 1 of the scan plane, and point C represents the closest point to the midpoint a in the adjacent scan plane. If the two points are angular points, the edge where the point pair (B, C) is located is regarded as a point A and a point cloud Pk-1The matched line segment of (1). Since the line segment where the two feature points on one scan plane are located is represented as a straight line, in which case no corner points exist and should be discarded, the corner points on the different scan planes are selected as the point pairs on the matching line segment.
Right graph point D ∈ SkIs a point cloud PkA certain plane point in the point cloud Pk-1The closest point to the midpoint i, the dotted line represents the scan plane 4 on which point E lies, the solid line represents the adjacent scan plane 3 of the scan plane, point F represents the closest point to the midpoint D in the same scan plane, and point G represents the closest point in the adjacent scan plane. In this case, it can be ensured that the three selected matching points (E, F, G) are not collinear, and if the three matching points are all plane points, the plane in which the three matching points are located is the matching plane of point D.
In step 403, the pose change condition of the unmanned device is determined according to the line or plane to which the point belongs.
In one embodiment, the goal of motion estimation can be simplified to make the cost function simple, with the matching of corner points to corresponding line segments and plane points to corresponding planes established
The minimum value is obtained. Wherein d isi→JDenotes the distance of the corner point i from its matching line segment, dj→SRepresenting the distance of the plane point j from its matching plane.
In step 404, map construction is started according to the pose change condition and the point cloud data, or map construction is continued on the basis of the map constructed in the previous map construction period.
In the related technology, the adjustment of the point cloud angle can be realized through a high-precision registration algorithm so as to reduce the influence of error accumulation on the map accuracy, but the high-precision registration algorithm has high requirement on the computing capability of equipment, and has complex operation and long time consumption. By the method in the embodiment of the disclosure, the adjustment of the point cloud angle is realized by utilizing the pitch angle statistic function of the IMU, the calculation amount is reduced, and the map construction efficiency is improved.
In one embodiment, the mapping may be performed using the logic shown below:
by the method, an LOAM (laser radar measurement and Mapping) algorithm is improved by IMU information fusion, pose estimation with high operating frequency and map creation with low frequency can be operated on two independent threads, the calculation complexity of the algorithm is effectively reduced, and point cloud mismatching under the condition of pitch angle motion height discontinuity in the rescue environment is corrected by fusing IMU information. By the method, the estimation of the position and the attitude of the robot and the establishment of an environment map in a rescue environment can be finished in real time, and the method has a good correction effect on the discontinuous pitch angle motion.
A schematic diagram of one embodiment of a mapping apparatus of the present disclosure is shown in fig. 6. The point cloud data acquisition module 61 can acquire point cloud data of an environment in which the unmanned aerial vehicle is located. In one embodiment, the point cloud data may be acquired according to a range sensor based SLAM algorithm using a three-dimensional laser detector. The pitch angle change value determination module 62 can determine a pitch angle change value for the drone using the IMU. In one embodiment, the IMU may obtain the pitch angle variation according to the detection frequency thereof, and perform statistics to obtain the pitch angle variation within a predetermined time period as the pitch angle variation value. The point cloud correction module 63 can correct the rotation angle of the point cloud data according to the pitch angle change value. In one embodiment, the point cloud data may be rotated as a whole according to the pitch angle change value, so that the angle of the constructed map is adjusted but the shape is not changed. The map construction module 64 can perform map construction based on the corrected point cloud data.
The map construction device can correct the point cloud data by using the pitch angle change value detected by the IMU, and construct a map by using the corrected point cloud data, thereby reducing the influence of the terrain change on the map construction and improving the accuracy of the map construction.
In one embodiment, a certain threshold value can be set, the rotation angle of the point cloud is modified when the threshold value is exceeded, otherwise the angle change can be ignored, so that the map construction accuracy is acceptable, meanwhile, the increase of computation and the reduction of map construction efficiency caused by repeatedly adjusting the rotation angle of the point cloud data are avoided, and the efficiency requirement in practical application is met.
A schematic diagram of one embodiment of a mapping module in a mapping apparatus of the present disclosure is shown in fig. 7. The feature point determination unit 701 can perform roughness calculation on points in the point cloud within the same scanning plane, and select points with the largest and smallest roughness as a feature point candidate set. The feature point matching unit 702 can determine a line or a plane to which a point in the point cloud belongs by feature point matching, and determine a pose change condition of the unmanned aerial vehicle according to the line or the plane to which the point belongs. The pose change determination unit 703 can determine the pose change situation of the unmanned device according to the line or plane to which the point belongs. The map generation unit 704 can start map construction according to the pose change condition and the point cloud data, or continue map construction on the basis of the map constructed in the previous map construction period.
The map building module can realize the adjustment of the point cloud angle by utilizing the pitch angle counting function of the IMU, reduces the calculation amount and improves the map building efficiency.
A schematic structural diagram of an embodiment of the map building apparatus of the present disclosure is shown in fig. 8. The mapping means comprises a memory 801 and a processor 802. Wherein: the memory 801 may be a magnetic disk, flash memory, or any other non-volatile storage medium. The memory is for storing the instructions in the corresponding embodiments of the map construction method above. Coupled to the memory 801, the processor 802 may be implemented as one or more integrated circuits, such as a microprocessor or microcontroller. The processor 802 is configured to execute instructions stored in the memory, so as to reduce the influence of terrain changes on the map construction and improve the accuracy of the map construction.
In one embodiment, as also shown in fig. 9, the mapping apparatus 900 includes a memory 901 and a processor 902. The processor 902 is coupled to the memory 901 via a BUS 903. The map building apparatus 900 may also be connected to an external storage 905 for invoking external data via a storage interface 904, and may also be connected to a network or another computer system (not shown) via a network interface 906. And will not be described in detail herein.
In the embodiment, the data instructions are stored in the memory, and the instructions are processed by the processor, so that the influence of terrain change on map construction can be reduced, and the accuracy of map construction is improved.
In another embodiment, a computer-readable storage medium has stored thereon computer program instructions which, when executed by a processor, implement the steps of the method in the corresponding embodiment of the mapping method. As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, apparatus, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable non-transitory storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
A schematic diagram of one embodiment of the unmanned device of the present disclosure is shown in fig. 10. The map building apparatus 1001 may be any of the map building apparatuses mentioned above. The IMU 1002 can acquire the pitch angle variation of the unmanned device in real time. The point cloud detection device 1003 may be a three-dimensional laser detector, and may be capable of detecting an environment where the unmanned device is located and collecting point cloud data.
The unmanned equipment can correct the point cloud data by using the pitch angle change value detected by the IMU, and map construction is performed by using the corrected point cloud data, so that the influence of the terrain change on the map construction is reduced, and the accuracy of the map construction is improved.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
Thus far, the present disclosure has been described in detail. Some details that are well known in the art have not been described in order to avoid obscuring the concepts of the present disclosure. It will be fully apparent to those skilled in the art from the foregoing description how to practice the presently disclosed embodiments.
The methods and apparatus of the present disclosure may be implemented in a number of ways. For example, the methods and apparatus of the present disclosure may be implemented by software, hardware, firmware, or any combination of software, hardware, and firmware. The above-described order for the steps of the method is for illustration only, and the steps of the method of the present disclosure are not limited to the order specifically described above unless specifically stated otherwise. Further, in some embodiments, the present disclosure may also be embodied as programs recorded in a recording medium, the programs including machine-readable instructions for implementing the methods according to the present disclosure. Thus, the present disclosure also covers a recording medium storing a program for executing the method according to the present disclosure.
Finally, it should be noted that: the above examples are intended only to illustrate the technical solutions of the present disclosure and not to limit them; although the present disclosure has been described in detail with reference to preferred embodiments, those of ordinary skill in the art will understand that: modifications to the specific embodiments of the disclosure or equivalent substitutions for parts of the technical features may still be made; all such modifications are intended to be included within the scope of the claims of this disclosure without departing from the spirit thereof.
Claims (14)
1. A map construction method, comprising:
acquiring point cloud data of an environment where the unmanned equipment is located;
determining a pitch angle change value of the unmanned equipment by using an Inertial Measurement Unit (IMU);
correcting the rotation angle of the point cloud data according to the pitch angle change value;
and constructing a map according to the corrected point cloud data.
2. The method of claim 1, wherein the correcting the rotation angle of the point cloud data comprises:
under the condition that the pitch angle change value of the IMU in a preset mapping period exceeds a preset threshold value, rotating the point cloud data according to the pitch angle change value;
not rotating the point cloud data if a change in pitch angle of the IMU over a predetermined mapping period does not exceed a predetermined threshold.
3. The method of claim 1, wherein the point cloud data is acquired using a three-dimensional laser detector from a distance sensor based instant positioning and mapping SLAM algorithm.
4. The method of claim 1, wherein,
and the pitch angle change value is obtained by counting the pitch angle change condition of the unmanned equipment in a preset mapping period by the IMU.
5. The method of claim 1, wherein the mapping from the corrected point cloud data comprises:
calculating the roughness of points in the point cloud in the same scanning plane, selecting the points with the maximum roughness and the minimum roughness as a feature point alternative set, taking the points with the maximum roughness as angular points, and taking the points with the minimum roughness as plane points;
determining a line or a surface to which a point in the point cloud belongs through feature point matching;
determining the pose change condition of the unmanned equipment according to the line or the plane to which the point belongs;
and starting map construction according to the pose change condition and the point cloud data, or continuing to construct a map on the basis of the map constructed in the previous map construction period.
6. A map building apparatus comprising:
the point cloud data acquisition module is configured to acquire point cloud data of an environment where the unmanned equipment is located;
a pitch angle change value determination module configured to determine a pitch angle change value of the drone with an Inertial Measurement Unit (IMU);
a point cloud correction module configured to correct a rotation angle of the point cloud data according to the pitch angle variation value;
and the map building module is configured to build a map according to the corrected point cloud data.
7. The apparatus of claim 6, wherein the point cloud correction module is configured to:
under the condition that the pitch angle change value of the IMU in a preset mapping period exceeds a preset threshold value, rotating the point cloud data according to the pitch angle change value;
not rotating the point cloud data if a change in pitch angle of the IMU over a predetermined mapping period does not exceed a predetermined threshold.
8. The apparatus of claim 6, wherein the point cloud data is acquired using a three-dimensional laser detector from a distance sensor based instant positioning and mapping SLAM algorithm.
9. The apparatus of claim 6, wherein,
and the pitch angle change value is obtained by counting the pitch angle change condition of the unmanned equipment in a preset mapping period by the IMU.
10. The apparatus of claim 6, wherein the mapping module comprises:
the characteristic point determining unit is configured to calculate the roughness of points in point clouds in the same scanning plane, select the points with the maximum roughness and the minimum roughness as a characteristic point alternative set, use the points with the maximum roughness as angular points and use the points with the minimum roughness as plane points;
a feature point matching unit configured to determine a line or a plane to which a point in the point cloud belongs by feature point matching;
a pose change determination unit configured to determine a pose change situation of the unmanned aerial vehicle according to a line or a plane to which the point belongs;
and the map generation unit is configured to start map construction according to the pose change condition and the point cloud data or continue map construction on the basis of a map constructed in the last map construction period.
11. A map building apparatus comprising:
a memory; and
a processor coupled to the memory, the processor configured to perform the method of any of claims 1-5 based on instructions stored in the memory.
12. A computer readable storage medium having stored thereon computer program instructions which, when executed by a processor, implement the steps of the method of any one of claims 1 to 5.
13. An unmanned device comprising:
the map building apparatus of any one of claims 6 to 11;
the inertial measurement unit IMU is configured to acquire the pitch angle change condition of the unmanned equipment in real time; and the combination of (a) and (b),
and the point cloud detection device is configured to detect the environment in which the unmanned equipment is positioned and collect point cloud data.
14. The apparatus of claim 13, wherein the point cloud detection apparatus comprises a three-dimensional laser detector.
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