Measurement method, system and device, and computer-readable storage medium

Abstract

The present disclosure provides a measurement method. The measurement method is implemented by using a measurement system, the measurement system comprises a navigation module and a laser ranging module, and the navigation module comprises an inertial measurement unit. The measurement method comprises: acquiring measurement information of a point to be measured, the measurement information comprising sensor pose information of the inertial measurement unit and laser ranging information of the laser ranging module; calculating a position measurement result of said point on the basis of the measurement information of said point, wherein the sensor pose information is acquired by using the following steps: constructing a pre-integration factor on the basis of sensor parameters; and when an RTK signal quality parameter of the navigation module corresponding to the measurement position is less than a preset signal quality threshold, performing first optimization processing on the basis of a lidar odometry factor and the pre-integration factor to obtain the sensor pose information. According to the measurement method, accurate measurement can be performed in scenarios where the signal quality of a navigation module is poor or even blocked, thereby breaking through the limitations of application scenarios of measurement devices.

Description

Measurement methods, systems, equipment and computer-readable storage media

[0001] Cross-reference to related applications

[0002] This disclosure claims priority to Chinese Patent Application No. 202411196584.4, filed on August 29, 2024, entitled "Measuring Method, System, Apparatus and Computer-Readable Storage Medium", the entire contents of which are incorporated herein by reference. Technical Field

[0003] This disclosure relates to the field of positioning technology, and in particular to a measurement method, system, device and computer-readable storage medium. Background Technology

[0004] In the field of engineering surveying and mapping, the use of inertial navigation RTK (Real-time kinematic) receivers for high-precision measurements in scenarios with good satellite signals has been widely applied and the technology is mature.

[0005] However, in scenarios where satellite signal quality is poor or even obstructed, the receiver may become unusable due to the inability to fix RTK in place. In particular, RTK technology still has limitations in large-scale satellite signal denial scenarios such as tunnels and mines.

[0006] Therefore, there is an urgent need for a high-precision measurement method suitable for application scenarios with poor satellite signal quality.

[0007] It should be noted that the information disclosed in the background section above is only used to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0008] Based on this, the present disclosure provides a measurement method, system, device, and computer-readable storage medium that can perform accurate measurements even in scenarios where the navigation module signal quality is poor or even blocked, thus overcoming the limitations of the application scenarios of measurement devices.

[0009] This disclosure provides a measurement method according to some embodiments, implemented using a measurement system. The measurement system includes a navigation module and a laser ranging module. The navigation module includes an inertial measurement unit. The method includes the following steps:

[0010] Acquire measurement information of the point to be measured, the measurement information including sensor pose information of the inertial measurement unit and laser ranging information of the laser ranging module;

[0011] The position measurement result of the point to be measured is calculated based on the measurement information of the point to be measured.

[0012] The sensor pose information is obtained through the following steps:

[0013] Construct a pre-integration factor based on sensor parameters;

[0014] When the RTK signal quality parameter of the navigation module corresponding to the measurement position is less than the preset signal quality threshold, the sensor pose information is obtained by performing a first optimization process based on the lidar odometry factor and the pre-integration factor.

[0015] In some embodiments, before performing the first optimization process based on the lidar odometry factor and the pre-integration factor to obtain the sensor pose information, the method further includes constructing the lidar odometry factor using the following steps:

[0016] Initial point cloud data is acquired through the laser ranging module;

[0017] Motion distortion correction is performed on the initial point cloud data to obtain the target point cloud data;

[0018] Based on the target point cloud data, feature extraction and feature matching are performed to construct the lidar odometry factor.

[0019] In some embodiments, the step of extracting and matching features based on the target point cloud data to construct the lidar odometry factor includes the following steps:

[0020] Select keyframe point cloud data from the target point cloud data;

[0021] The keyframe point cloud data is subjected to feature extraction and feature matching to construct the lidar odometry factor.

[0022] In some embodiments, the second optimization process is performed based on the lidar odometry factor, the pre-integration factor, and the closed-loop factor;

[0023] The closed-loop factor is constructed using the following steps:

[0024] The initial point cloud data is matched with the global point cloud data to construct the closed-loop factor.

[0025] In some embodiments, the step of extracting and matching features based on the target point cloud data to construct the lidar odometry factor includes the following steps:

[0026] Feature extraction stage: Based on the target point cloud data, the local curvature of the laser points in each frame of the target point cloud data is calculated. The laser points in the scan are sorted according to the local curvature to determine edge feature points and planar feature points, which are used as the extracted features.

[0027] Feature matching stage: Using the sensor pose increment information, the laser points of the target point cloud data in the k-th frame are projected into the laser coordinate system corresponding to the (k+1)-th frame; edge feature points are found from the laser points of the target point cloud data in the (k+1)-th frame to match the edge lines in the laser points of the target point cloud data in the k-th frame; planar feature points are found from the laser points of the target point cloud data in the (k+1)-th frame to match the planar blocks in the laser points of the target point cloud data in the k-th frame; where k is an integer greater than or equal to 1;

[0028] The lidar odometry factor construction stage involves: constructing minimum constraints using corresponding features, including distances between edge feature points and distances between planar feature points; constructing motion constraint equations between two consecutive frames of the target point cloud data based on the distances between edge feature points and the distances between planar feature points; and constructing lidar odometry factors based on the motion constraint equations.

[0029] In some embodiments, the sensor pose information is further obtained using the following steps:

[0030] The pre-integration factor is constructed based on the sensor parameters;

[0031] When the RTK signal quality parameter of the navigation module corresponding to the measurement position is greater than or equal to the preset signal quality threshold, a second optimization process is performed based on the absolute position factor and the pre-integration factor to obtain the sensor pose information.

[0032] In some embodiments, before acquiring the measurement information of the point to be measured, the measurement method further includes:

[0033] The measurement system is initialized starting from a given initial position.

[0034] This disclosure also provides a measurement system according to some embodiments for implementing the measurement method provided in the foregoing embodiments, the measurement system comprising:

[0035] The navigation module includes an inertial measurement unit, an RTK measurement system, and a lidar, and is used to provide sensor pose information;

[0036] The laser ranging module is used to collect laser ranging information;

[0037] A data processing module, connected to the navigation module and the laser ranging module, is used to acquire measurement information of the point to be measured, the measurement information including the sensor pose information and the laser ranging information, and to calculate the position measurement result of the point to be measured based on the measurement information of the point to be measured;

[0038] The data processing module is configured to acquire the sensor pose information using the following steps:

[0039] Construct a pre-integration factor based on sensor parameters;

[0040] When the RTK signal quality parameter of the navigation module corresponding to the measurement position is less than the preset signal quality threshold, the sensor pose information is obtained by performing a first optimization process based on the lidar odometer factor and the pre-integration factor.

[0041] This disclosure also provides a measuring device according to some embodiments, including:

[0042] processor;

[0043] A memory in which executable instructions of the processor are stored;

[0044] The processor is configured to execute the steps of the measurement method provided in the foregoing embodiments by executing the executable instructions.

[0045] This disclosure also provides, according to some embodiments, a computer-readable storage medium for storing a program that, when executed by a processor, implements the steps of the measurement method provided in the foregoing embodiments.

[0046] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0047] The embodiments disclosed herein may have, or at least have, the following advantages:

[0048] In this embodiment of the disclosure, by acquiring the measurement information of the point to be measured and calculating the position measurement result of the point to be measured based on the measurement information, the real-time acquisition and processing of the measurement information of the point to be measured is realized, avoiding the complex data processing steps required in traditional measurement methods, and improving measurement efficiency.

[0049] In scenarios where the navigation module signal quality is poor or even obstructed, the RTK signal quality parameters of the navigation module corresponding to the measurement location are less than a preset signal quality threshold. In this case, the present invention can perform a first optimization process based on the lidar odometry factor and pre-integration factor to obtain accurate and reliable sensor pose information for calculating the position measurement results of the point to be measured. The present invention can perform accurate measurements even in scenarios where the navigation module RTK signal quality is poor or even obstructed, overcoming the limitations of the measurement equipment's application scenarios. For example, the present invention is applicable to large-scale navigation module signal rejection scenarios such as tunnels and mines.

[0050] Other advantages, objectives, and features of this disclosure will be set forth in part in the description which follows, and in part will be apparent to those skilled in the art from the following examination and study, or may be learned from practice of this disclosure. The objectives and other advantages of this disclosure may be realized and obtained through the following description. Attached Figure Description

[0051] Other features, objects, and advantages of this disclosure will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings.

[0052] Figure 1 is a structural block diagram of a measurement system provided in some embodiments of this disclosure;

[0053] Figure 2 is a flowchart illustrating the measurement method provided in some embodiments of this disclosure;

[0054] Figure 3 is a schematic flowchart of the measurement method for obtaining sensor pose information in some embodiments of this disclosure;

[0055] Figure 4 is a schematic diagram of the structure of a measurement system provided in some embodiments of this disclosure;

[0056] Figure 5 is a schematic flowchart illustrating the measurement method for obtaining position measurement results provided in some embodiments of this disclosure;

[0057] Figure 6 is a schematic flowchart of the measurement method for constructing lidar odometry factors in some embodiments of this disclosure;

[0058] Figure 7 is a schematic diagram of the measurement environment when the measurement system provided in some embodiments of this disclosure measures multiple points to be measured;

[0059] Figure 8 is a schematic diagram of the structure of a measuring device provided in some embodiments of this disclosure;

[0060] Figure 9 is a schematic diagram of the structure of a computer-readable storage medium provided in some embodiments of this disclosure.

[0061] Explanation of reference numerals in the attached diagram: M100, navigation module; U1, inertial measurement unit; M200, laser ranging module; M300, data processing module; 1, first integrated navigation system; 2, second integrated navigation system; 3, laser ranging module; 4, data processing module. Detailed Implementation

[0062] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that this disclosure will be more comprehensive and complete, and will fully convey the concept of the exemplary embodiments to those skilled in the art. The described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.

[0063] Unless otherwise defined, the technical or scientific terms used in this disclosure shall have the ordinary meaning understood by one of ordinary skill in the art to which this disclosure pertains. The terms “first,” “second,” and similar terms used in this disclosure do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Similarly, the terms “an,” “a,” or “the,” and similar terms do not indicate a quantity limitation, but rather indicate the presence of at least one. The terms “comprising,” “including,” or “including,” and similar terms mean that the element or object preceding the term encompasses the elements or objects listed following the term and their equivalents, without excluding other elements or objects. The terms “connected,” “linked,” or “connected,” and similar terms are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect.

[0064] Furthermore, the accompanying drawings are merely illustrative of this disclosure and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities may be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor devices and / or microcontroller devices.

[0065] To address the shortcomings of related technologies, this disclosure provides a measurement method, system, device, and computer-readable storage medium that enables accurate measurements even in scenarios with poor or obstructed navigation module signals, overcoming the limitations of measurement equipment application scenarios. Details will be elaborated in subsequent embodiments.

[0066] This disclosure provides a measurement method according to some embodiments. The measurement method is implemented using, for example, the measurement system shown in FIG1. ​​The measurement system may specifically include a navigation module M100 and a laser ranging module M200. The navigation module M100 includes an inertial measurement unit U1.

[0067] Please refer to Figure 2. The measurement method may specifically include the following steps S100 to S200:

[0068] S100: Acquire measurement information of the point to be measured. The measurement information includes the sensor pose information of the inertial measurement unit (IMU sensor) U1 and the laser ranging information of the laser ranging module M200.

[0069] S200: The position measurement result of the point to be measured is calculated based on the measurement information of the point to be measured.

[0070] As an example, the position measurement results obtained by this measurement method can be specifically expressed as three-dimensional absolute coordinates, but are not limited to this.

[0071] Specifically, the sensor pose information in step S100 can be obtained using steps S110 to S120 as shown in Figure 3:

[0072] S110: Construct a pre-integration factor based on sensor parameters.

[0073] Among them, the pre-integration factor refers to the IMU pre-integration residual factor constructed by the IMU sensor, and can also be called the IMU pre-integration residual factor.

[0074] S120: When the RTK signal quality parameter of the navigation module corresponding to the measurement position is less than the preset signal quality threshold, the first optimization process is performed based on the lidar odometer factor and the pre-integration factor (IMU pre-integration residual factor) to obtain the sensor pose information.

[0075] The lidar odometer factor can also be called the lidar odometer residual factor. It will be referred to as the lidar odometer (residual) factor in the following text.

[0076] In this embodiment of the disclosure, by acquiring the measurement information of the point to be measured and calculating the position measurement result of the point to be measured based on the measurement information, the real-time acquisition and processing of the measurement information of the point to be measured is realized, avoiding the complex data processing steps required in traditional measurement methods, and improving measurement efficiency.

[0077] In scenarios where the navigation module signal quality is poor or even obstructed, the RTK signal quality parameters of the navigation module corresponding to the measurement location are less than a preset signal quality threshold. In this case, the present invention can perform a first optimization process based on the lidar odometry (residual) factor and the pre-integration factor (IMU pre-integration residual factor) to obtain accurate and reliable sensor pose information for use in calculating the position measurement results of the point to be measured. The present invention can perform accurate measurements even in scenarios where the navigation module signal quality is poor or even obstructed, overcoming the limitations of the measurement equipment's application scenarios. For example, this disclosure is applicable to large-scale navigation module signal rejection scenarios such as tunnels and mines.

[0078] In addition, the sensor pose information in step S100 can also be obtained using the following steps:

[0079] A pre-integration factor (IMU pre-integration residual factor) is constructed based on the sensor parameters. When the RTK signal quality parameter of the navigation module corresponding to the measurement position is greater than or equal to the preset signal quality threshold, a second optimization process is performed based on the absolute position factor and the pre-integration factor (IMU pre-integration residual factor) to obtain the sensor pose information.

[0080] The absolute position factor is also known as the RTK factor, or RTK residual factor, and will be referred to as the RTK (residual) factor in the following text. The RTK residual factor and the lidar odometer residual factor can be collectively referred to as the residual factor.

[0081] In the above measurement method, under conditions of good navigation module signal quality (e.g., open areas), the RTK signal quality parameter of the navigation module corresponding to the measurement location is greater than or equal to a preset signal quality threshold. In this case, a second optimization process can be adaptively performed based on the RTK (residual) factor and the pre-integration factor (IMU pre-integration residual factor) to obtain accurate and reliable sensor pose information for use in calculating the position measurement results of the point to be measured. This measurement method can cope with complex and changing measurement environments. By adaptively switching between the first and second optimization processes, it ensures that high-precision sensor pose information can be obtained under various measurement environments. For example, this measurement method is suitable for seamless indoor and outdoor measurement needs.

[0082] For example, the measurement system may include an RTK receiver, and an example structure of the measurement system is shown in the figure. As shown in Figure 4, the measurement system may include a first integrated navigation system 1, a second integrated navigation system 2, a laser ranging module 3, and a data processing module 4.

[0083] The first integrated navigation system 1 in Figure 4 can also be referred to as a LI-SLAM system. The first integrated navigation system 1 may include, for example, a lidar, an IMU sensor U1 as shown in Figure 1, a front-end odometer, and a sliding window optimizer.

[0084] The second integrated navigation system 2 in Figure 4 can also be referred to as an RTK / IMU integrated navigation system. The second integrated navigation system 2 may include, for example, an RTK measurement system, an IMU sensor U1 as shown in Figure 1, and an optimizer.

[0085] The laser ranging module (Laser) 3 in Figure 4 can be, for example, the laser ranging module M200 shown in Figure 1. The laser ranging module M200 can include, but is not limited to, a millimeter-level laser rangefinder. The laser ranging module 3 can be fixedly connected to the RTK receiver antenna, and its geometric relationship with the antenna's phase center is determined. The laser ranging module 3 can illuminate a target point in space with a laser beam, acquire the carrier phase observation value of the laser beam through a reflected light sensor, and process the data to obtain the laser ranging information between the target point and the laser emission point.

[0086] The data processing module 4 in Figure 4 can be, for example, the data processing module M300 shown in Figure 1. The data processing module 4 can select sensor pose information from the LI-SLAM system or the RTK / IMU integrated navigation system based on the signal quality of the measurement environment, and combine this with the laser ranging information from the laser ranging module M200 to obtain the position measurement result of the point to be measured, thus completing the point measurement.

[0087] The embodiments disclosed herein effectively integrate LiDAR, RTK, and IMU. In the face of complex and ever-changing measurement environments, it can adaptively switch between the LI-SLAM system and the RTK / IMU integrated navigation system, thereby achieving continuous and reliable acquisition of sensor pose information in indoor and outdoor scenarios.

[0088] In some embodiments, before obtaining the measurement information of the point to be measured in step S100, the measurement method may also include an initialization step, such as initializing the measurement system with a starting position as the starting point.

[0089] For example, the measurement system is started in an open area with good navigation module signal quality. The receiver is configured to access RTK differential data to obtain high-precision RTK position and velocity information, and the local geographic coordinate system n is initialized using any starting position p0 as the starting point. Based on this, the pose information of any sensor can be represented as p n and Where, p n This represents the projection of the displacement vector from the initial position in the n-frame to the current IMU position onto the n-frame. This represents the coordinate transformation matrix from the local geographic coordinate system n to the IMU carrier coordinate system b.

[0090] After that, continue to initialize the RTK / IM U integrated navigation system in an open area with good navigation module signal quality, for example, by hand-holding a test device to complete the initialization.

[0091] The following description, in conjunction with Figure 5, illustrates the specific manifestation of step S110, which constructs the pre-integration factor (IMU pre-integration residual factor) based on sensor parameters during the process of obtaining measurement information of the point to be measured in step S100.

[0092] As an example, by continuously extrapolating from the high-frequency angular velocity and acceleration data of the IMU sensor, the IMU's position, velocity, and attitude information at any given moment can be obtained. This "any given moment" can be, for example, the fixed solution moment of an RTK, the start moment of a LiDAR frame, the moment the laser rangefinder acquires data, and so on.

[0093] It is understandable that the RTK measurement system, lidar, IMU sensor U1, and laser ranging module M200 can all be equipped with the same time reference based on pulses per second (PPS) signals. The lever vector and rotation relationship between different sensors can be calibrated through corresponding pre-measurement and calibration methods.

[0094] The IMU position, velocity, and attitude information for the next moment can be recursively obtained from the angular velocity and acceleration data acquired from the IMU sensor using the following formula:

[0095] In the formula, p, v, and q represent the position, velocity, and attitude of the IMU, respectively, with attitude represented by quaternions; k and k+1 represent time k and time k+1, respectively; Δt k This represents the time interval between time k and time k+1; a t and ω t These are acceleration and angular velocity data acquired from the IMU sensor, respectively. and These represent the zero bias of the accelerometer and gyroscope, respectively; g n This represents the gravity vector in the n-system. Represents the time corresponding to time t Ω = (LuX); other symbols are the same as above.

[0096] The above formulas can be used to obtain the IMU position, velocity, and attitude information at the IMU measurement data acquisition time, RTK fixed solution time, lidar frame start time, and laser rangefinder acquisition time.

[0097] For example, this can be achieved by multiplying each side of the above equation. The pre-integration factor (IMU pre-integration residual factor) is obtained:

[0098] Therefore, we can conclude that:

[0099] In the formula, This is the pre-integration factor (IMU pre-integration residual factor). The pre-integration factor (IMU pre-integration residual factor) is only related to the acceleration data, angular velocity data, and zero bias of the IMU sensor at different times.

[0100] For example, for consecutive k frames and k+1 frames, the pre-integration residual can be expressed as the following formula:

[0101] In the formula, r Pre X represents the pre-integration residual; X represents the state variables to be optimized, including the IMU's position, velocity, attitude, accelerometer bias, and gyroscope bias. Indicates the amount of observation.

[0102] It is understandable that in the first optimization process, the pre-integration factor (IMU pre-integration residual factor) provides constraints on the changes in position, velocity, and attitude between frame k and frame k+1, while the pre-integration residual is the error between these constraints and the actual measured values. In the first optimization process, by minimizing the pre-integration residual, the sensor pose information can be optimally estimated, thereby improving the positioning accuracy and reliability of the measurement method.

[0103] The embodiments of this disclosure can adaptively select either a LI-SLAM system or an RTK / IMU integrated navigation system to construct a factor graph based on the signal quality of the measurement environment, and then perform optimization processing to obtain sensor pose information.

[0104] The following description uses an area with good signal quality as the measurement environment to illustrate some embodiments of this disclosure.

[0105] In scenarios with good navigation module signal quality (such as open areas), RTK can obtain high-precision position and velocity information. For example, the RTK position and velocity residuals can be expressed using the following formula:

[0106] In the formula, r RTK Represents the residual values ​​for RTK position and velocity; This represents the projection of the lever vector from the IMU center to the RTK antenna phase center into the IMU carrier coordinate system.

[0107] As shown in Figure 5, by adding the aforementioned pre-integration factor (IMU pre-integration residual factor) and RTK factor to the optimizer for a second optimization process, the sensor pose information can be obtained. The optimizer can include, but is not limited to, the Ceres optimizer.

[0108] The following description uses an area with poor signal quality as the measurement environment to illustrate some embodiments of this disclosure.

[0109] For example, when the navigation module signal quality begins to degrade but the RTK remains fixed, the LI-SLAM system can be activated, and initial pose information can be obtained from the RTK / IMU integrated navigation system to initialize the LI-SLAM system. The LI-SLAM system can then perform measurements even in scenarios where the navigation module signal quality is poor or even obstructed.

[0110] Referring to Figure 6, in some embodiments, before the first optimization process based on the lidar odometry (residual) factor and the pre-integration factor (IMU pre-integration residual factor) to obtain the sensor pose information, the following steps S121 to S123 are used to construct the lidar odometry (residual) factor:

[0111] S121: Acquire initial point cloud data via laser ranging module M200.

[0112] S122: Perform motion distortion correction on the initial point cloud data to obtain the target point cloud data.

[0113] S123: Based on target point cloud data, perform feature extraction and feature matching to construct the lidar odometry (residual) factor.

[0114] In step S121, initial point cloud data can be acquired using a lidar; the initial point cloud data can also be called lidar point cloud data.

[0115] For example, the initial pose information of the laser in a frame and the pose increment information between the start and end times of the frame can be recursively obtained from the angular velocity data and acceleration data collected by the IMU sensor.

[0116] For example, during the process of acquiring a frame of laser point cloud data by the lidar, due to the movement of the device, the laser point cloud data in the same frame needs to undergo motion distortion correction, which is step S122. The motion distortion correction in step S122 can be performed based on the aforementioned initial pose information and pose increment information recursively derived from the observation data of the IMU sensor, and the pose information at that time of the frame recursively derived by the IMU sensor is used as the initial pose information of the lidar.

[0117] Steps S122 and S123 employ a local point cloud matching method, for example, by using the aforementioned initial pose information and pose increment information recursively derived from IMU sensor observation data to perform motion distortion correction, feature extraction, and feature matching.

[0118] In some embodiments, the front-end odometry of the LI-SLAM system can be used to perform the above steps S122 and S123. Since global point cloud matching is not required, but local point cloud matching is used, point cloud map construction and post-processing target point extraction are no longer necessary, which effectively simplifies the real-time processing flow, helps to save processor resource overhead and power consumption, and simplifies the processing flow. Therefore, the above measurement method has lower requirements for hardware computing power.

[0119] For example, the aforementioned local point cloud refers to a point cloud whose quantity is only sufficient to meet the coordinate recursion requirements at the centimeter level.

[0120] As shown in Figure 5, the measurement method provided in the above embodiments can construct a lidar odometry (residual) factor, which, together with the IMU pre-integration factor (IMU pre-integration residual factor), is added to a sliding window optimizer for first optimization processing to accurately obtain sensor pose information. For example, the measurement method provided in the above embodiments can be continuously optimized by the LI-SLAM system in scenarios where RTK cannot be fixed to obtain IMU sensor pose information.

[0121] In some embodiments, the above step S123, which involves feature extraction and feature matching based on target point cloud data to construct a lidar odometry (residual) factor, may include the following steps:

[0122] Select keyframe point cloud data from the target point cloud data;

[0123] Feature extraction and feature matching are performed on keyframe point cloud data to construct the lidar odometry (residual) factor.

[0124] In the above embodiments, target point cloud data determined to be laser keyframes can be selected as keyframe point cloud data, and feature extraction can be performed based on this keyframe point cloud data. For example, keyframes can be determined based on pose changes between frames, while features of non-keyframes are discarded.

[0125] It should be noted that, in the above embodiments, the consecutive k-frame times and k+1-frame times in the aforementioned formula can specifically be the consecutive k-frame times and k+1-frame times of laser keyframes.

[0126] In some embodiments, the above step S123, which involves feature extraction and feature matching based on target point cloud data to construct a lidar odometry (residual) factor, can be specifically represented by the following feature extraction stage, feature matching stage, and lidar odometry (residual) factor construction stage.

[0127] In the feature extraction stage: Based on the target point cloud data, the local curvature of the laser points in each frame of the target point cloud data is calculated. After the laser points in the scan are sorted according to the local curvature, edge feature points and planar feature points are determined as the extracted features.

[0128] Specifically, the local curvature of the laser points in each frame of target point cloud data can be calculated using the following formula:

[0129] In the formula, c represents the local curvature; P k Let i,j∈P be the laser points in the target point cloud data of the k-th frame. k S represents the P returned in a single scan by the lidar. k,i A continuous set of points, such as 8, 16, 64, etc.

[0130] The laser points in the scan are sorted according to the local curvature c to determine the edge feature points and planar feature points.

[0131] In the feature matching stage: using the sensor pose increment information, the laser points of the target point cloud data in frame k are projected onto the laser coordinate system corresponding to frame k+1; edge feature points are found from the laser points of the target point cloud data in frame k+1 and matched with the edge lines in the laser points of the target point cloud data in frame k; planar feature points are found from the laser points of the target point cloud data in frame k+1 and matched with the planar blocks in the laser points of the target point cloud data in frame k. Here, k is an integer greater than or equal to 1.

[0132] Specifically, assume P k Let P be the point set of the target point cloud data in the k-th frame. k+1 For the point set of the target point cloud data in the (k+1)th frame, the sensor pose increment information can be used to transform P k Projected onto P k+1 In the laser coordinate system corresponding to the frame, then from P k+1 Finding edge feature points and P k Edge line matching in P k+1 Find planar feature points and P k Planar block matching in.

[0133] For example, P k+1 For a certain edge feature point i in P, k Select the point j closest to i in P, and in P k If the nearest point k in the scan line adjacent to point j is a given point, then points j and k constitute the edge line corresponding to edge feature point i. k+1 A certain feature point i in a plane, in P k Select the point j closest to i, and the points l and m closest to point j in the same scan beam and adjacent scan beam respectively. Then, points j, l, and m constitute the planar block corresponding to the planar feature point i.

[0134] In the stage of constructing the lidar odometry (residual) factor: the minimum constraints are constructed using the corresponding features, including the distance between edge feature points and the distance between planar feature points; the motion constraint equation between two consecutive frames of target point cloud data is constructed based on the distance between edge feature points and the distance between planar feature points; and the lidar odometry (residual) factor is constructed based on the motion constraint equation.

[0135] Specifically, after the feature matching stage is completed, the minimum constraints can be constructed using the corresponding features. The minimum constraints are divided into the distance d1 between edge feature points and the distance d2 between planar feature points.

[0136] Distance between edge feature points

[0137] In the formula, i∈P k+1 j, l are the corresponding edge lines, j, l∈P k Other symbols are the same as above.

[0138] Distance between planar feature points

[0139] In the formula, i∈P k+1 j, l, m are the corresponding planar blocks, j, l, m ∈ P k Other symbols are the same as above.

[0140] Based on the above, the motion constraint equation between two consecutive frames of target point cloud data can be constructed based on the distance d1 between edge feature points and the distance d2 between planar feature points as follows: f1(P k+1,i ,T k+1 )=d1,f2(P k+1,i ,T k+1 )=d2

[0141] In the formula, T k+1 If the pose is between consecutive k frames and k+1 frames, then the lidar matching residual can be expressed as:

[0142] In some embodiments, the first optimization process described in step S120 can be based on the lidar odometry (residual) factor, the pre-integration factor (IMU pre-integration residual factor), and the closed-loop factor.

[0143] For example, the above closed-loop factor can be constructed in the following way:

[0144] The initial point cloud data is matched with the global point cloud data to construct a closed-loop factor.

[0145] During the movement of the measuring device, it may return to a previously visited position, thus forming a closed loop. In the above embodiment, the local point cloud data can be matched with the global point cloud data to determine whether a closed loop has been formed.

[0146] By incorporating the aforementioned pre-integration factor (IMU pre-integration residual factor), lidar odometry (residual) factor, and closed-loop factor into the sliding window optimizer for the first optimization process, the sensor pose information can be obtained. The sliding window optimizer can include, but is not limited to, the G2O optimizer.

[0147] Based on the same inventive concept, this disclosure also provides a measurement system for implementing the measurement method provided in the foregoing embodiments. Therefore, the measurement system can also achieve all the technical effects achievable by the foregoing measurement method, and will not be described in detail here. Furthermore, the solution provided by this measurement system is similar to the solution described in the foregoing measurement method; therefore, the specific limitations in the measurement system embodiments provided below can be found in the foregoing limitations of the measurement method.

[0148] Please continue to refer to Figure 1. In some embodiments, the measurement system may specifically include a navigation module M100, a laser ranging module M200, and a data processing module M300.

[0149] The navigation module M100 includes an inertial measurement unit U1, an RTK measurement system, and a lidar. The navigation module M100 provides sensor pose information. The laser ranging module M200 acquires laser ranging information. The data processing module M300 is connected to the navigation module M100 and the laser ranging module M200. It acquires measurement information of the point to be measured, which may include sensor pose information and laser ranging information. The data processing module M300 also calculates the position measurement result of the point to be measured based on this measurement information.

[0150] Specifically, the data processing module M300 can be configured to acquire sensor pose information using the following steps:

[0151] A pre-integration factor (IMU pre-integration residual factor) is constructed based on the sensor parameters. When the signal quality parameter of the navigation module M100 corresponding to the measurement position is less than the preset signal quality threshold, the first optimization process is performed based on the lidar odometer (residual) factor and the pre-integration factor (IMU pre-integration residual factor) to obtain the sensor pose information.

[0152] The measurement system provided in this disclosure effectively integrates Lidar, RTK, and IMU sensors, and can be designed as an integrated unit. It also incorporates a laser ranging module M200 (e.g., a millimeter-level laser rangefinder) and is mounted in front of the RTK receiver. The system has a high degree of integration and is easy to carry and use by hand.

[0153] To better illustrate the workflow of this measurement system, the following explanation will use the measurement of multiple points A, B, C, and D inside and outside a tunnel as an example, with reference to Figure 7.

[0154] First, the measuring equipment is activated outside the tunnel, and the receiver is configured to access RTK differential data to obtain high-precision RTK position and velocity information. Then, the local geographic coordinate system n is initialized using any starting position p0 as the starting point. After the RTK is fixed, the measuring equipment can complete this initialization process using relevant technologies.

[0155] When measuring point A, the navigation module signal quality is good, and the RTK signal quality parameter of the navigation module is greater than or equal to the preset signal quality threshold. This measurement system can acquire the IMU sensor pose information in real time from the RTK / IMU integrated navigation system. The laser ranging module M200 (e.g., a millimeter-level laser rangefinder) is aligned with point A to measure the laser ranging information between the laser emission point and point A. Based on the aforementioned sensor pose information and the laser ranging information between the laser emission point and point A, the position measurement result of point A can be expressed by the following formula:

[0156] in, This represents the location information of point A in the local geographic coordinate system n. This indicates the position information of the IMU sensor when the laser ranging module M200 measures point A. This represents the coordinate transformation matrix from the IMU carrier coordinate system (b-frame) to the local geographic coordinate system (n-frame) when the laser ranging module M200 measures point A, i.e., the IMU's attitude information. This sensor pose information of the IMU can be acquired in real time by the RTK / IMU integrated navigation system. This represents the projection of the position vector from the IMU center to the laser emission point into the IMU carrier coordinate system b. This represents the projection of the position vector from the laser emission point to the point A under test in the IMU carrier coordinate system b.

[0157] Measurements are being performed on points B, C, and D. At this point, the navigation module signal quality is poor, with the RTK signal quality parameters falling below a preset signal quality threshold. Before the measuring equipment enters the tunnel, the measuring system can activate the LI-SLAM system. The LI-SLAM system recursively acquires sensor pose information in real time, thereby obtaining the pose information required to measure each point, such as the required position of point B. and posture Move the measuring device to the vicinity of the points B, C, and D to be measured, and align the laser ranging module M200 (e.g., a millimeter-level laser rangefinder) with the points B, C, and D in sequence to measure the laser ranging information between the laser emission point and the points B, C, and D.

[0158] Taking point B as an example, the position measurement result of point B can be expressed by the following formula:

[0159] in, This indicates the location information of point B in the local geographic coordinate system n. This indicates the position information of the IMU sensor when the laser ranging module M200 measures point B. This represents the coordinate transformation matrix from the IMU carrier coordinate system (b-system) to the local geographic coordinate system (n-system) when the laser ranging module M200 measures point B. The sensor pose information of this IMU can be acquired in real time by the LI-SLAM system. Same as above. This represents the projection of the position vector from the laser emission point to the point B under test in the IMU carrier coordinate system b.

[0160] Based on the positions of the test points A, B, C, and D in the local geographic coordinate system obtained from the aforementioned steps, as well as the starting position of the RTK in the local geographic coordinate system, the three-dimensional absolute position coordinates of each test point in the geodetic coordinate system can be calculated, thus completing the position measurement.

[0161] As can be seen, the measurement system provided in this embodiment is equipped with a laser ranging module M200 (e.g., a millimeter-level laser rangefinder). Therefore, the surveyor can intuitively obtain the position measurement result of the point to be measured by illuminating the laser ranging module M200 onto the point to be measured. The field operation is simple and does not require complicated indoor processing procedures.

[0162] The measurement systems in the embodiments of this disclosure can all be used to implement the corresponding measurement methods. Therefore, the technical features between the method embodiments and the system embodiments can be substituted and supplemented for each other without conflict, so that those skilled in the art can learn the technical content of this disclosure.

[0163] It should be noted that the measurement system provided in this disclosure is not limited to the specific application scenarios described above. For example, after the measurement system completes initialization, the point to be measured can be various hazardous points, complex points, and indoor points, etc., but it is not limited thereto.

[0164] It is understood that in the measurement system provided in this disclosure, each module can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the operations corresponding to each module.

[0165] Furthermore, the measurement system provided in this disclosure may also include other existing functional modules that support the operation of the measurement system. The measurement system shown in Figure 1 is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0166] Based on the same inventive concept, this disclosure also provides a measuring device, including a processor; a memory storing executable instructions of the processor; wherein the processor is configured to perform the steps of the measuring method described in the foregoing embodiments by executing the executable instructions. Therefore, the measuring device can also achieve all the technical effects achievable by the foregoing measuring method, and will not be described in detail here.

[0167] Those skilled in the art will understand that various aspects of this disclosure can be implemented as a system, method, or program product. Therefore, various aspects of this disclosure can be specifically implemented in the following forms: a completely hardware implementation, a completely software implementation (including firmware, microcode, etc.), or a combination of hardware and software aspects, collectively referred to herein as a "circuit," "module," or "platform."

[0168] The measuring device 600 according to this embodiment of the present disclosure will now be described with reference to FIG8. The measuring device 600 shown in FIG8 is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present disclosure.

[0169] As shown in Figure 8, the measuring device 600 is presented in the form of a general-purpose computing device. The components of the measuring device 600 may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different system components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0170] The storage unit stores program code that can be executed by the processing unit 610, causing the processing unit 610 to perform the steps described in the measurement method section of this specification according to various exemplary embodiments of the present disclosure. For example, the processing unit 610 can perform the steps shown in FIG2.

[0171] The storage unit 620 may include a readable medium in the form of a volatile storage unit, such as a random access memory unit (RAM) 6201 and / or a cache storage unit 6202, and may further include a read-only memory unit (ROM) 6203.

[0172] The storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: an operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0173] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the various bus structures.

[0174] The measuring device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), one or more devices that enable a user to interact with the measuring device 600, and / or any device that enables the measuring device 600 to communicate with one or more other computing devices (e.g., router, modem, etc.). This communication can be performed via input / output (I / O) interface 650. Furthermore, the measuring device 600 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of the measuring device 600 via bus 630. It should be understood that, although not shown in Figure 8, other hardware and / or software modules can be used in conjunction with the measuring device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0175] Based on the same inventive concept, this disclosure also provides a computer-readable storage medium for storing a program that, when executed by a processor, implements the steps of the measurement method described in the foregoing embodiments.

[0176] In some possible implementations, various aspects of this disclosure may also be implemented as a program product comprising program code that, when executed on a terminal device, causes the terminal device to perform the steps described in the measurement method section of this specification according to various exemplary embodiments of this disclosure.

[0177] Referring to Figure 9, a program product 800 for implementing the above-described method according to an embodiment of the present disclosure is depicted. This product may employ a portable compact disc read-only memory (CD-ROM) and include program code, and may be executed on a terminal device, such as a personal computer. However, the program product of the present disclosure is not limited thereto. In this document, the readable storage medium may be any tangible medium containing or storing a program that may be used by or in conjunction with an instruction execution system, apparatus, or device.

[0178] The program product may employ any combination of one or more readable media. A readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of readable storage media (a non-exhaustive list) include: an electrical connection having one or more wires, a portable disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0179] The computer-readable storage medium may include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. The readable storage medium may also be any readable medium other than a readable storage medium, capable of transmitting, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium may be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof.

[0180] Program code for performing the operations of this disclosure can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0181] When the program in the computer storage medium is executed by the processor, it implements the steps of the measurement method. Therefore, the computer storage medium can also achieve the technical effects of the above-mentioned measurement method.

[0182] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features of the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0183] The embodiments described above are merely illustrative of several implementations of this disclosure, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this disclosure, and these all fall within the scope of protection of this disclosure. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A measurement method, characterized in that, The method employs a measurement system, which includes a navigation module and a laser ranging module. The navigation module includes an inertial measurement unit. The method comprises the following steps: Acquire measurement information of the point to be measured, the measurement information including sensor pose information of the inertial measurement unit and laser ranging information of the laser ranging module; The position measurement result of the point to be measured is calculated based on the measurement information of the point to be measured. The sensor pose information is obtained through the following steps: Construct a pre-integration factor based on sensor parameters; When the RTK signal quality parameter of the navigation module corresponding to the measurement position is less than the preset signal quality threshold, the sensor pose information is obtained by performing a first optimization process based on the lidar odometer factor and the pre-integration factor.

2. The measurement method according to claim 1, characterized in that, Before obtaining the sensor pose information by performing the first optimization process based on the lidar odometry factor and the pre-integration factor, the lidar odometry factor is constructed using the following steps: Initial point cloud data is acquired through the laser ranging module; Motion distortion correction is performed on the initial point cloud data to obtain the target point cloud data; Based on the target point cloud data, feature extraction and feature matching are performed to construct the lidar odometry factor.

3. The measurement method according to claim 2, characterized in that, The step of extracting and matching features based on the target point cloud data to construct the lidar odometry factor includes the following steps: Select keyframe point cloud data from the target point cloud data; The keyframe point cloud data is subjected to feature extraction and feature matching to construct the lidar odometry factor.

4. The measurement method according to claim 2, characterized in that, The first optimization process is based on the lidar odometry factor, the pre-integration factor, and the closed-loop factor; The closed-loop factor is constructed using the following steps: The initial point cloud data is matched with the global point cloud data to construct the closed-loop factor.

5. The measurement method according to claim 2, characterized in that, The step of extracting and matching features based on the target point cloud data to construct the lidar odometry factor includes the following steps: Feature extraction stage: Based on the target point cloud data, the local curvature of the laser points in each frame of the target point cloud data is calculated. The laser points in the scan are sorted according to the local curvature to determine edge feature points and planar feature points, which are used as the extracted features. Feature matching stage: Using the sensor pose increment information, the laser points of the target point cloud data in the kth frame are projected into the laser coordinate system corresponding to the k+1th frame; Find the edge feature points from the laser points of the target point cloud data in frame k+1 and match them with the edge lines of the laser points in the target point cloud data in frame k; find the planar feature points from the laser points of the target point cloud data in frame k+1 and match them with the planar blocks of the laser points in the target point cloud data in frame k; where k is an integer greater than or equal to 1; The lidar odometry factor construction stage involves: constructing minimum constraints using corresponding features, including distances between edge feature points and distances between planar feature points; constructing motion constraint equations between two consecutive frames of the target point cloud data based on the distances between edge feature points and the distances between planar feature points; and constructing lidar odometry factors based on the motion constraint equations.

6. The measurement method according to claim 1, characterized in that, The sensor pose information is also obtained through the following steps: The pre-integration factor is constructed based on the sensor parameters; When the RTK signal quality parameter of the navigation module corresponding to the measurement position is greater than or equal to the preset signal quality threshold, a second optimization process is performed based on the absolute position factor and the pre-integration factor to obtain the sensor pose information.

7. The measurement method according to claim 1, characterized in that, Before acquiring the measurement information of the point to be measured, the measurement method further includes: The measurement system is initialized starting from a given initial position.

8. A measurement system, characterized in that, For implementing the measurement method as described in any one of claims 1 to 7, the measurement system comprises: The navigation module includes an inertial measurement unit, an RTK measurement system, and a lidar, and is used to provide sensor pose information; The laser ranging module is used to collect laser ranging information; A data processing module, connected to the navigation module and the laser ranging module, is used to acquire measurement information of the point to be measured, the measurement information including the sensor pose information and the laser ranging information, and to calculate the position measurement result of the point to be measured based on the measurement information of the point to be measured; The data processing module is configured to acquire the sensor pose information using the following steps: Construct a pre-integration factor based on sensor parameters; When the RTK signal quality parameter of the navigation module corresponding to the measurement position is less than the preset signal quality threshold, the sensor pose information is obtained by performing a first optimization process based on the lidar odometer factor and the pre-integration factor.

9. A measuring device, characterized in that, include: processor; A memory in which executable instructions of the processor are stored; The processor is configured to perform the steps of the measurement method according to any one of claims 1 to 7 by executing the executable instructions.

10. A computer-readable storage medium for storing a program, characterized in that, When the program is executed by the processor, it implements the steps of the measurement method according to any one of claims 1 to 7.

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