Laser inertial odometer calculation method and system for weak structure long tunnel environment

Abstract

This invention discloses a laser inertial odometry calculation method for weakly structured long tunnel environments. The method comprises: establishing a prediction equation for the laser inertial odometry system's motion state and a covariance propagation equation for the error state; establishing an observation equation for the reflection intensity residual and the geometric observation residual; fusing the prior distribution of the state variables, the geometric observation residual, and the intensity observation residual to form a maximum a posteriori optimization objective function; and iteratively updating the covariance of the system's motion state and error state based on the maximum a posteriori optimization objective function until convergence, thereby obtaining the optimal system pose. This method utilizes a low-cost sensor combination and fully exploits the laser radar echo reflection intensity information, enhancing the sensor's positioning and mapping accuracy and algorithm robustness in long tunnels and weakly structured scenarios.

Description

Technical Field

[0001] The present invention relates to the field of multi-sensor fusion and synchronous positioning and mapping, and in particular to a laser inertial odometer calculation method and system for a weak-structure long tunnel environment. Background Art

[0002] Lightweight, low-cost measurement sensors (such as LiDAR and IMUs) are increasingly being used in mobile robotic systems, and mobile mapping algorithms based on these sensors (such as LIO-SAM and FastLIO2) have also made rapid progress. However, the spatial sparsity of low-cost LiDAR limits the performance of these mapping algorithms in scenes with weak structural constraints, such as open terrain and tunnels. Furthermore, existing state-of-the-art mobile mapping algorithms typically employ point cloud registration models based on geometric primitives, which struggle to handle the 3D spatial degradation of certain scenes. To address these issues without introducing additional sensors, Coin-LIO and RI-LIO attempt to generate image-like 2D modal data by projecting the echo intensity information of point clouds. These methods then construct observation equations based on the reflection intensity map to provide additional constraints for mobile mapping systems. Unfortunately, these approaches rely on expensive high-beam LiDAR. Summary of the Invention

[0003] To overcome the shortcomings of the above-mentioned existing technologies, the present invention provides a laser inertial odometry calculation method for long tunnel environments with weak structures. By utilizing a low-cost sensor combination and making full use of the laser radar echo reflection intensity information, the method enhances the sensor's positioning and mapping accuracy and algorithm robustness in long tunnels and weak structural constraint scenarios.

[0004] According to one aspect of the present invention, a method for calculating laser inertial odometry in a weak structure long tunnel environment is provided, comprising:

[0005] Establish the prediction equation of the laser inertial odometry system's motion state and the covariance propagation equation of the error state;

[0006] Establish observation equations for reflection intensity residuals and geometric observation residuals;

[0007] The prior distribution of state variables, geometric observation residuals and intensity observation residuals are integrated to form the maximum a posteriori optimization objective function;

[0008] Based on the maximum a posteriori optimization objective function, the covariance of the system motion state and error state is iteratively updated until convergence to obtain the optimal system pose.

[0009] As a further technical solution, after obtaining the optimal system posture, it also includes:

[0010] Based on the obtained optimal system pose, the scanning points are transformed into the initial coordinate system to obtain a point cloud map.

[0011] As a further technical solution, the observation equations of the reflection intensity residual and the geometric observation residual are established, including:

[0012] The intensity map generated by projecting point clouds from several frames is maintained through historical frames. Line detection is performed on the intensity map using the deep learning-based m-LSD algorithm, and the observation equation of the reflection intensity residual is generated through point-line matching.

[0013] The back-propagated scan points in the current frame are transformed into the world coordinate system, and their equations falling on the local plane of the global map are linearized to generate the observation equations of the geometric observation residuals.

[0014] As a further technical solution, the observation equation of the reflection intensity residual is established, which also includes:

[0015] For the current frame point cloud, candidate edge points are screened based on the reflection intensity covariance calculation;

[0016] Match the selected candidate edge points with the straight lines detected by the m-LSD algorithm;

[0017] Construct point-line matching pairs and generate observation equations.

[0018] As a further technical solution, the method further includes:

[0019] The true value of the error state is linearized to update the covariance between the system motion state and the error state.

[0020] According to one aspect of the present invention, a laser inertial odometry calculation system for a weak structure long tunnel environment is provided, comprising:

[0021] The first main module is used to establish the prediction equation of the laser inertial odometry system's motion state and the covariance propagation equation of the error state;

[0022] The second main module is used to establish the observation equation of the reflection intensity residual and the geometric observation residual;

[0023] The third main module is used to fuse the prior distribution of state variables, geometric observation residuals and intensity observation residuals to form the maximum a posteriori optimization objective function;

[0024] The fourth main module is used to iteratively update the covariance of the system motion state and error state based on the maximum a posteriori optimization objective function until convergence to obtain the optimal system posture.

[0025] According to one aspect of the present invention, a laser inertial odometry calculation system for a weak-structure long tunnel environment is provided, comprising a rotating laser radar, an inertial measurement unit, and a processing unit. The processing unit executes the laser inertial odometry calculation method for a weak-structure long tunnel environment based on data collected by the rotating laser radar and the inertial measurement unit.

[0026] According to one aspect of the present invention, a laser inertial odometry calculation system for a weak-structure long tunnel environment is provided, comprising a memory and a processor. The memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the laser inertial odometry calculation method for a weak-structure long tunnel environment.

[0027] According to one aspect of the present invention, a non-transitory computer-readable storage medium is provided, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions enable the computer to execute the laser inertial odometry calculation method for a weak structure long tunnel environment.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention provides a method that, based on a low-cost sensor combination, fully utilizes the laser radar echo reflection intensity information to enhance the sensor's positioning and mapping accuracy and algorithm robustness in long tunnels and weak structural constraint scenarios.

[0030] The present invention provides a system that uses a simple industrial design to fix a low-beam LiDAR on a motor and rotate it accordingly. A robust mobile mapping algorithm is proposed based on the rotating LiDAR and an inertial measurement unit, which fully utilizes the intensity information of the point cloud to overcome degenerate scenarios with weak structural constraints. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief introduction will be given below to the drawings used in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0032] Figure 1 A flow chart of a laser inertial odometry calculation method for a weak structure long tunnel environment provided by an embodiment of the present invention.

[0033] Figure 2 A schematic diagram of the visualization effect of reflection intensity constraint in an outdoor scene provided by an embodiment of the present invention.

[0034] Figure 3A schematic diagram of the visualization effect of reflection intensity constraint in an indoor scene provided by an embodiment of the present invention.

[0035] Figure 4 This is a schematic diagram of the mapping results of a 550-meter-long tunnel provided by one embodiment of the present invention.

[0036] Figure 5 A schematic diagram of a laser inertial odometry calculation system for a weak structure long tunnel environment provided by another embodiment of the present invention.

[0037] Figure 6 A schematic diagram of a laser inertial odometry calculation system for a weak structure long tunnel environment provided by yet another embodiment of the present invention. DETAILED DESCRIPTION

[0038] To address the current situation where existing mobile mapping algorithms perform poorly in scenarios with weak structural constraints, this paper provides a laser inertial odometry calculation method, enabling more robust and accurate mobile mapping, particularly in weakly structured long tunnel environments. This method utilizes a simple industrial design, securing a low-beam LiDAR to a motor for simultaneous rotation (referred to as a rotating LiDAR). This rotating LiDAR not only offers a wider scanning range but also significantly improves mapping efficiency and point cloud density. The mobile mapping algorithm proposed in this paper, based on the rotating LiDAR and an inertial measurement unit, fully utilizes the intensity information of the point cloud to overcome degenerate scenarios with weak structural constraints.

[0039] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, the technical features in the various embodiments or single embodiments provided by the present invention are arbitrarily combined with each other to form a new technical solution. This combination is not restricted by the sequence of steps and / or structural composition mode, but must be based on the ability of ordinary technicians in this field to implement it. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that this combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0040] This embodiment of the present invention provides a method for calculating laser inertial odometry in long tunnels with weak structures. The method involves establishing prediction equations for the system's motion state, covariance propagation equations for the system's error state, observation equations for the reflection intensity residual and geometric registration residual, and updating the system's motion state. This method fully utilizes lidar echo reflection intensity information to enhance the sensor's positioning and mapping accuracy and algorithm robustness in long tunnels and weakly constrained environments.

[0041] See also Figure 1 The laser inertial odometry calculation method for a weak structure long tunnel environment provided by an embodiment of the present invention specifically includes the following steps:

[0042] Step 1: Establish the prediction equation of the laser inertial odometry system motion state and the covariance propagation equation of the error state.

[0043] Before step 1, the odometer system state variable definition is also included, specifically:

[0044] The state variables of the laser inertial odometry system are defined as , the measurement value is defined as , the measured white noise is defined as .

[0045] ,

[0046] in, 、 Represent the state variables of IMU and motor respectively; 、 Represent the measurement values ​​of IMU and motor respectively; 、 Represent the white noise of the IMU and motor measurements respectively; Indicates the rotation from the IMU system to the world system; Represents the translation of the rotation from the IMU system to the world system; Indicates the speed of rotation from the IMU system to the world system; represents the random walk of IMU; Indicates the zero bias of IMU; represents the gravity vector; Indicates the rotation of the LiDAR system to the rotating axis system; Represents the translation of the LiDAR system to the rotation axis system; represents the random walk of the motor; represents the white noise of the motor; 、 、 、 They represent the IMU random walk and zero-bias Gaussian noise respectively; Indicates the rotation of the LiDAR system to the rotating axis system; Represents the translation of the LiDAR system to the rotation axis system; Indicates the motor angle; Indicates the noise at the motor's rotation angle.

[0047] The state popularity here is defined as:

[0048] ,

[0049] in, The dimension is 30.

[0050] In the embodiment of the present invention, the prediction equation of the system motion state is established as follows:

[0051] for The state prediction equation has been defined in the general lidar inertial odometry framework FastLIO2:

[0052] ,

[0053] for First, construct the motion equation to describe the motion relationship between the motor and LiDAR. For a point in the LiDAR coordinate system , which is converted to the motor shaft transition system A by the following formula. Obtained by motor calibration Transformation of the laser radar coordinate system L to A at .

[0054] ,

[0055] in, 、 Represents the rotation and translation bias of LiDAR and motor respectively; y represents the unit vector along the y-axis; Represents the mapping from Lie algebra to Lie group.

[0056] Next, write the state in a transfer form (a form related to the state of the previous frame):

[0057]

[0058] ,

[0059] in, 、 They represent the motor angle at time t and the angle deviation between time t+1 and time t respectively.

[0060] Here you can define , in the actual state prediction process, the following formula is used:

[0061] .

[0062] In the embodiment of the present invention, the covariance propagation equation of the system error state is established as follows:

[0063] for , define the error state variable as:

[0064] ,

[0065] They respectively represent the rotation error and translation error from the IMU system to the world coordinate system, the velocity error from the IMU system to the world coordinate system, the IMU random walk and bias, the gravity error, and the rotation error and translation error from the rotation axis coordinate system to the IMU coordinate system.

[0066] The covariance transfer formula of is defined in FastLIO2 as:

[0067] .

[0068] It should be noted that the meanings of the symbols in the above formula are common in the FastLIO2 framework and will not be repeated here.

[0069] for , define the error state variable as:

[0070] .

[0071] Let's start the derivation The covariance transfer formula for and Derived separately:

[0072] ,

[0073] in, Represents the motor state equation function; Indicates the error of the motor angle; Indicates the rotation from the LiDAR coordinate system to the rotation axis coordinate system.

[0074] According to flow type The partial derivative conclusion of for and Partial derivatives of :

[0075] ,

[0076] in, 、 Both are defined in FastLIO2.

[0077] for , deduced according to the following formula for and Partial derivatives of :

[0078]

[0079] ,

[0080] From the above we can get: .

[0081] Combined with the error state prediction equation of imu, we can get

[0082] .

[0083] Step 2: Establish the observation equation of reflection intensity residual and geometric observation residual.

[0084] In the embodiment of the present invention, the observation equation of the geometric observation residual is established as follows:

[0085] For a back-propagated scan point in the current frame , after taking into account the measurement noise and transforming to the world coordinate system, it should fall on a local plane of the global map, as described by the following formula:

[0086] ,

[0087] In the above formula, the subscript L represents the radar coordinate system, the subscript A represents the motor shaft transition system, the subscript I represents the IMU coordinate system, the subscript G represents the global world system, T represents the transformation matrix, t represents time t, and j represents the scanning point number.

[0088] Linearizing it, we can get:

[0089] .

[0090] The Jacobian matrix of the geometric observation is derived below:

[0091] .

[0092] The residual can be defined as (some notations are simplified):

[0093]

[0094]

[0095] .

[0096] In the embodiment of the present invention, the observation equation of the reflection intensity residual is established as follows:

[0097] An intensity map generated by projecting 150 frames of point cloud is maintained through historical frames, and line detection is performed using the m-LSD algorithm based on deep learning. For the current frame point cloud, candidate reflection edge points are selected by calculating the covariance of the local reflection intensity of the scanning point. Feature matching is performed by the vertical distance between the point and the line to obtain Figure 1 and Figure 2 results.

[0098] Figure 1 and Figure 2 In the figure, the blue line is the straight line detection result of the reflection intensity map, the red point represents the reflection edge point in the current frame that matches the straight line, and the green arrow represents the direction of the reflection intensity constraint.

[0099] For each point-line matching pair in the current frame, the observation equation is formed as follows:

[0100] ,

[0101] In the above formula, the subscript L represents the radar coordinate system, the subscript A represents the motor shaft transition system, the subscript I represents the IMU coordinate system, the subscript G represents the global world system, T represents the transformation matrix, t represents time t, and k represents the scanning point number.

[0102] in, , is the pinhole projection function from point cloud to intensity map. Linearizing it yields:

[0103] .

[0104] The Jacobian matrix of the intensity observation is derived below:

[0105] We abbreviate some symbols:

[0106] .

[0107] According to the chain rule, we can get:

[0108] .

[0109] The partial derivatives of the above formula have been derived in geometric observations, so we can get:

[0110] .

[0111] Step 3: The prior distribution of state variables, geometric observation residuals, and intensity observation residuals are fused to form the maximum a posteriori optimization objective function.

[0112] In order to update the covariance of the error state while updating the state, the true value of the error state is linearized:

[0113] .

[0114] According to flow type The derivation conclusion is:

[0115] ,

[0116] in, , , .

[0117] Will The prior distribution of , geometric observation residuals and intensity observation residuals are fused to form the MAP optimization objective function:

[0118] .

[0119] Defined as follows:

[0120] .

[0121] Update the status as follows:

[0122] .

[0123] Step 4: Based on the maximum a posteriori optimization objective function, the covariance of the system motion state and error state is iteratively updated until convergence to obtain the optimal system posture.

[0124] After each iteration, the state variables and covariance are updated according to the following formula:

[0125] .

[0126] When the iteration converges, the scan points are transformed to the initial coordinate system by estimating the optimal system pose to obtain a point cloud map. The mapping effect is as follows: Figure 3 shown.

[0127] The implementation of each embodiment of the present invention is based on programmed processing performed by a device with processor functionality. Therefore, in practical engineering applications, the technical solutions and functions of each embodiment of the present invention are packaged into various modules. Based on this reality, and in addition to the aforementioned embodiments, an embodiment of the present invention provides a laser inertial odometry calculation system for weak-structure long tunnel environments. This system is used to execute the laser inertial odometry calculation method for weak-structure long tunnel environments described in the aforementioned method embodiments.

[0128] like Figure 6As shown, the system includes: a first main module, which is used to establish the prediction equation of the motion state of the laser inertial odometry system and the covariance propagation equation of the error state; a second main module, which is used to establish the observation equation of the reflection intensity residual and the geometric observation residual; a third main module, which is used to fuse the prior distribution of the state variables, the geometric observation residual and the intensity observation residual to form a maximum a posteriori optimization objective function; a fourth main module, which is used to iteratively update the covariance of the system motion state and the error state based on the maximum a posteriori optimization objective function until convergence, and obtain the optimal system posture.

[0129] The laser inertial odometry calculation system for weak structure long tunnel environment provided by the embodiment of the present invention is aimed at the current situation that the mobile mapping algorithm in the prior art performs poorly in weak structure constraint scenarios. Figure 6 Several modules in the mobile mapping algorithm proposed based on rotating LiDAR and inertial measurement unit make full use of the intensity information of point cloud to overcome the degenerate scenarios with weak structural constraints.

[0130] It should be noted that the system embodiments provided by the present invention are not only used to implement the methods in the above-mentioned method embodiments, but also used to implement the methods in other method embodiments provided by the present invention. The only difference lies in the setting of corresponding functional modules, and its principles are basically the same as the principles of the above-mentioned system embodiments provided by the present invention. As long as those skilled in the art refer to the specific technical solutions in other method embodiments on the basis of the above-mentioned system embodiments, obtain corresponding technical means and technical solutions composed of these technical means by combining technical features, and on the premise of ensuring the practicality of the technical solutions, they will improve the equipment in the above-mentioned system embodiments to obtain corresponding system embodiments for implementing the methods in other method embodiments.

[0131] Based on the same inventive concept as the aforementioned method embodiment, the present invention also provides a laser inertial odometer calculation system for a weak structure long tunnel environment, such as Figure 5 As shown, it includes a rotating laser radar, an inertial measurement unit and a processing unit. The processing unit executes the laser inertial odometry calculation method for a weak structure long tunnel environment based on the data collected by the rotating laser radar and the inertial measurement unit, including the following steps:

[0132] Establish the prediction equation of the laser inertial odometry system's motion state and the covariance propagation equation of the error state;

[0133] Establish observation equations for reflection intensity residuals and geometric observation residuals;

[0134] The prior distribution of state variables, geometric observation residuals and intensity observation residuals are integrated to form the maximum a posteriori optimization objective function;

[0135] Based on the maximum a posteriori optimization objective function, the covariance of the system motion state and error state is iteratively updated until convergence to obtain the optimal system pose.

[0136] Based on the same inventive concept as the aforementioned method embodiment, an embodiment of the present invention further provides a laser inertial odometry calculation system for a weak-structure long tunnel environment, comprising a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the aforementioned laser inertial odometry calculation method for a weak-structure long tunnel environment.

[0137] In an embodiment of the present invention, the memory may be a non-volatile memory, such as a hard disk drive (HDD) or a solid-state drive (SSD), or a volatile memory (volatile memory), such as a random-access memory (RAM). The memory is any other medium that can be used to carry or store desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto. The memory in an embodiment of the present invention may also be a circuit or any other device that can implement a storage function, for storing program instructions and / or data.

[0138] In the embodiments of the present invention, the processor may be a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component, and may implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of the present invention. A general-purpose processor may be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of the present invention may be directly implemented and executed by a hardware processor, or by a combination of hardware and software modules within the processor.

[0139] Based on the same inventive concept as the aforementioned method embodiment, an embodiment of the present invention further provides a non-transitory computer-readable storage medium storing computer instructions. The computer instructions cause the computer to execute the laser inertial odometry calculation method for a weak structure long tunnel environment as follows:

[0140] Establish the prediction equation of the laser inertial odometry system's motion state and the covariance propagation equation of the error state;

[0141] Establish observation equations for reflection intensity residuals and geometric observation residuals;

[0142] The prior distribution of state variables, geometric observation residuals and intensity observation residuals are integrated to form the maximum a posteriori optimization objective function;

[0143] Based on the maximum a posteriori optimization objective function, the covariance of the system motion state and error state is iteratively updated until convergence to obtain the optimal system pose.

[0144] In summary, the present invention provides a laser inertial odometry calculation method based on a rotating LiDAR and an inertial measurement unit. This method is particularly effective for long tunnel environments with weak structures. It fully utilizes the intensity information of the point cloud to overcome degenerate scenarios with weak structural constraints, enabling more robust and accurate mobile mapping.

[0145] Any content not described in the present specification may be regarded as prior art.

[0146] The terms "including" and "having" and any variations thereof in the description and claims of the present invention and the above-mentioned drawings are intended to cover non-exclusive inclusions, for example, a process, method, system, product or apparatus that includes a series of steps or units is not necessarily limited to the steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to these processes, methods, products or apparatuses.

[0147] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the technical solutions of the embodiments of the present invention.

Claims

1. The laser inertial mileage calculation method for weak structure long tunnel environment is characterized by: include: Establish the prediction equation of the laser inertial odometry system's motion state and the covariance propagation equation of the error state; Establishing observation equations for reflection intensity residuals and geometric observation residuals involves: maintaining an intensity map generated by projecting point clouds from several frames through historical frames, performing line detection on the intensity map using the deep learning-based m-LSD algorithm, and generating observation equations for reflection intensity residuals through point-line matching; transforming the back-propagated scan points in the current frame to the world coordinate system, linearizing their equations on the local plane of the global map, and generating observation equations for geometric observation residuals; The prior distribution of state variables, geometric observation residuals and intensity observation residuals are integrated to form the maximum a posteriori optimization objective function; Based on the maximum a posteriori optimization objective function, the covariance of the system motion state and error state is iteratively updated until convergence to obtain the optimal system pose.

2. The laser inertial odometer calculation method for a weak structure long tunnel environment according to claim 1 is characterized in that: After obtaining the optimal system pose, it also includes: Based on the obtained optimal system pose, the scanning points are transformed into the initial coordinate system to obtain a point cloud map.

3. The laser inertial odometer calculation method for a weak structure long tunnel environment according to claim 1 is characterized in that: Establishing the observation equation of the reflected intensity residual also includes: For the current frame point cloud, candidate edge points are screened based on the reflection intensity covariance calculation; Match the selected candidate edge points with the straight lines detected by the m-LSD algorithm; Construct point-line matching pairs and generate observation equations.

4. The laser inertial odometer calculation method for a weak structure long tunnel environment according to claim 1, characterized in that: The method further comprises: The true value of the error state is linearized to update the covariance between the system motion state and the error state.

5. The laser inertial odometer calculation system for weak structure long tunnel environment is characterized by: include: The first main module is used to establish the prediction equation of the laser inertial odometry system's motion state and the covariance propagation equation of the error state; The second main module is used to establish the observation equations for the reflection intensity residual and the geometric observation residual. This includes: maintaining the intensity map generated by projecting the point cloud of several frames through historical frames, performing line detection on the intensity map using the deep learning-based m-LSD algorithm, and generating the observation equation for the reflection intensity residual through point-line matching; transforming the scan points in the current frame that have undergone backpropagation into the world coordinate system, linearizing the equations of the points that fall on the local plane of the global map, and generating the observation equation for the geometric observation residual. The third main module is used to fuse the prior distribution of state variables, geometric observation residuals and intensity observation residuals to form the maximum a posteriori optimization objective function; The fourth main module is used to iteratively update the covariance of the system motion state and error state based on the maximum a posteriori optimization objective function until convergence to obtain the optimal system posture.

6. The laser inertial odometer calculation system for weak structure long tunnel environment is characterized by: The invention comprises a rotating laser radar, an inertial measurement unit and a processing unit. The processing unit executes the laser inertial odometer calculation method for a weak structure long tunnel environment as described in any one of claims 1 to 4 based on data collected by the rotating laser radar and the inertial measurement unit.

7. The laser inertial odometer calculation system for weak structure long tunnel environment is characterized by: The invention comprises a memory and a processor, wherein the memory stores program instructions executed by the processor, and the processor calls the program instructions to execute the laser inertial odometry calculation method for a weak structure long tunnel environment as described in any one of claims 1 to 4.

8. A non-transitory computer-readable storage medium, characterized in that The non-transitory computer-readable storage medium stores computer instructions, which enable the computer to execute the laser inertial odometry calculation method for a weak structure long tunnel environment according to any one of claims 1 to 4.

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