Tracked vehicle ground feature identification method

Abstract

The invention relates to a track vehicle ground feature recognition method, belongs to the technical field of vehicle control, and solves the problem of inaccurate ground feature recognition in the prior art. Comprising the steps of performing time and space synchronization on ground coverage features and ground state features to obtain synchronization features; constructing a vehicle kinematic equation based on the vehicle kinematic model of the instantaneous steering center, and estimating vehicle sliding parameters at all moments by using a synchronous feature solving equation; performing principal component analysis on ground elevation information in the synchronization features to obtain ground unit normal vectors at all moments; estimating a vehicle pose sequence according to the ground unit normal vector at each moment; obtaining a ground texture type according to the deviation between the estimated vehicle pose sequence and the vehicle pose sequence in the synchronization feature; according to the vehicle speed, the vehicle sliding parameters, the ground surface material and the ground texture type, a ground adhesion coefficient interval is obtained through a clustering algorithm; and accurate identification of the ground texture type and the ground adhesion coefficient interval is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of feature recognition, and in particular to a ground feature recognition method for a tracked vehicle. Background Art

[0002] The road environment is complex and changeable, the terrain features are complex, and the ground features are strongly correlated with the vehicle's driving status, which will directly affect the vehicle's safety, passability and mission efficiency.

[0003] Currently, direct data measurement methods are usually used, in which additional sensors are installed on the vehicle body and suspension to measure vehicle motion, and a vehicle kinematic model is constructed based on the interaction between the vehicle and the ground to calculate the characteristics of the ground on which the vehicle is traveling.

[0004] The existing method requires the addition of additional sensors to obtain data, and it is not easy to deploy sensors for measurement in some extreme scenarios. In addition, the sensors installed on the vehicle body and suspension are not accurate enough, and are affected by noise, making it more difficult to predict ground features. Summary of the invention

[0005] In view of the above analysis, an embodiment of the present invention aims to provide a method for identifying ground features of a tracked vehicle, so as to solve the problem of inaccurate ground feature identification in the prior art.

[0006] An embodiment of the present invention provides a method for identifying ground features of a tracked vehicle, comprising the following steps:

[0007] The collected ground coverage features and ground state features are synchronized in time and space to obtain synchronized features; the ground coverage features include surface material and ground elevation information, and the ground state features include vehicle speed and vehicle posture sequence;

[0008] The vehicle kinematics equation is constructed based on the vehicle kinematics model of the instantaneous steering center, and the vehicle kinematics equation is solved by using the synchronization feature to estimate the vehicle sliding parameters at each moment;

[0009] Performing principal component analysis on the ground elevation information in the synchronization feature to obtain the ground unit normal vector at each moment; estimating the vehicle posture sequence according to the ground unit normal vector at each moment; obtaining the ground texture type at each moment according to the deviation between the estimated vehicle posture sequence and the vehicle posture sequence in the synchronization feature;

[0010] According to the vehicle speed, vehicle sliding parameter, surface material and ground texture type at each moment, a clustering algorithm is used to obtain the ground adhesion coefficient interval at each moment; the ground texture type and ground adhesion coefficient interval at each moment are used as the identified ground features.

[0011] Based on the further improvement of the above method, the collected ground cover features and ground state features are synchronized in time and space, including: fitting each data in the ground state feature with its own time polynomial using the least squares method, calculating each ground state feature at the corresponding moment of the ground cover feature according to the fitted polynomial, and completing the time synchronization of the ground state feature and the ground cover feature; and completing the spatial synchronization of the ground cover feature and the ground state feature by calculating the transformation matrix between different coordinate systems.

[0012] Based on the further improvement of the above method, calculating the transformation matrix between different coordinate systems includes: calculating the transformation matrix between the camera and the IMU coordinate system of the integrated navigation by the following steps:

[0013] Constructing a rotation change matrix between multiple pairs of adjacent and continuous camera images, establishing an overdetermined equation by multiplying the rotation change matrix by the rotation matrix in the transformation matrix to be solved to 0, and calculating the rotation matrix by solving the overdetermined equation;

[0014] Constructing a translation change matrix based on a sliding window, the translation change matrix includes: a translation matrix in a transformation matrix to be solved, a plurality of ground state features and an observation depth of a plurality of features on a camera image, and solving the translation matrix using a non-iterative linear method with the goal of minimizing the sum of the Mahalanobis norms of various errors in the translation change matrix within the sliding window;

[0015] According to the rotation matrix and the translation matrix, a transformation matrix between the camera and the IMU coordinate system of the integrated navigation is obtained.

[0016] Based on the further improvement of the above method, the vehicle kinematic model based on the instantaneous turning center includes: when the tracked vehicle moves, the vehicle body rotates around the first instantaneous turning center, and the line connecting the vehicle body collective center and the first instantaneous turning center is perpendicular to the actual speed direction of the vehicle; the left and right side track grounding sections rotate around the second instantaneous turning center and the third instantaneous turning center respectively; the slip coefficient is considered based on the longitudinal linear velocity of the left track, and the slip coefficient is considered based on the longitudinal linear velocity of the right track.

[0017] Based on the further improvement of the above method, the vehicle kinematic equation is expressed by the following formula:

[0018]

[0019] Among them, v x represents the longitudinal velocity of the geometric center of the tracked vehicle in the IMU coordinate system, v y represents the lateral velocity of the geometric center of the tracked vehicle in the IMU coordinate system, ω z represents the vehicle yaw angular velocity; v sl and vsr Indicates the longitudinal linear speed of the tracks on both sides; x c The x-coordinate value of the first instantaneous turning center; y l and r They represent the y coordinate values ​​of the second instantaneous turning center and the third instantaneous turning center respectively; l Indicates the slip coefficient of the left track, f r It indicates the slip coefficient of the right track; D indicates the center distance of the tracks on both sides.

[0020] Based on the further improvement of the above method, the vehicle sliding parameters are obtained by:

[0021] The slip coefficient and the slip coefficient in the vehicle kinematic equation are solved, and after adding the positive and negative directions according to their consistency with the vehicle's traveling direction, the average value of the slip coefficient and the slip coefficient in the positive and negative directions is calculated to obtain the slip coefficient.

[0022] Based on the further improvement of the above method, principal component analysis is performed on the ground elevation information in the synchronization feature to obtain the ground unit normal vector at each moment. The point cloud corresponding to the vehicle trajectory point at each moment is used as the central point cloud, and the area to be analyzed of each central point cloud is obtained according to the ground elevation information. The spatial coordinates of all point clouds in the area to be analyzed of each central point cloud are formed into a matrix and then principal component analysis is performed to obtain the first principal component vector. The first principal component vector is normalized to obtain the corresponding ground unit normal vector.

[0023] Based on the further improvement of the above method, the ground texture type is obtained according to the deviation between the estimated vehicle posture sequence and the vehicle posture sequence in the synchronization feature, including: based on the random forest algorithm, multiple independent classification models are constructed, and then the final classification decision is made by voting on the principle of minority obeys majority; wherein, multiple independent classification models all adopt support vector machine models, and the ground texture type is obtained according to the amplitude spectrum deviation, power spectrum density deviation and cross-correlation vector deviation based on the constructed ground texture truth table; the ground texture types include: solid and soft.

[0024] Based on the further improvement of the above method, the amplitude spectrum deviation, power spectrum density deviation and cross-correlation vector deviation are obtained by Fourier transforming the estimated vehicle posture sequence and the vehicle posture sequence in the synchronization feature as time series, and then calculating the amplitude spectrum, power spectrum density and cross-correlation vector respectively; the amplitude spectrum deviation is calculated using the mean square error, the power spectrum density deviation is calculated using the cosine similarity, and the cross-correlation vector deviation is calculated using the Euclidean distance.

[0025] Based on the further improvement of the above method, according to the vehicle speed, vehicle sliding parameters, surface material and ground texture type at each moment, the ground adhesion coefficient range at each moment is obtained by using a clustering algorithm, including:

[0026] Based on the ground adhesion coefficient truth table, a record is extracted for each ground adhesion coefficient interval and encoded as the initial clustering center in the clustering algorithm; the vehicle speed, vehicle sliding parameters, surface material and ground texture type at each moment are encoded as the encoding vector of a sample, and the clustering algorithm is used to cluster the same number of samples in the preset time period into multiple categories; according to the ground adhesion coefficient interval corresponding to the initial clustering center of each category, the ground adhesion coefficient interval of each sample in the category is obtained.

[0027] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects:

[0028] 1. Collect multimodal data features of different perception areas of tracked vehicles, and improve the accuracy of environmental perception through time and space synchronization, enhance real-time perception capabilities, improve the adaptability and accuracy of tracked vehicle ground feature recognition, assist the tracked vehicle planning module under unmanned aerial vehicle maneuvers to select more efficient and lower-cost paths, and enable the control module to better ensure the safety of tracked vehicles in different scenarios.

[0029] 2. Use environmental perception to obtain ground elevation information to estimate the vehicle posture sequence, and compare it with the actual vehicle posture sequence, which increases the number of information modes and improves the recognition accuracy of the firmness / softness of the ground.

[0030] 3. Constructing a vehicle kinematic model based on the instantaneous steering center is closer to the actual operating conditions and improves the calculation accuracy of the sliding parameters. At the same time, considering different vehicle speeds, vehicle sliding parameters, surface materials and ground texture types, a truth table of the ground adhesion coefficient is constructed, which improves the rationality of the initial clustering center selection of the clustering algorithm, makes the clustering algorithm have better generalization ability, and improves the prediction accuracy of the ground adhesion coefficient range in actual scenarios.

[0031] In the present invention, the above-mentioned technical solutions can also be combined with each other to achieve more preferred combination solutions. Other features and advantages of the present invention will be described in the subsequent description, and some advantages can become obvious from the description, or can be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained through the contents particularly pointed out in the description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings are only for the purpose of illustrating particular embodiments and are not to be considered limiting of the present invention. Like reference symbols denote like components throughout the drawings.

[0033] Figure 1 This is a flow chart of a method for identifying ground features of a tracked vehicle in an embodiment of the present invention;

[0034] Figure 2 Schematic diagram of a vehicle kinematic model based on an instantaneous steering center in an embodiment of the present invention. DETAILED DESCRIPTION

[0035] The preferred embodiments of the present invention are described in detail below in conjunction with the accompanying drawings, wherein the accompanying drawings constitute a part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not used to limit the scope of the present invention.

[0036] A specific embodiment of the present invention discloses a method for identifying ground features of a tracked vehicle, such as Figure 1 As shown, the following steps are included:

[0037] S1. Synchronize the collected ground coverage features and ground state features in time and space to obtain synchronized features; the ground coverage features include surface material and ground elevation information, and the ground state features include vehicle speed and vehicle posture sequence.

[0038] It should be noted that the tracked vehicle in this embodiment is a ground unmanned mobile platform equipped with a camera, an environment perception laser radar and a body perception combined navigation, which will perceive different areas at the same time point. Among them, the multi-environment perception area of ​​the camera and the environment perception laser radar covers a concentric circle range of 0.6m to 15m with the center of the body as the center, which is used to obtain ground coverage features, including: surface materials identified from the image collected by the camera and ground elevation information obtained from the point cloud data collected by the laser radar. The surface materials are divided into paved roads, dirt roads, gravel roads, ice and snow roads and grasslands; the ground elevation information is an elevation grid map with a resolution of 0.2m; the body perception perception area of ​​the body perception combined navigation covers a range of 0.6m with the center of the body as the center, which is used to obtain ground state features from the data collected by the combined navigation, including: vehicle speed, drive wheel speed and vehicle posture sequence, where the vehicle posture includes the vehicle pitch angle, roll angle and roll angle.

[0039] Since the ground cover features and ground state features are the perception results of different areas and the sampling frequencies are also different, the results of these two areas are synchronized in time and space, and the aligned results are used to estimate the terrain features at the same location.

[0040] Specifically, when performing time synchronization, each data in the ground state feature is fitted using the least squares method to obtain a time t b The body perception information curve S(t b). Considering that the change of ground state characteristics is not linear, a polynomial is used for fitting; considering the influence of noise, the degree of the polynomial cannot be too high. Therefore, a cubic polynomial is used for fitting, which not only takes into account the nonlinear characteristics, but also eliminates the influence of measurement noise on the least squares method. The formula is as follows:

[0041] S(t b )=y b =a0+a1t b +a2t b 2 +a3t b 3 (1)

[0042] Using the least squares method, with the goal of minimizing the sum of squares of errors, we fit the coefficients a0, a1, a2, and a3, and construct the curve S(t b ).

[0043] Furthermore, according to the acquisition time t of the environment perception laser radar c , find the ground state characteristics y at the corresponding time on each fitted curve c =S(t c ), which corresponds to the ground coverage characteristics at the same time and achieves time synchronization.

[0044] It should be noted that spatial synchronization is a calibration method. Tracked vehicles usually involve multiple coordinate systems. The spatial synchronization of ground coverage features and ground state features is achieved by calculating the transformation matrix between different coordinate systems.

[0045] Among them, the transformation matrix between different coordinate systems includes but is not limited to: using the camera with environmental sensing capability and the combined navigation with body perception capability as the calibration objects of the two environments to perform external parameter calibration. That is, at the same time, according to the pose estimated by the camera image and the actual pose obtained by the combined navigation with body perception, the transformation matrix T between the camera and IMU coordinate systems is obtained. bc (including the rotation matrix q bc and the translation matrix p bc ); The ground coverage features at the same time are converted into data in the IMU coordinate system according to the transformation matrix.

[0046] Specifically, the optical flow method is used to extract the adjacent frames c of the image taken by the camera i ,c i+1 The relative motion between At the same time, two adjacent frames b under IMU i ,b i+1 The posture change of Construct an overdetermined matrix equation as shown below:

[0047]

[0048] Since the equation contains quaternions and spatial transformations, it is not full rank and cannot be solved directly. The following approximate solutions are given for the rotation and translation matrices respectively.

[0049] First, by decomposing the posture changes, we can get the rotation changes of the camera and IMU in two adjacent frames: and Construct the change matrix Q of adjacent frames as shown below i,i+1 :

[0050]

[0051] On the basis of single-frame changes, the rotation change matrices between multiple pairs of adjacent and continuous camera images are constructed. The overdetermined equation is established by multiplying the rotation change matrix with the rotation matrix in the transformation matrix to be solved to 0, so as to avoid the problem of being unable to solve due to insufficient rank:

[0052]

[0053] Where N represents the number of frames, w i,i+1 represents the change weight between two adjacent images, Q N Represents the selection change matrix for multiple pairs of adjacent frames.

[0054] It should be noted that if the rotation matrix q is estimated based on the initial images bc Initial value of Then first calculate the threshold r between two adjacent images according to the following formula: i,i+1 :

[0055]

[0056] Among them, a represents the coefficient, and tr(·) represents taking the trace of the matrix.

[0057] Then calculate the corresponding change weight through the following formula to calculate the rotation matrix q bc :

[0058]

[0059] Preferably, the preset threshold value is set to 0.02.

[0060] If the initial value of the rotation matrix cannot be estimated Then Q N After filling the full rank, by calculating Q N The rotation matrix q is calculated by the right unit singular vector of the minimum singular value of bc .

[0061] After completing the calculation of the rotation matrix, a tightly coupled sliding window is used to calculate the position changes of multiple ground state features and image estimates in the IMU coordinate system to initialize the translation matrix.

[0062] Specifically, a translation change matrix χ is constructed based on the sliding window, including: the translation matrix in the transformation matrix to be solved, multiple ground state features and the observation depth of multiple features on the camera image, which is expressed by the following formula:

[0063]

[0064] Among them, x j represents the jth ground state feature, The gravity vector representing the jth ground state feature, N n represents the number of ground state features in the sliding window, M m represents the number of features with sufficient parallax (significant displacement) in the sliding window; n and m are the starting indices in the sliding window; λ m is the depth of the mth feature on the image from the first observation; It is the initial translation matrix, which is used to calculate the spatial motion result of the translation calculation; Represents the vehicle's pitch angle, roll angle and ground state characteristics.

[0065] After initialization, the translation matrix p is completed by performing maximum likelihood estimation on the matrix obtained by the sliding window. bc Estimation, where the maximum likelihood estimate is to minimize the sum of the Mahalanobis norms of all measurement errors from the IMU and camera within the sliding window, as shown below:

[0066]

[0067] The subscript B represents feature b. k The set of all IMU measurements, subscript C represents feature c l and camera pose c h The set of all observations between p Represents the changing model of camera and IMU positioning in the case of time alignment; Indicates the change in the camera's estimated posture. Represents the linear camera measurement model, which is a 3rd-order square matrix containing the camera intrinsic parameters; Represents the linear IMU measurement model, which is a 4-order square matrix including a rotation matrix and a translation matrix; Represents the change in IMU measurement, Indicates the translation change between two adjacent frames. Indicates the rotation change between two adjacent frames.

[0068] The translation matrix is ​​solved using a non-iterative linear method, and the minimum Mahalanobis distance of the minimum sliding window is used as the optimization target. The translation matrix p is optimized using the Newton method. bc Make optimizations.

[0069] According to the above obtained rotation matrix q from the camera to the IMU bc And the translation matrix p bc , forming the transformation matrix T bc , achieving a spatial alignment.

[0070] In this embodiment, the ground coverage feature reflects the visual features of the environment's appearance, and the ground state feature reflects the "invisible" "tactile" characteristics of the ground surface. By aligning the two features, the accuracy of environmental perception is improved, the real-time perception capability is enhanced, and the adaptability and accuracy of tracked vehicle ground feature recognition are improved.

[0071] S2. Construct a vehicle kinematic model based on the instantaneous steering center and use the synchronization features to estimate the vehicle sliding parameters at each moment.

[0072] It should be noted that the interaction between the vehicle and the ground can be reflected by the sliding parameter, which shows the interaction between the track and the ground. During the driving process of the tracked vehicle, it does not travel completely in a straight line, but is abstracted into a combination of steering primitives with multiple curvatures. Since the track contact area of ​​the tracked vehicle is large, slip and sliding will occur during the steering process, so the track contact area of ​​the tracked vehicle cannot be simplified to a point. Therefore, the motion trajectory of the tracked vehicle is related to many factors and cannot be directly predicted by the angular velocity of the tracks on both sides.

[0073] The real sliding parameter comes from the instantaneous turning situation of the sliding. This embodiment abstracts the instantaneous turning situation into Figure 2 The vehicle kinematic model based on the instantaneous turning center shown in FIG: When the vehicle moves, the vehicle body can be regarded as rotating around the first instantaneous turning center, and the tracks on both sides not only follow the vehicle body to move at the entrained speed, but also move relative to the vehicle body at a longitudinal linear speed. Therefore, the absolute speed of a point on the track is the vector sum of the entrained speed and the longitudinal linear speed of the point. The track grounding sections on both sides are regarded as rigid bodies, and the track grounding sections on the left and right sides rotate around the second instantaneous turning center and the third instantaneous turning center respectively.

[0074] exist Figure 2 In the figure, the tracked vehicle moves on the horizontal ground and the center of mass of the tracked vehicle coincides with the geometric center. The height information is ignored, OXY is used to represent the earth coordinate system, and oxy is used to represent the vehicle body IMU coordinate system. xis the longitudinal velocity of the tracked vehicle body geometric center o in the IMU coordinate system, v y is the lateral velocity of the geometric center of the tracked vehicle in the IMU coordinate system, and the absolute velocity v of the geometric center of the vehicle o Yes x and v y Vector sum. ω z is the vehicle yaw rate, counterclockwise is positive, ω v is the driving wheel speed of the tracked vehicle, which is collected by the vehicle wheel speed sensor; r v is the radius of the driving wheel of the tracked vehicle. θ represents the heading angle of the vehicle, O c (x c ,y c ) is the first instantaneous turning center of the vehicle; l (x l ,y l ) and O r (x r ,y r ) represent the second instantaneous turning center and the third instantaneous turning center corresponding to the left track and the right track respectively; v sl and v sr Indicates the longitudinal linear speed of the tracks on both sides; O l and O r The line between them constructs points M and N on the left and right tracks. The v of points M and N on the tracks qM 、v qN represents the drag speed of the left and right tracks of the tracked vehicle; D is the center distance of the tracks on both sides, where O c The line connecting the geometric center o of the vehicle body is perpendicular to the actual speed direction v of the vehicle o .

[0075] Further, using the coordinates of the tracked vehicle's center of mass o = [X, Y, θ] T To express the position of the tracked vehicle in the geodetic coordinate system, the formula is as follows:

[0076]

[0077] The vehicle speed in the traditional vehicle coordinate system is used in the kinematic model, and the slip coefficient f of the left track is taken into account based on the longitudinal linear speed of the left and right tracks. l and the slip coefficient f of the right track r , the vehicle longitudinal velocity, lateral velocity and yaw rate are obtained according to the following formula:

[0078]

[0079] Furthermore, by analyzing the track contact section of the tracked vehicle, the coordinate values ​​of each instantaneous turning center of the tracked vehicle are calculated by the following formula, which describes the relationship between the instantaneous turning center of the tracks on both sides and the vehicle body and other kinematic parameters when the vehicle turns:

[0080]

[0081] According to the above formula, the vehicle longitudinal velocity, lateral velocity and steering angular velocity are as follows:

[0082]

[0083] Among them, the longitudinal linear speed of the crawler tracks on both sides is calculated by dividing the speed of the crawler drive wheels on both sides by ω sl ,ω sr Respectively with the radius of the track drive wheel r r Multiply them together and the formula is as follows:

[0084]

[0085] According to formula (10) and formula (12), the vehicle kinematic equation is as follows:

[0086]

[0087] Get the vehicle longitudinal velocity v from the synchronization feature x and the vehicle lateral velocity v y , the longitudinal linear velocity v of the left and right tracks is obtained by the speed of the track drive wheel sl 、v sr and the actual instantaneous steering center coordinates, and finally calculate the current slip coefficient f of the vehicle l and slip coefficient f r .

[0088] It should be noted that the slip coefficient and the slip coefficient have positive and negative directions. The slip coefficient direction is recorded as positive when it is consistent with the vehicle's moving direction, and negative when it is not. The slip coefficient direction is recorded as negative when it is consistent with the vehicle's moving direction, and positive when it is not. The slip coefficient direction and the slip coefficient direction are obtained by calculating the difference between the drag speed and the longitudinal linear speed of the corresponding side track.

[0089] Give slip coefficient f l and slip coefficient f l After adding the positive and negative directions, the vehicle's sliding parameter f is obtained according to the following formula:

[0090]

[0091] S3. Perform principal component analysis on the ground elevation information in the synchronization feature to obtain the ground unit normal vector at each moment; estimate the vehicle posture sequence based on the ground unit normal vector at each moment; and obtain the ground texture type at each moment based on the deviation between the estimated vehicle posture sequence and the vehicle posture sequence in the synchronization feature.

[0092] It should be noted that the ground texture types include solid and soft, which is a part of the ground features to be identified in this embodiment. The principal component analysis method is used for the ground elevation information synchronized in step S1 to calculate the normal vector of the discrete surface and estimate the possible posture information of the vehicle; the deviation between the estimated posture information and the posture information synchronized in step S1 is compared to determine the solid or soft characteristics of the ground.

[0093] The principal component analysis method is calculated on the elevation information to obtain the ground unit normal vector at each moment, including: taking the point cloud corresponding to the vehicle trajectory point at each moment as the central point cloud, obtaining the area to be analyzed composed of each central point cloud and its surrounding neighboring point clouds according to the ground elevation information, and after projecting the area to be analyzed, fitting the elevation information of the area to be analyzed to find a direction vector that makes the distribution of the projection points of the point cloud in this area on the direction vector the most concentrated. The direction vector is the ground unit normal vector to be calculated.

[0094] Specifically, in the ground elevation information, a grid area that is 2 units away from the central point cloud in 8 directions (up, down, left, right, and four diagonals) is taken as the area to be analyzed, that is, the area to be analyzed is a grid area of ​​25 point clouds including the central point cloud; the three-dimensional spatial coordinates of each point cloud in the area to be analyzed are organized into a 25×3 matrix, and then principal component analysis is performed to obtain the first principal component vector, that is, the maximum eigenvalue vector.

[0095] The first principal component vector λ max The components λ in the x, y, and z directions x ,λ y ,λ z After normalization, the corresponding ground unit normal vector γ is obtained, and the formula is as follows:

[0096]

[0097] According to the components of the ground unit normal vector in each coordinate axis in the geodetic coordinate system, the angle between the ground unit normal vector and the z-axis is used as the z-axis rotation angle of the IMU coordinate system relative to the geodetic coordinate system, and the rotation matrix of the geodetic coordinate system relative to the IMU coordinate system is calculated; according to the rotation matrix and the coordinates of the vehicle trajectory points at each moment, the vehicle posture sequence is estimated in represents the elevation angle in the geodetic coordinate system at time t, is the roll angle in the geodetic coordinate system at time t, is the roll angle in the geodetic coordinate system at time t.

[0098] Furthermore, according to the deviation between the estimated vehicle posture sequence and the vehicle posture sequence in the synchronization feature, the ground texture type is obtained, including: based on the random forest algorithm, multiple independent classification models are constructed, and then the final classification decision is made by voting on the principle of minority obeys majority; wherein, multiple independent classification models all adopt the support vector machine model, and the classification results are obtained according to the amplitude spectrum deviation, power spectrum density deviation and cross-correlation vector deviation based on the constructed ground texture truth table.

[0099] Specifically, the estimated vehicle posture sequence and the vehicle posture sequence synchronized in step S1 are Fourier transformed as time series, and the amplitude spectrum, power spectrum density and cross-correlation vector are calculated respectively; the amplitude spectrum deviation is calculated using the mean square error, the power spectrum density deviation is calculated using the cosine similarity, and the cross-correlation vector deviation is calculated using the Euclidean distance.

[0100] During implementation, the method is driven on roads with various surface materials. The height difference between the ruts left by the tracked vehicle and the actual ground is calculated to directly obtain the solid or soft classification as the most intuitive label. The estimated vehicle pose sequences and the collected vehicle pose sequences on different surface materials are then analyzed to determine the thresholds of the amplitude spectrum deviation, power spectrum density deviation and cross-correlation vector deviation to construct a ground texture truth table.

[0101] As shown in Table 1, the table exemplarily shows a rutting depth, thresholds of various deviations and corresponding classifications for a tracked vehicle on the ground with different surface materials.

[0102] Table 1 Truth table of ground firmness / softness for tracked vehicles

[0103]

[0104] S4. According to the vehicle speed, vehicle sliding parameter, surface material and ground texture type at each moment, a clustering algorithm is used to obtain the ground adhesion coefficient interval at each moment; the ground texture type and ground adhesion coefficient interval at each moment are used as the identified ground features.

[0105] It should be noted that the ground adhesion coefficient is an important component of ground features and is used to provide more accurate constraints in vehicle motion planning and control. The ground adhesion coefficient is in the range of (0, 1), and this embodiment divides it into 10 intervals: (0-0.10, 0.10-0.20, ..., 0.90-1.00).

[0106] Considering that clustering algorithms, especially K-means algorithm, seek local optimal solutions through iterative optimization, the initial cluster center is very sensitive. The selection of the initial cluster center has a great influence on the final clustering result. If the initial cluster center is not selected properly, it may lead to low clustering quality or slow algorithm convergence.

[0107] In this embodiment, the ground adhesion coefficient truth table of the tracked vehicle is obtained by repeated tests on roads of various surface materials, from which the initial cluster center is selected.

[0108] Specifically, a suspension displacement sensor, a vehicle engine power sensor, and a body acceleration sensor are installed on the tracked vehicle. Under a test environment consisting of various surface materials, vehicle speeds, ground textures, and sliding parameters, the vehicle engine power sensor is used to determine the power and speed of the vehicle engine, and the traction force f provided by the vehicle engine in real time is obtained. c ; Then through the known vehicle weight m c and vehicle acceleration a c , the ground adhesion coefficient under each test environment is obtained by the following formula:

[0109]

[0110] After repeated testing of each test environment, the true value of the ground adhesion coefficient of the test environment is obtained according to the interval of the average ground adhesion coefficient. Finally, the true values ​​of the ground adhesion coefficients of multiple test environments constitute the ground adhesion coefficient truth table of the tracked vehicle, as shown in Table 2.

[0111] Table 2 Truth table of ground adhesion coefficient of tracked vehicles

[0112]

[0113] Since the test environment cannot cover various complex environments in reality, the truth table is only used to select the initial cluster centers. That is, a record is randomly selected from the truth table of the ground adhesion coefficient of the tracked vehicle for each of the 10 ground adhesion coefficient intervals, and the vehicle speed interval, vehicle sliding parameter interval, surface material and ground texture type corresponding to the record are encoded respectively, and the encoding vectors are concatenated as the encoding vectors of the 10 initial cluster centers.

[0114] It should be noted that the surface material and ground texture type are text features, and the word2vec model is used to obtain their respective text feature codes; the vehicle speed and vehicle sliding parameter are intervals, and the corresponding interval feature codes are obtained; the text feature codes and interval feature codes are concatenated to obtain the encoding vectors of each record in the truth table.

[0115] Furthermore, according to the vehicle speed, vehicle sliding parameters, surface material and ground texture type at each moment, a clustering algorithm is used to obtain the ground adhesion coefficient interval at each moment, including:

[0116] The vehicle speed, vehicle sliding parameter, surface material and ground texture type at each moment are taken as the four-dimensional features of a sample. The encoding vector of each sample is obtained in the same way as the encoding vector of the initial clustering center. The k-means algorithm is used to cluster the same number of samples in the preset time period into multiple categories. According to the ground adhesion coefficient interval corresponding to the initial clustering center of each category, the ground adhesion coefficient interval of each sample in the category is obtained.

[0117] Preferably, in the k-means algorithm, the distance between samples is measured by calculating the Minkowski distance, and the Minkowski distance parameter p is set to 0.5.

[0118] Finally, the ground texture type and ground adhesion coefficient range at each moment are used as the recognition results of ground features to assist the tracked vehicle planning module under unmanned aerial vehicle maneuvers to select a more efficient and lower-cost path, so that the control module can better ensure the safety of tracked vehicles in different driving scenarios.

[0119] Compared with the prior art, the method for identifying ground features of tracked vehicles provided in this embodiment collects multimodal data features of different perception areas of tracked vehicles, and improves the accuracy of environmental perception through synchronization of time and space, enhances real-time perception capability, and improves the adaptability and accuracy of ground feature recognition of tracked vehicles. The ground elevation information obtained by environmental perception is used to estimate the vehicle posture sequence, which is compared with the actual vehicle posture sequence, increasing the number of modes of information and improving the recognition accuracy of the solidity / softness of the ground. A vehicle kinematic model based on the instantaneous steering center is constructed, which is closer to the actual operating conditions and improves the calculation accuracy of the sliding parameters; at the same time, the ground adhesion coefficient truth table is constructed considering different vehicle speeds, vehicle sliding parameters, surface materials and ground texture types, which improves the rationality of the selection of the initial clustering center of the clustering algorithm, so that the clustering algorithm has better generalization ability and improves the prediction accuracy of the ground adhesion coefficient range in actual scenarios.

[0120] Those skilled in the art will appreciate that all or part of the processes of the above-mentioned embodiments can be implemented by instructing related hardware through a computer program, and the program can be stored in a computer-readable storage medium, wherein the computer-readable storage medium is a disk, an optical disk, a read-only storage memory, or a random access memory, etc.

[0121] The above description is only a preferred specific implementation manner of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by any technician familiar with the technical field within the technical scope disclosed by the present invention should be covered within the protection scope of the present invention.

Claims

1. A method for identifying ground features of a tracked vehicle, characterized in that: The following steps are involved: The collected ground coverage features and ground state features are synchronized in time and space to obtain synchronized features; the ground coverage features include surface material and ground elevation information, and the ground state features include vehicle speed and vehicle posture sequence; The vehicle kinematics equation is constructed based on the vehicle kinematics model of the instantaneous steering center, and the vehicle kinematics equation is solved by using the synchronization feature to estimate the vehicle sliding parameters at each moment; Performing principal component analysis on the ground elevation information in the synchronization feature to obtain the ground unit normal vector at each moment; estimating the vehicle posture sequence according to the ground unit normal vector at each moment; obtaining the ground texture type at each moment according to the deviation between the estimated vehicle posture sequence and the vehicle posture sequence in the synchronization feature; According to the vehicle speed, vehicle sliding parameter, surface material and ground texture type at each moment, a clustering algorithm is used to obtain the ground adhesion coefficient interval at each moment; the ground texture type and ground adhesion coefficient interval at each moment are used as the identified ground features.

2. The method for recognizing ground features of a tracked vehicle according to claim 1, characterized in that: The collected ground coverage features and ground state features are synchronized in time and space, including: fitting each data in the ground state feature with a respective time polynomial using the least squares method, calculating each ground state feature at the corresponding moment of the ground coverage feature based on the fitted polynomial, and completing the time synchronization of the ground state feature and the ground coverage feature; and completing the spatial synchronization of the ground coverage feature and the ground state feature by calculating the transformation matrix between different coordinate systems.

3. The method for recognizing ground features of a tracked vehicle according to claim 2, characterized in that: The calculation of the transformation matrix between different coordinate systems includes: calculating the transformation matrix between the camera and the IMU coordinate system of the integrated navigation by the following steps: Constructing a rotation change matrix between multiple pairs of adjacent and continuous camera images, establishing an overdetermined equation by multiplying the rotation change matrix by the rotation matrix in the transformation matrix to be solved to 0, and calculating the rotation matrix by solving the overdetermined equation; Constructing a translation change matrix based on a sliding window, the translation change matrix includes: a translation matrix in a transformation matrix to be solved, a plurality of ground state features and an observation depth of a plurality of features on a camera image, and solving the translation matrix using a non-iterative linear method with the goal of minimizing the sum of the Mahalanobis norms of various errors in the translation change matrix within the sliding window; According to the rotation matrix and the translation matrix, a transformation matrix between the camera and the IMU coordinate system of the integrated navigation is obtained.

4. The method for recognizing ground features of a tracked vehicle according to claim 1, characterized in that: The vehicle kinematic model based on the instantaneous turning center includes: when the tracked vehicle moves, the vehicle body rotates around the first instantaneous turning center, and the line connecting the vehicle body collective center and the first instantaneous turning center is perpendicular to the actual speed direction of the vehicle; the left and right track grounding sections rotate around the second instantaneous turning center and the third instantaneous turning center respectively; the slip coefficient is considered based on the longitudinal linear velocity of the left track, and the slip coefficient is considered based on the longitudinal linear velocity of the right track.

5. The method for recognizing ground features of a tracked vehicle according to claim 1, characterized in that: The vehicle kinematic equation is expressed by the following formula: Among them, v x represents the longitudinal velocity of the geometric center of the tracked vehicle in the IMU coordinate system, v y represents the lateral velocity of the tracked vehicle body’s geometric center in the IMU coordinate system, ω z represents the vehicle yaw angular velocity; v sl and v sr Indicates the longitudinal linear speed of the tracks on both sides; x c The x-coordinate value of the first instantaneous turning center; y l and r They represent the y coordinate values ​​of the second instantaneous turning center and the third instantaneous turning center respectively; l Indicates the slip coefficient of the left track, f r It indicates the slip coefficient of the right track; D indicates the center distance of the tracks on both sides.

6. The method for recognizing ground features of a tracked vehicle according to claim 5, characterized in that: The vehicle sliding parameters are obtained in the following way: The slip coefficient and the slip coefficient in the vehicle kinematic equation are solved, and after adding the positive and negative directions according to their consistency with the vehicle's traveling direction, the average value of the slip coefficient and the slip coefficient in the positive and negative directions is calculated to obtain the slip coefficient.

7. The method for recognizing ground features of a tracked vehicle according to claim 1, characterized in that: The ground elevation information in the synchronization feature is subjected to principal component analysis to obtain the ground unit normal vector at each moment. The point cloud corresponding to the vehicle trajectory point at each moment is taken as the central point cloud, and the area to be analyzed of each central point cloud is obtained according to the ground elevation information. The spatial coordinates of all point clouds in the area to be analyzed of each central point cloud are formed into a matrix and then principal component analysis is performed to obtain the first principal component vector. The first principal component vector is normalized to obtain the corresponding ground unit normal vector.

8. The method for recognizing ground features of a tracked vehicle according to claim 1, characterized in that: The method of obtaining the ground texture type based on the deviation between the estimated vehicle posture sequence and the vehicle posture sequence in the synchronization feature includes: constructing multiple independent classification models based on the random forest algorithm, and then making a final classification decision by voting on the principle of minority obeys majority; wherein the multiple independent classification models all adopt a support vector machine model, and obtain the ground texture type based on the constructed ground texture truth table according to the amplitude spectrum deviation, power spectrum density deviation and cross-correlation vector deviation; the ground texture types include: solid and soft.

9. The method for recognizing ground features of a tracked vehicle according to claim 8, characterized in that: The amplitude spectrum deviation, power spectrum density deviation and cross-correlation vector deviation are obtained by Fourier transforming the estimated vehicle posture sequence and the vehicle posture sequence in the synchronization feature as a time series, and then respectively calculating the amplitude spectrum, power spectrum density and cross-correlation vector; the amplitude spectrum deviation is calculated using the mean square error, the power spectrum density deviation is calculated using the cosine similarity, and the cross-correlation vector deviation is calculated using the Euclidean distance.

10. The method for recognizing ground features of a tracked vehicle according to claim 1, characterized in that: The method of obtaining the ground adhesion coefficient interval at each moment by using a clustering algorithm according to the vehicle speed, vehicle sliding parameter, ground surface material and ground texture type at each moment includes: Based on the ground adhesion coefficient truth table, a record is extracted for each ground adhesion coefficient interval and encoded as the initial clustering center in the clustering algorithm; the vehicle speed, vehicle sliding parameters, surface material and ground texture type at each moment are encoded as the encoding vector of a sample, and the clustering algorithm is used to cluster the same number of samples in the preset time period into multiple categories; according to the ground adhesion coefficient interval corresponding to the initial clustering center of each category, the ground adhesion coefficient interval of each sample in the category is obtained.

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