Safe landing area selection method based on slope and roughness error estimation

Abstract

The invention discloses a safe landing area selection method based on slope and roughness error estimation, and relates to the technical field of automatic deep space exploration landing, and the method comprises the steps: carrying out the data collection of a terrain, carrying out the plane fitting, and estimating a plane fitting error; estimating a gradient error and a roughness error based on the plane fitting error; and based on the gradient error and the roughness error, establishing a safe landing probability graph, and determining a safe landing area. According to the method, point cloud data of a terrain is obtained by using a laser radar sensor, a digital elevation model is generated, detailed modeling of the terrain is realized, plane fitting is carried out on DEM data through a least square method, a measurement error is propagated to a plane fitting error and a DEM point position error, the plane fitting error is further propagated to a slope error, and the slope error is further propagated. Meanwhile, a plane fitting error and a DEM point location error are propagated to a roughness error, a safe landing probability graph is established, and a candidate safe landing area is accurately positioned by combining a set threshold value and average value analysis.

Description

Technical Field

[0001] The present invention relates to the field of automated deep space exploration landing technology, and in particular to a method for selecting a safe landing area based on slope and roughness error estimation. Background Art

[0002] With the deepening of global scientific exploration missions, especially the expansion of future deep space exploration missions to areas of high scientific value, the demand for safe landing of landers has become increasingly stringent. However, the terrain in these areas is often complex, diverse and drastically changing, which places unprecedented demands on the accuracy of landing obstacle avoidance and safe zone selection. Traditional topographic mapping technologies mostly rely on GPS, photogrammetry or ground measurement technology, each of which has its own inherent technical defects. In deep space scenes with difficult layout and harsh environment, the collection of terrain data often faces the challenges of low data resolution and large spatial errors, resulting in insufficient obstacle recognition capabilities. At the same time, data fusion between different technologies is difficult and complex, which increases data processing time and computing resource consumption.

[0003] As the core technology for acquiring high-precision terrain data, the application of LiDAR improves spatial resolution and terrain detection accuracy. However, LiDAR technology itself also has measurement errors, especially in complex terrain and adverse weather conditions. Its measurement errors may affect the accuracy of measurement results. This error often causes deviations in terrain data, directly leading to missed or false detections in obstacle identification, which is a risk that cannot be ignored for landing and obstacle avoidance work in deep space exploration missions. Traditional terrain analysis models usually do not consider or fail to fully deal with the cumulative effect of terrain errors, which limits the identification accuracy of safe landing areas and may miss potential dangerous areas. At the same time, traditional methods lack probabilistic analysis of uncertainty distribution in the evaluation of slope and roughness, and only rely on fixed thresholds for judgment. They cannot dynamically adapt to terrain changes, resulting in deviations in landing area identification.

[0004] Due to the singleness of data and the limitations of analysis models, the available safe landing areas under complex terrain conditions are greatly reduced, increasing the risk of obstacle avoidance landing in deep space exploration missions. In order to select safe landing areas more reliably, it is necessary to develop a technology that can dynamically combine multi-source data and predict the uncertainty of terrain features. Summary of the invention

[0005] In view of the above-mentioned problems, the present invention is proposed.

[0006] Therefore, the technical problems solved by the present invention are: the existing terrain measurement methods have measurement errors that lead to inaccurate identification, long data processing time and high computing resource requirements, and poor ability to adapt to dynamic changes in terrain; and how to accurately establish a safe landing probability map and determine the landing area through efficient data collection and reasonable error propagation.

[0007] To solve the above technical problems, the present invention provides the following technical solutions: a method for selecting a safe landing area based on slope and roughness error estimation, comprising collecting terrain data, performing plane fitting and estimating the plane fitting error; estimating the slope error and the roughness error based on the plane fitting error; establishing a safe landing probability map based on the slope error and the roughness error, and determining the safe landing area.

[0008] As a preferred solution of the method for selecting a safe landing area based on slope and roughness error estimation described in the present invention, the terrain data collection includes using a lidar sensor to obtain point cloud data of the terrain and generate a digital elevation model.

[0009] As a preferred solution of the method for selecting a safe landing area based on slope and roughness error estimation of the present invention, the plane fitting and plane fitting error estimation include calculating the size of the terrain block for plane fitting according to the lander size and the guidance error, each terrain block covers N digital elevation model data points, and according to the coordinate values ​​of the N data points in the terrain block, the average plane of the terrain block is fitted by the least squares method to construct a plane fitting equation, which is expressed as:

[0010] k 1 X+k 2 Y+k 3 Z=1

[0011] Among them, k 1 , k 2 and k 3 are the plane fitting parameters, X, Y and Z are the three-dimensional coordinates of the target point obtained by the lidar scanner; the N data points in the terrain block are recorded as an N×3 coordinate matrix G, which is expressed as:

[0012]

[0013] Among them, (x l ,y l , z l )(l=1, 2, ..., N) are the coordinates of N data points in each terrain block; construct the plane fitting error equation, expressed as:

[0014] V=Gk-h

[0015] γ=[v 1 v 2 ... v N ] T , v l (l=1, 2, ..., N)

[0016] h=[1 1 ... 1]T

[0017] Where V is a column vector containing all correction numbers, v l is the lth correction number, k is the parameter vector of plane fitting, h is a column vector of all 1s, Gk is the fitting plane, and T is the transpose operation of the matrix; the plane fitting parameter vector is calculated, which is expressed as:

[0018] k=[k 1 k 2 k 3 ] T =(G T G) -1 G T h

[0019] Based on the error propagation theorem, the measurement error is propagated to the plane fitting error, and the variance matrix of the plane fitting parameter vector is calculated, which is expressed as:

[0020]

[0021] Among them, D kk is the variance matrix of the plane fitting parameter vector, is the unit weight variance estimate, and F is the weight matrix.

[0022] As a preferred solution of the method for selecting a safe landing area based on slope and roughness error estimation of the present invention, the estimating the slope error based on the plane fitting error includes propagating the plane fitting error to the slope error, defining the angle between the normal vector n of the fitted plane and the Z axis of the coordinate system as the slope α of the terrain block, and the normal vector n of the fitted plane is expressed as:

[0023] n=[k 1 k 2 k 3 ] T

[0024] Calculate the slope α of the terrain block, expressed as:

[0025]

[0026] b=[0 0 1] T

[0027] Where b is the vertical vector. Based on the error propagation theorem, the slope calculation formula is linearized and the slope propagation coefficient matrix A is constructed, which is expressed as:

[0028]

[0029] Among them, C 1 , C 2 and C 2To simplify the parameters; calculate the simplified parameter C 1 , C 2 and C 2 , expressed as:

[0030]

[0031] According to the variance matrix of the plane fitting parameter vector and the propagation coefficient matrix of the slope, the variance of the slope is calculated, which is expressed as:

[0032]

[0033] in, is the variance of the slope, is the slope error.

[0034] As a preferred solution of the method for selecting a safe landing area based on slope and roughness error estimation of the present invention, the roughness error estimation based on the plane fitting error includes propagating the plane fitting error and the digital elevation model point error to the roughness error, defining the maximum value of the distance between each data point in the terrain block and the fitted plane as the roughness, and calculating the roughness r, which is expressed as:

[0035]

[0036] r=max(d l )

[0037] Among them, d l is the distance between each data point in the terrain block and the fitted plane; calculate the variance D of the digital elevation model point xx , expressed as:

[0038]

[0039] in, is the covariance matrix of the mth lidar sampling point, w m is the weight of the mth lidar sampling point, and M is the total number of lidar sampling points. Based on the error propagation theorem, the measurement error is propagated to the digital elevation model point error, and the covariance matrix D of the lidar sampling points is constructed. x，Y，Z , expressed as:

[0040]

[0041] Where S is the distance between the scanner and the target point, θ is the zenith angle, is the azimuth, K is the propagation coefficient matrix of the laser radar measurement error, δ S is the ranging error, δ θ and is the angle measurement error, is the covariance matrix of distance and angle measurement; based on the error propagation theorem, the roughness calculation formula is linearized and the roughness propagation coefficient matrix B is constructed, which is expressed as:

[0042]

[0043]

[0044] Among them, p is the indicator of the value of the partial derivative term; the value constraint of p is defined as:

[0045]

[0046] According to the covariance matrix of the plane fitting parameter vector and the digital elevation model point, and the propagation coefficient matrix of the roughness, the variance of the roughness is calculated, which is expressed as:

[0047]

[0048] in, is the variance of roughness, is the roughness error, and D is the covariance matrix between the plane fitting parameter vector and the digital elevation model points.

[0049] As a preferred solution of the method for selecting a safe landing area based on slope and roughness error estimation according to the present invention, the establishment of a safe landing probability map includes generating a Gaussian distribution of slope and roughness based on the slope error and the roughness error, which is expressed as:

[0050]

[0051] Among them, S m is the slope value based on the slope error, R is the roughness value based on the roughness error; the safe landing probability of the slope and roughness is calculated and expressed as:

[0052]

[0053] Among them, P α (safe) is the safe landing probability of the slope, P r (safe) is the safe landing probability of roughness, Φ(·) is the cumulative distribution function of the standard normal distribution, T α is the slope threshold, T r is the roughness threshold; according to the safe landing probability of slope and roughness, the probability value of the safe landing probability map is calculated, which is expressed as:

[0054]

[0055] Wherein, P(i, j) is the safe landing probability value of the pixel in the i-th row and j-th column in the safe landing probability map; when the safe landing probability of the slope is less than or equal to the safe landing probability of the roughness, the probability value of the safe landing probability map is the safe landing probability of the slope; when the safe landing probability of the slope is greater than the safe landing probability of the roughness, the probability value of the safe landing probability map is the safe landing probability of the roughness.

[0056] As a preferred solution of the method for selecting a safe landing area based on slope and roughness error estimation described in the present invention, wherein: the determination of the safe landing area includes calculating the minimum size of the landing area according to the size of the lander and the guidance error, setting the search step size to be smaller than the minimum size of the landing area, and gradually searching for the safe landing area; calculating the mean value of the safe landing probability in each landing area If the mean probability of a safe landing is Greater than the search threshold T p , then it is a candidate safe area; if the mean of the safe landing probability Less than the search threshold T p , then it is a danger zone.

[0057] Another object of the present invention is to provide a safe landing area selection system based on slope and roughness error estimation, which can solve the problem of inaccurate identification caused by measurement errors in current topographic surveying technology by estimating slope error and roughness error based on plane fitting error.

[0058] As a preferred solution of the safe landing area selection system based on slope and roughness error estimation described in the present invention, it includes: a plane fitting module, an error propagation module, and a safe landing area module; the plane fitting module is used to collect data on the terrain, perform plane fitting and estimate the plane fitting error; the error propagation module is used to estimate the slope error and the roughness error based on the plane fitting error; the safe landing area module is used to establish a safe landing probability map based on the slope error and the roughness error, and determine the safe landing area.

[0059] A computer device includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of a method for selecting a safe landing area based on slope and roughness error estimation.

[0060] A computer-readable storage medium stores a computer program, which, when executed by a processor, implements the steps of a method for selecting a safe landing area based on slope and roughness error estimation.

[0061] Beneficial effects of the present invention: The method for selecting a safe landing area based on slope and roughness error estimation provided by the present invention obtains point cloud data of the terrain by using a lidar sensor to generate a digital elevation model, and can perform high-precision data collection on the terrain. The advantage of using a lidar is its high resolution and high precision, and accurate three-dimensional terrain features can be obtained even in areas with complex environments. These characteristics are used to achieve detailed modeling of the terrain, and the data is plane-fitted by the least squares method to accurately calculate the error of the fitting plane. The distribution of the terrain data and its uncertainty are estimated by constructing a plane fitting equation and an error equation, which lays a solid foundation for slope and roughness error analysis. By calculating the propagation of the plane fitting error to the slope error and the roughness error, a quantitative evaluation of the uncertainty of the terrain features is achieved, the slope of the terrain block is calculated using the angle between the normal vector and the Z-axis, and a propagation coefficient matrix of the slope error is established, which can accurately estimate the slope. Error, by analyzing the plane fitting error and its propagation between various points in the terrain, the roughness error is calculated. The roughness error reflects the irregularity of the terrain surface. The comprehensive use of error propagation theory and geometric calculation method effectively reveals the impact of terrain uncertainty on the safe landing process. By analyzing the slope and roughness errors, a more accurate assessment of the terrain can be made, which is of key significance for judging landing safety. A safe landing probability map is established through the Gaussian distribution generated by the slope error and the roughness error, and the probability quantification of multi-dimensional terrain factors is realized, breaking through the limitations of traditional fixed value evaluation. The analysis operation of probability values ​​is used to calculate the safe landing probability of each area, and combined with the set threshold and average value analysis, the candidate safe landing area is accurately located. Through a high-precision and efficient analysis method, the safe landing position of the lander can be more accurately judged in complex terrain. The present invention achieves better results in accuracy, adaptability and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for use in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0063] Figure 1 An overall flow chart of a method for selecting a safe landing area based on slope and roughness error estimation provided for the first embodiment of the present invention.

[0064] Figure 2 A safe landing probability map and a landing area selection map of a safe landing area selection method based on slope and roughness error estimation are provided in the second embodiment of the present invention.

[0065] Figure 3 A diagram of obstacle recognition results without considering the error method of a safe landing area selection method based on slope and roughness error estimation provided in the second embodiment of the present invention.

[0066] Figure 4 A reference obstacle recognition result diagram of a safe landing area selection method based on slope and roughness error estimation provided in the second embodiment of the present invention.

[0067] Figure 5 A module schematic diagram of a safe landing area selection system based on slope and roughness error estimation provided in the third embodiment of the present invention. DETAILED DESCRIPTION

[0068] In order to make the above-mentioned purposes, features and advantages of the present invention more obvious and easy to understand, the specific implementation methods of the present invention are described in detail below in conjunction with the drawings of the specification. Obviously, the described embodiments are part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in the art without creative work should fall within the scope of protection of the present invention.

[0069] Example 1, reference Figure 1 , which is an embodiment of the present invention, provides a method for selecting a safe landing area based on slope and roughness error estimation, comprising:

[0070] S1: Collect terrain data, perform plane fitting and estimate plane fitting error.

[0071] Furthermore, data collection of the terrain includes using a lidar sensor to obtain point cloud data of the terrain and generate a digital elevation model (DEM).

[0072] It should be noted that plane fitting and plane fitting error estimation include calculating the size of the terrain block for plane fitting according to the lander size and guidance error. Each terrain block covers N digital elevation model data points. According to the coordinate values ​​of the N data points in the terrain block, the average plane of the terrain block is fitted by the least squares method, and the plane fitting equation is constructed, which is expressed as:

[0073] k 1 X+k 2 Y+k 3 Z=1

[0074] Among them, k 1 , k 2 and k 3 are the plane fitting parameters, X, Y and Z are the three-dimensional coordinates of the target point obtained by the lidar scanner; the N data points in the terrain block are recorded as an N×3 coordinate matrix G, which is expressed as:

[0075]

[0076] Among them, (x l ,y l , z l )(l=1, 2, ..., N) are the coordinates of N data points in each terrain block; construct the plane fitting error equation, expressed as:

[0077] V=Gk-h

[0078] γ=[v 1 v 2 ... v N ] T , v l (l=1, 2, ..., N)

[0079] h=[1 1 ... 1] T

[0080] Where V is a column vector containing all corrections, vl is the lth correction, k is a plane fitting parameter vector, h is a column vector of all 1s, Gk is the fitting plane, and T is the transpose operation of the matrix. The plane fitting parameter vector is calculated as follows:

[0081] k=[k 1 k 2 k 3 ] T =(G T G) -1 G T h

[0082] Based on the error propagation theorem, the measurement error is propagated to the plane fitting error, and the variance matrix of the plane fitting parameter vector is calculated, which is expressed as:

[0083]

[0084] Among them, D kk is the variance matrix of the plane fitting parameter vector, is the unit weight variance estimate, and F is the weight matrix.

[0085] It should also be noted that the use of LiDAR to obtain point cloud data has the advantages of high precision and high resolution. Compared with GPS and photogrammetry, LiDAR can provide accurate three-dimensional surface features in different environments, and it is efficient and real-time. The use of LiDAR can effectively support the generation of digital elevation models to accurately reflect subtle changes in terrain. The DEM data is fitted with a plane through the least squares method. According to the error propagation theorem, the measurement error is propagated to the plane fitting error, and the plane fitting error is calculated to make the fitting model more in line with the ideal state. An accurate terrain representative plane is obtained, which improves the accuracy of terrain processing.

[0086] S2: Estimate slope error and roughness error based on plane fitting error.

[0087] Furthermore, estimating the slope error based on the plane fitting error includes propagating the plane fitting error to the slope error, defining the angle between the normal vector n of the fitted plane and the Z axis of the coordinate system as the slope α of the terrain block, and the normal vector n of the fitted plane is expressed as:

[0088] n=[k 1 k 2 k 3 ] T

[0089] Calculate the slope α of the terrain block, expressed as:

[0090]

[0091] b=

[001] T

[0092] Where b is the vertical vector. Based on the error propagation theorem, the slope calculation formula is linearized and the slope propagation coefficient matrix A is constructed, which is expressed as:

[0093]

[0094] Among them, C 1 , C 2 and C 2 To simplify the parameters; calculate the simplified parameter C 1 , C 2 and C 2 , expressed as:

[0095]

[0096] According to the variance matrix of the plane fitting parameter vector and the propagation coefficient matrix of the slope, the variance of the slope is calculated, which is expressed as:

[0097]

[0098] in, is the variance of the slope, is the slope error.

[0099] It should be noted that estimating the roughness error based on the plane fitting error includes propagating the plane fitting error and the digital elevation model point error to the roughness error, defining the maximum value of the distance between each data point in the terrain block and the fitted plane as the roughness, and calculating the roughness r, which is expressed as:

[0100]

[0101] r=max(d l )

[0102] Among them, d l is the distance between each data point in the terrain block and the fitted plane; calculate the variance D of the digital elevation model point xx , expressed as:

[0103]

[0104] in, is the covariance matrix of the mth lidar sampling point, w m is the weight of the mth lidar sampling point, and M is the total number of lidar sampling points. Based on the error propagation theorem, the measurement error is propagated to the digital elevation model point error, and the covariance matrix D of the lidar sampling points is constructed. X，Y，Z , expressed as:

[0105]

[0106] Where S is the distance between the scanner and the target point, θ is the zenith angle, is the azimuth, K is the propagation coefficient matrix of the laser radar measurement error, δ S is the ranging error, δ θ and is the angle measurement error, is the covariance matrix of distance and angle measurement; based on the error propagation theorem, the roughness calculation formula is linearized and the roughness propagation coefficient matrix B is constructed, which is expressed as:

[0107]

[0108] Among them, p is the indicator of the value of the partial derivative term; the value constraint of p is defined as:

[0109]

[0110] According to the covariance matrix of the plane fitting parameter vector and the digital elevation model point, and the propagation coefficient matrix of the roughness, the variance of the roughness is calculated, which is expressed as:

[0111]

[0112] in, is the variance of roughness, is the roughness error, and D is the covariance matrix between the plane fitting parameter vector and the digital elevation model points.

[0113] It should also be noted that the slope error is calculated by determining the slope through the angle between the normal vector and the Z axis. Combining the error propagation theory, the slope calculation formula is linearized and simplified by entering the parameters, C 1 is the cosine value of the slope angle, C 2 is the weight factor in the slope variance propagation, which plays an adjustment role in the error propagation. In particular, the larger the plane normal vector modulus is, the smaller the weight of the propagation error is. 3 It is the reciprocal of the modulus of the plane normal vector and is used to directly represent the normalization of the normal vector. It is one of the key factors in calculating error propagation. It combines error analysis with geometric calculation to effectively estimate the uncertainty of the slope and obtain the propagation coefficient matrix of the slope error. It effectively integrates mathematical derivation and error estimation technology to improve the solution analysis capability under known error conditions. The roughness calculation is analyzed by combining point deviation with plane deviation and is defined as the error of the maximum distance. It intuitively reflects the irregularity of the terrain surface. It uses differential calculation combined with the covariance matrix for error propagation to accurately evaluate the calculation weights of terrain features and provide a reliable data basis for the subsequent safe landing probability.

[0114] S3: Based on the slope error and roughness error, a safe landing probability map is established and a safe landing area is determined.

[0115] Furthermore, establishing a safe landing probability map includes generating Gaussian distributions of slope and roughness based on the slope error and the roughness error, which can be expressed as:

[0116]

[0117] Among them, S m is the slope value based on the slope error, R is the roughness value based on the roughness error; the safe landing probability of the slope and roughness is calculated and expressed as:

[0118]

[0119] Among them, P α (safe) is the safe landing probability of the slope, P r (safe) is the safe landing probability of roughness, Φ(·) is the cumulative distribution function of the standard normal distribution, T α is the slope threshold, T r is the roughness threshold; according to the safe landing probability of slope and roughness, the probability value of the safe landing probability map is calculated, which is expressed as:

[0120]

[0121] Wherein, P(i, j) is the safe landing probability value of the pixel in the i-th row and j-th column in the safe landing probability map; when the safe landing probability of the slope is less than or equal to the safe landing probability of the roughness, the probability value of the safe landing probability map is the safe landing probability of the slope; when the safe landing probability of the slope is greater than the safe landing probability of the roughness, the probability value of the safe landing probability map is the safe landing probability of the roughness.

[0122] It should be noted that determining the safe landing area includes calculating the minimum size of the landing area according to the size of the lander and the guidance error, setting the search step size smaller than the minimum size of the landing area, and gradually searching for the safe landing area; calculating the mean of the safe landing probability in each landing area If the mean probability of a safe landing is Greater than the search threshold T p , then it is a candidate safe area; if the mean of the safe landing probability Less than the search threshold T p , then it is a danger zone.

[0123] It should also be noted that the use of the Gaussian distribution characteristics of slope and roughness to construct their respective safe landing probabilities can provide intuitive quantification of multidimensional factors of complex terrain, which breaks through the limitations of traditional fixed value evaluation. The application of Gaussian distribution characteristics combined with the analysis of probability values ​​has laid a foundation for a set of feasible mathematical models for determining safe landing areas, improving the scientific nature of decision-making, and using the set threshold and mean analysis combined with the search step size to judge candidate safe areas, ensuring that the lander can be accurately positioned under restricted terrain conditions. The constructed safe landing probability map provides innovative technical support for identifying suitable landing areas, greatly improving the safety and predictability of landing.

[0124] Example 2, reference Figure 2-Figure 4 , which is an embodiment of the present invention, provides a method for selecting a safe landing area based on slope and roughness error estimation. In order to verify the beneficial effects of the present invention, scientific demonstration is carried out through economic benefit calculation and simulation experiments.

[0125] In order to further verify the effectiveness of the safe landing zone selection method based on slope and roughness error estimation of the present invention, in the experiment, by simulating complex terrain surface data, laser radar (LiDAR) point cloud data with measurement errors was generated on the simulated terrain. Subsequently, the method of the present invention and the method without considering the error were respectively applied to perform obstacle identification and safe zone selection, so as to verify the effect of the method of the present invention through comparative analysis. The parameters required for the experiment refer to Table 1.

[0126] Table 1 Experimental parameters

[0127] parameter value LiDAR measures altitude 100m LiDAR ranging error 0.05cm LiDAR horizontal angle error 94.5″ LiDAR vertical angle error 42.9″ Slope Threshold 8° Roughness Threshold 0.2m

[0128] Figure 2 The safe landing probability map and landing area selection obtained by the method of the present invention are shown in the figure. The selected landing area is marked with a circle. Figure 3 is the obstacle recognition result obtained by the method without considering the error. Figure 4 The reference obstacle recognition result obtained from the laser point cloud data without measurement error can be seen. It can be seen that there is a significant difference between the obstacle recognition result without considering the error and the reference obstacle recognition result. In contrast, the safe landing probability map obtained by the method of the present invention is more consistent with the reference obstacle map. Analysis of the slope distribution in the simulated terrain shows that the slope in the area with differences is about 8°. When the obstacle recognition does not consider the measurement error, the slope obstacles near the threshold may be ignored, while the method of the present invention can identify obstacles near the threshold. Table 2 lists the safe landing probability values ​​of the landing area selected by the method of the present invention. The safe landing probability value of the landing area is 0.8815, indicating that there is a certain landing risk in the landing area.

[0129] Table 2 Safe landing probability values ​​of selected landing areas

[0130] method Probability of safe landing Method of the present invention 0.8815

[0131] Under the influence of measurement errors, there is a large difference between the obstacle identification result without considering the error and the reference obstacle identification result, and the method without considering the error can only obtain deterministic obstacle identification and safe landing area search results, while the method of the present invention can identify slope obstacles close to the slope obstacle threshold, thereby being able to identify potential obstacles that cannot be identified by the method without considering the error, and evaluate the landing safety of the area in the form of probability to search for a safer landing area. The probability search threshold of the safe area can be adjusted according to the requirements of the landing mission, and the safe landing area that meets the safety requirements of the landing mission can be searched through the safe landing probability value. Therefore, the present invention is creative.

[0132] Example 3, reference Figure 5 , as an embodiment of the present invention, provides a safe landing area selection system based on slope and roughness error estimation, including a plane fitting module, an error propagation module, and a safe landing area module.

[0133] The plane fitting module is used to collect terrain data, perform plane fitting and estimate the plane fitting error; the error propagation module is used to estimate the slope error and roughness error based on the plane fitting error; the safe landing area module is used to establish a safe landing probability map based on the slope error and roughness error, and determine the safe landing area.

[0134] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the methods of each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0135] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0136] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering, or processing in another suitable manner as necessary, and then stored in a computer memory.

[0137] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc. It should be noted that the above embodiments are only used to illustrate the technical solution of the present invention and are not limited. Although the present invention is described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention, which should be included in the scope of the claims of the present invention.

[0138] 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 preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

Claims

1. A method for selecting a safe landing area based on slope and roughness error estimation, characterized in that: include: Collect terrain data, perform plane fitting and estimate plane fitting errors; Estimate slope error and roughness error based on plane fitting error; Based on the slope error and roughness error, a safe landing probability map is established and the safe landing area is determined.

2. The method for selecting a safe landing area based on slope and roughness error estimation according to claim 1, characterized in that: The data collection of the terrain includes using a laser radar sensor to obtain point cloud data of the terrain and generate a digital elevation model.

3. The method for selecting a safe landing area based on slope and roughness error estimation according to claim 2, characterized in that: The plane fitting and plane fitting error estimation include calculating the size of the terrain block for plane fitting according to the lander size and the guidance error, each terrain block covers N digital elevation model data points, and according to the coordinate values ​​of the N data points in the terrain block, the average plane of the terrain block is fitted by the least square method, and the plane fitting equation is constructed, which is expressed as: k1X+k2Y+k3Z=1 Among them, k1, k2 and k3 are plane fitting parameters, and X, Y and Z are the three-dimensional coordinates of the target point obtained by the lidar scanner; The N data points in the terrain block are recorded as an N×3 coordinate matrix G, expressed as: Among them, (x l ,y l ,z l )(l=1,2,...,N) are the coordinates of N data points in each terrain block; Construct the plane fitting error equation, expressed as: V=Gk-h V=[v1 v2 … v N ] T ,v l (l=1,2,...,N h＝[1 1 … 1] T Where V is a column vector containing all correction numbers, v l is the lth correction number, k is the parameter vector of plane fitting, h is the column vector of all 1s, Gk is the fitting plane, and T is the transpose operation of the matrix; Calculate the plane fitting parameter vector, expressed as: k=[k1 k2 k3] T =(G T G) -1 G T h Based on the error propagation theorem, the measurement error is propagated to the plane fitting error, and the variance matrix of the plane fitting parameter vector is calculated, which is expressed as: Among them, D kk is the variance matrix of the plane fitting parameter vector, is the unit weight variance estimate, and F is the weight matrix.

4. The method for selecting a safe landing area based on slope and roughness error estimation according to claim 3, characterized in that: The method of estimating the slope error based on the plane fitting error includes propagating the plane fitting error to the slope error, defining the angle between the normal vector n of the fitted plane and the Z axis of the coordinate system as the slope α of the terrain block, and the normal vector n of the fitted plane is expressed as: n=[k1 k2 k3] T Calculate the slope α of the terrain block, expressed as: b＝[0 0 1] T Where, b is the vertical vector; Based on the error propagation theorem, the slope calculation formula is linearized and the slope propagation coefficient matrix A is constructed, which is expressed as: Among them, C1, C2 and C2 are simplified parameters; The simplified calculation parameters C1, C2 and C2 are expressed as: According to the variance matrix of the plane fitting parameter vector and the propagation coefficient matrix of the slope, the variance of the slope is calculated, which is expressed as: in, is the variance of the slope, is the slope error.

5. The method for selecting a safe landing area based on slope and roughness error estimation according to claim 4, characterized in that: The roughness error estimation based on the plane fitting error includes propagating the plane fitting error and the digital elevation model point error to the roughness error, defining the maximum value of the distance between each data point in the terrain block and the fitted plane as the roughness, and calculating the roughness r, which is expressed as: r=max(d l ) Among them, d l is the distance between each data point in the terrain block and the fitted plane; Calculate the variance D of the digital elevation model points xx , expressed as: in, is the covariance matrix of the mth lidar sampling point, w m is the weight of the mth lidar sampling point, and M is the total number of lidar sampling points; Based on the error propagation theorem, the measurement error is propagated to the point error of the digital elevation model, and the covariance matrix D of the lidar sampling points is constructed. X,Y,Z , expressed as: Where S is the distance between the scanner and the target point, θ is the zenith angle, is the azimuth, K is the propagation coefficient matrix of the laser radar measurement error, δ S is the ranging error, δ θ and is the angle measurement error, is the covariance matrix of distance and angle measurement; Based on the error propagation theorem, the roughness calculation formula is linearized and the roughness propagation coefficient matrix B is constructed, which is expressed as: Among them, p is an indicator of the value of the partial derivative term; Define the value constraints of p, expressed as: According to the covariance matrix of the plane fitting parameter vector and the digital elevation model point, and the propagation coefficient matrix of the roughness, the variance of the roughness is calculated, which is expressed as: in, is the variance of roughness, is the roughness error, and D is the covariance matrix between the plane fitting parameter vector and the digital elevation model points.

6. The method for selecting a safe landing area based on slope and roughness error estimation according to claim 5, characterized in that: The establishing of the safe landing probability map includes generating a Gaussian distribution of the slope and the roughness based on the slope error and the roughness error, which is expressed as: Among them, S m is the slope value based on the slope error, R is the roughness value based on the roughness error; Calculate the safe landing probability for slope and roughness, expressed as: Among them, P α (safe) is the safe landing probability of the slope, P r (safe) is the safe landing probability of roughness, Φ(·) is the cumulative distribution function of the standard normal distribution, T α is the slope threshold, T r is the roughness threshold; According to the safe landing probability of slope and roughness, the probability value of the safe landing probability diagram is calculated, which is expressed as: Wherein, P(i,j) is the safe landing probability value of the pixel in the i-th row and j-th column in the safe landing probability map; When the safe landing probability of the slope is less than or equal to the safe landing probability of the roughness, the probability value of the safe landing probability graph is the safe landing probability of the slope; When the safe landing probability of the slope is greater than the safe landing probability of the roughness, the probability value of the safe landing probability graph is the safe landing probability of the roughness.

7. The method for selecting a safe landing area based on slope and roughness error estimation according to claim 6, characterized in that: Determining the safe landing area includes calculating the minimum size of the landing area according to the size of the lander and the guidance error, setting the search step size to be smaller than the minimum size of the landing area, and gradually searching for the safe landing area; Calculate the mean of the safe landing probability in each landing zone If the mean probability of a safe landing is Greater than the search threshold T p , then it is a candidate safe area; If the mean probability of a safe landing is Less than the search threshold T p , then it is a danger zone.

8. A system using the method for selecting a safe landing area based on slope and roughness error estimation as claimed in any one of claims 1 to 7, characterized in that: Including plane fitting module, error propagation module, safe landing area module; The plane fitting module is used to collect terrain data, perform plane fitting and estimate plane fitting errors; The error propagation module is used to estimate the slope error and the roughness error based on the plane fitting error; The safe landing area module is used to establish a safe landing probability map based on the slope error and the roughness error, and determine the safe landing area.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method for selecting a safe landing area based on slope and roughness error estimation described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method for selecting a safe landing area based on slope and roughness error estimation described in any one of claims 1 to 7 are implemented.

Images