Method, device, equipment and medium for calculating grid size of grid map

Abstract

A method, device, equipment and medium for calculating the grid size of a grid map, belonging to the field of geographic information technology. The method for calculating the grid size of a grid map includes: obtaining a map accuracy loss function and a map information loss function whose pre-constructed variables are the grid size; adding the map accuracy loss function and the map information loss function to obtain a total map accuracy loss function; based on the total map accuracy loss function, determining the value of the grid size, solving the technical problem of blindness and uncertainty in the related art of determining the grid size by relying on experience, and improving the accuracy of determining the grid size.

Description

Technical Field

[0001] The present application relates to the field of geographic information technology, and in particular to a method, device, equipment and computer-readable storage medium for calculating the grid size of a grid map. Background Art

[0002] Compared with environmental maps and topological maps, raster maps have outstanding advantages, such as: easy to create and save, good preservation of important information in the environment, easy to update the map, and high map accuracy. Therefore, raster maps are widely used in the field of mobile robots. Grid size is an important parameter for constructing raster maps, and how to accurately determine the value of the grid size is becoming increasingly important. In related technologies, when constructing raster maps, relevant personnel usually rely on experience to determine the grid size, which is blind and uncertain, and further leads to the low accuracy of the constructed raster map. Summary of the invention

[0003] The present application provides a grid size calculation method, device, equipment and computer-readable storage medium for a grid map, which can solve the technical problems existing in the prior art of relying on experience to determine the grid size, which is blind and uncertain, and further leads to low accuracy of the constructed grid map.

[0004] In a first aspect, an embodiment of the present application provides a method for calculating the grid size of a grid map, and the method for calculating the grid size of a grid map includes:

[0005] Get the pre-built map accuracy loss function and map information loss function whose variables are grid size;

[0006] Adding the map accuracy loss function and the map information loss function to obtain a total map accuracy loss function;

[0007] Based on the total map accuracy loss function, a value of the grid size is determined.

[0008] In combination with the first aspect, in one implementation, the process of constructing a map accuracy loss function where the variable is a grid size includes:

[0009] Taking the grid size as a variable, determine the first probability expression that the measured points obey the uniform distribution;

[0010] Calculate the distance error between the measured point and the point where the measured value corresponding to the measured point falls, and determine the Gaussian distribution probability density function corresponding to the distance error;

[0011] Integrate the Gaussian distribution probability density function to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid;

[0012] Based on the first probability expression and the second probability expression, determine a third probability expression where the measured value falls within the target grid;

[0013] Based on the third probability expression, a map accuracy loss function with the variable being the grid size is constructed.

[0014] In combination with the first aspect, in one implementation, the calculating the distance error between the measured point and the point where the measurement value corresponding to the measured point falls includes:

[0015] Obtain the actual distance between the laser radar and the measured point, and the measured distance between the laser radar and the corresponding measurement value landing point of the measured point;

[0016] Based on the actual distance and the measured distance, a distance error between the measured point and a point where the measured value corresponding to the measured point falls is calculated.

[0017] In combination with the first aspect, in one implementation, integrating the Gaussian distribution probability density function to obtain a second probability expression in which both the measured point and the measurement value corresponding to the measured point fall within the target grid includes:

[0018] Performing Taylor series expansion on the Gaussian distribution probability density function to obtain a Taylor series expanded probability density function;

[0019] The probability density function after Taylor series expansion is integrated to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid.

[0020] In combination with the first aspect, in one implementation, constructing a map accuracy loss function with a variable being a grid size based on the third probability expression includes:

[0021] The Simpson formula is used to perform an approximate calculation of the definite integral of the third probability expression, and a map accuracy loss function with the variable being the grid size is constructed.

[0022] In combination with the first aspect, in one implementation, determining the value of the grid size based on the total map accuracy loss function includes:

[0023] The grid size is taken as a variable, the total map accuracy loss function is derived with respect to the grid size, and the derived total map accuracy loss function is set to zero, and the grid size is solved to obtain the value of the grid size.

[0024] In a second aspect, an embodiment of the present application provides a grid size calculation device for a grid map, wherein the grid size calculation device for a grid map includes:

[0025] A first acquisition module is used to acquire a map accuracy loss function and a map information loss function whose pre-constructed variables are grid size;

[0026] A first determination module, configured to add the map accuracy loss function and the map information loss function to obtain a total map accuracy loss function;

[0027] The second determination module is used to determine the value of the grid size based on the total map accuracy loss function.

[0028] In conjunction with the second aspect, in one implementation, the grid size calculation device for the grid map further includes a construction module, and the construction module is specifically used to:

[0029] Taking the grid size as a variable, determine the first probability expression that the measured points obey the uniform distribution;

[0030] Calculate the distance error between the measured point and the point where the measured value corresponding to the measured point falls, and determine the Gaussian distribution probability density function corresponding to the distance error;

[0031] Integrate the Gaussian distribution probability density function to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid;

[0032] Based on the first probability expression and the second probability expression, determine a third probability expression where the measured value falls within the target grid;

[0033] Based on the third probability expression, a map accuracy loss function with the variable being the grid size is constructed.

[0034] In conjunction with the second aspect, in one implementation, the building block is specifically used for:

[0035] Obtain the actual distance between the laser radar and the measured point, and the measured distance between the laser radar and the corresponding measurement value landing point of the measured point;

[0036] Based on the actual distance and the measured distance, a distance error between the measured point and a point where the measured value corresponding to the measured point falls is calculated.

[0037] In conjunction with the second aspect, in one implementation, the building block is specifically used for:

[0038] Performing Taylor series expansion on the Gaussian distribution probability density function to obtain a Taylor series expanded probability density function;

[0039] The probability density function after Taylor series expansion is integrated to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid.

[0040] In conjunction with the second aspect, in one implementation, the building block is specifically used for:

[0041] The Simpson formula is used to perform an approximate calculation of the definite integral of the third probability expression, and a map accuracy loss function with the variable being the grid size is constructed.

[0042] In conjunction with the second aspect, in one implementation, the second determining module is specifically configured to:

[0043] The grid size is taken as a variable, the total map accuracy loss function is derived with respect to the grid size, and the derived total map accuracy loss function is set to zero, and the grid size is solved to obtain the value of the grid size.

[0044] In a third aspect, an embodiment of the present application provides a grid size calculation device for a grid map, the grid size calculation device for a grid map comprising a processor, a memory, and a grid size calculation program for a grid map stored in the memory and executable by the processor, wherein when the grid size calculation program for a grid map is executed by the processor, the steps of the grid size calculation method for a grid map as described in any one of the first aspects are implemented.

[0045] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which a grid size calculation program for a grid map is stored. When the grid size calculation program for a grid map is executed by a processor, the steps of the method for calculating the grid size of a grid map as described in any one of the first aspects are implemented.

[0046] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0047] Firstly, a map accuracy loss function and a map information loss function with the pre-constructed variable being the grid size are obtained; then the map accuracy loss function and the map information loss function are added to obtain a total map accuracy loss function; further based on the total map accuracy loss function, the value of the grid size is determined, thereby solving the technical problem of blindness and uncertainty in the related art of determining the grid size by relying on experience, and improving the accuracy of determining the grid size. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 This is a flow chart of a first embodiment of a method for calculating grid size of a grid map of the present application;

[0049] Figure 2 A schematic diagram of the locations of measured points and the measurement values ​​corresponding to the measured points provided by an embodiment of the present application;

[0050] Figure 3 A schematic diagram of functional modules of an embodiment of a grid size calculation device for a grid map of the present application;

[0051] Figure 4 Schematic diagram of the hardware structure of a grid size calculation device for a grid map involved in an embodiment of the present application. DETAILED DESCRIPTION

[0052] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0053] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0054] In a first aspect, an embodiment of the present application provides a method for calculating the grid size of a grid map.

[0055] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the first embodiment of the method for calculating the grid size of a grid map of the present application. Figure 1 As shown, the grid size calculation method of the grid map includes:

[0056] Step 110, obtaining a map accuracy loss function and a map information loss function whose pre-constructed variables are grid size;

[0057] It can be understood that in practical applications, map accuracy is composed of map accuracy and map information volume, and both map accuracy and map information volume are related to the grid size. The smaller the grid size, the smaller the grid inclusion, the lower the possibility that the true position of the measured point and the laser point position fall on the same grid, and the map description of occupied areas and free areas will deviate, resulting in reduced accuracy; the larger the grid size, the fewer grids there will be when the map area is constant, and each grid independently represents the information occupied by the current grid area. When the grid becomes larger, the map details will be reduced, resulting in a decrease in information volume.

[0058] The present application obtains pre-constructed map accuracy loss function and map information loss function with the variable being grid size, and then constructs a total map accuracy loss function with the variable being grid size based on the map accuracy loss function and the map information loss function, and further determines the value of the grid size based on the total map accuracy loss function.

[0059] In one embodiment, the pre-constructed variable is the map information loss function of the grid size corresponding to the following formula (1):

[0060] F1(R)=kR 2 (1);

[0061] Among them, k is the weight coefficient, and k>0, which can be flexibly set according to actual needs; R is the grid size.

[0062] Furthermore, in one embodiment, the process of constructing a map accuracy loss function where the variable is a grid size includes:

[0063] Step 210, using the grid size as a variable, determining a first probability expression that the measured points obey a uniform distribution;

[0064] Figure 2 A schematic diagram of the measured points and the corresponding measurement value locations provided by one embodiment of the present application. Figure 2 As shown, m i is a certain grid, O is the center of the grid, R is the grid size, L is the distance from the laser radar to point O, Z is the measured point (X0, Y0), and M is the measurement value point corresponding to the measured point (X, Y).

[0065] The measured point can be any point uniformly distributed in the distance direction of the grid, and the distance direction is the horizontal direction between the laser radar and the measured point.

[0066] It can be understood that the position of the measured point in the distance direction obeys uniform distribution, and the first probability expression that the measured point obeys uniform distribution corresponds to the following formula (2) as follows:

[0067]

[0068] Step 220, calculating the distance error between the measured point and the point where the measured value corresponding to the measured point falls, and determining the Gaussian distribution probability density function corresponding to the distance error;

[0069] In the specific implementation, the actual distance between the laser radar and the measured point and the measured distance between the laser radar and the measured value landing point corresponding to the measured point are obtained, where the measured distance is the distance between the laser radar location and the measured value landing point corresponding to the measured point output by the laser radar. The data output by the laser radar includes distance and angle. In order to simplify the calculation process, this application only considers the distance data output by the laser radar, and changes the two-dimensional model into a one-dimensional model.

[0070] Furthermore, the actual distance is subtracted from the measured distance and then the absolute value is taken to obtain the distance error between the measured point and the point where the measured value corresponding to the measured point falls.

[0071] Assuming that the laser radar distance measurement value is noisy, the distance error between the measured point and the corresponding measurement point of the measured point follows a Gaussian distribution. By performing statistical analysis on all the distance errors obtained, the mean μ can be determined. The standard deviation σ x The value depends on the physical properties of the LiDAR sensor itself and is calibrated by the manufacturer or manually. x After that, it can be determined that the Gaussian distribution probability density function f(x) corresponding to the distance error corresponds to the following formula (3):

[0072]

[0073] Step 230, integrating the Gaussian distribution probability density function to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid;

[0074] The actual position of a random measured point z and the corresponding measurement value point M fall on the target grid (i.e. the same grid) m i The second probability expression in corresponds to the following formula (4):

[0075]

[0076] Step 240: Determine a third probability expression that the measured value falls within the target grid based on the first probability expression and the second probability expression;

[0077] Furthermore, based on the first probability expression and the second probability expression, the third probability expression that the measured value falls within the target grid is determined using the total probability formula, corresponding to the following formula (5):

[0078]

[0079] Step 250: Based on the third probability expression, construct a map accuracy loss function whose variable is grid size.

[0080] Further, after obtaining the third probability expression, the following calculation is performed on the third probability expression to construct a map accuracy loss function F2(R) with the variable being the grid size, corresponding to the following formula (6):

[0081] F2(R)=1-P(M∈m i ) (6);

[0082] In this embodiment, the grid size is first used as a variable to determine a first probability expression that the measured point obeys a uniform distribution; the distance error between the measured point and the measurement value landing point corresponding to the measured point is calculated, and a Gaussian distribution probability density function corresponding to the distance error is determined; then the Gaussian distribution probability density function is integrated to obtain a second probability expression that the measured point and the measurement value landing point corresponding to the measured point both fall in the target grid; further based on the first probability expression and the second probability expression, a third probability expression is determined that the measurement value landing point falls in the target grid; and based on the third probability expression, a map accuracy loss function with the grid size as a variable is constructed to prepare for constructing a total map accuracy loss function.

[0083] Step 120: Add the map accuracy loss function and the map information loss function to obtain a total map accuracy loss function;

[0084] Furthermore, the map accuracy loss function F2(R) and the map information loss function F1(R) are added together to obtain the total map accuracy loss function J, which corresponds to the following formula (7):

[0085] J=F2(R)+F1(R)=1-P(M∈m i )+KR 2 (7);

[0086] Step 130: Determine the value of the grid size based on the total map accuracy loss function.

[0087] Then, the grid size is used as a variable, and the total map accuracy loss function is assigned a value according to the actual application scenario. Then, the grid size in the total map accuracy loss function is solved to obtain the value of the grid size.

[0088] In this embodiment, firstly, a map accuracy loss function and a map information loss function whose pre-constructed variables are grid size are obtained; then the map accuracy loss function and the map information loss function are added to obtain a total map accuracy loss function; further based on the total map accuracy loss function, the value of the grid size is determined, which solves the technical problem of relying on experience to determine the grid size in the related art, which is blind and uncertain, and improves the accuracy of determining the grid size.

[0089] Further, in one embodiment, the Gaussian distribution probability density function is integrated to obtain a second probability expression in which the measured point and the measurement value corresponding to the measured point both fall within the target grid, including:

[0090] Step 410, performing Taylor series expansion on the Gaussian distribution probability density function to obtain a probability density function after Taylor series expansion;

[0091] In the specific implementation, since the integral operation is directly performed on the basis of the Gaussian distribution probability density function, the process is relatively complicated. In order to simplify the calculation process and improve the calculation efficiency, the Gaussian distribution probability density function is subjected to Taylor series expansion to obtain the probability density function after Taylor series expansion.

[0092] Step 420: Integrate the probability density function after Taylor series expansion to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid.

[0093] Furthermore, after obtaining the probability density function after Taylor series expansion, the probability density function after Taylor series expansion is integrated to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid.

[0094] In this embodiment, the Gaussian distribution probability density function is first subjected to Taylor series expansion to obtain a probability density function after Taylor series expansion, and then the probability density function after Taylor series expansion is integrated to obtain a second probability expression in which the measured point and the measurement value corresponding to the measured point both fall within the target grid, which can simplify the calculation process, reduce the calculation difficulty, and improve the calculation efficiency.

[0095] Furthermore, in one embodiment, constructing a map accuracy loss function with a variable of grid size based on the third probability expression includes:

[0096] The Simpson formula is used to perform an approximate calculation of the definite integral of the third probability expression, and a map accuracy loss function with the variable being the grid size is constructed.

[0097] In specific implementation, in order to simplify the calculation process, the Simpson formula is used to perform an approximate calculation of the definite integral of the third probability expression, and a map accuracy loss function with the variable being the grid size is constructed.

[0098] Similarly, the second probability expression obtained by integrating the probability density function after the Taylor series expansion in step 420, that is, the measured point and the measured value corresponding to the measured point both fall in the target grid, can be substituted into the third probability expression to obtain a double integral expression. At this time, the Simpson formula can also be used to approximate the definite integral of the double integral expression, which can further simplify the calculation process and construct a map accuracy loss function with the grid size as a variable, corresponding to the following formula (8):

[0099]

[0100] In this embodiment, the Simpson formula is used to perform approximate calculation of the definite integral of the third probability expression, which simplifies the calculation process and improves the efficiency of constructing the map accuracy loss function with the variable being the grid size.

[0101] Further, in one embodiment, determining the value of the grid size based on the total map accuracy loss function includes:

[0102] The grid size is taken as a variable, the total map accuracy loss function is derived with respect to the grid size, and the derived total map accuracy loss function is set to zero, and the grid size is solved to obtain the value of the grid size.

[0103] For example, after constructing the map accuracy loss function with the variable being the grid size, the map accuracy total loss function with the variable being the grid size can be obtained, which corresponds to the following formula (9):

[0104]

[0105] Furthermore, the grid size is used as a variable to derive the total map accuracy loss function with respect to the grid size. At the same time, the derived total map accuracy loss function is set to the minimum value, that is, when it is set to 0, the map accuracy is the highest and the grid size is the optimal value. That is, the grid size is solved by the following formula (10):

[0106]

[0107] The expression for calculating the grid size corresponds to the following formula (11):

[0108]

[0109] The grid size value calculated according to the above expression is the optimal grid size.

[0110] In addition, since k>0, it can be deduced from the above formula that R<2σ x , further clarifying the value range of R.

[0111] In this embodiment, the target value of the total map accuracy loss is first set to zero; then the grid size is used as a variable to derive the total map accuracy loss function to obtain the value of the grid size so as to minimize the map accuracy loss.

[0112] In a second aspect, an embodiment of the present application also provides a grid size calculation device for a grid map.

[0113] In one embodiment, referring to Figure 3 , Figure 3This is a functional module diagram of an embodiment of a grid size calculation device for a grid map of the present application. Figure 3 As shown, the grid size calculation device 300 of the grid map includes:

[0114] A first acquisition module 310 is used to acquire a map accuracy loss function and a map information loss function whose pre-constructed variables are grid size;

[0115] A first determination module 320 is used to add the map accuracy loss function and the map information loss function to obtain a total map accuracy loss function;

[0116] The second determination module 330 is used to determine the value of the grid size based on the total map accuracy loss function.

[0117] Furthermore, in one embodiment, the grid size calculation device of the grid map further includes a construction module, and the construction module is specifically used to:

[0118] Taking the grid size as a variable, determine the first probability expression that the measured points obey the uniform distribution;

[0119] Calculate the distance error between the measured point and the point where the measured value corresponding to the measured point falls, and determine the Gaussian distribution probability density function corresponding to the distance error;

[0120] Integrate the Gaussian distribution probability density function to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid;

[0121] Based on the first probability expression and the second probability expression, determine a third probability expression where the measured value falls within the target grid;

[0122] Based on the third probability expression, a map accuracy loss function with the variable being the grid size is constructed.

[0123] Furthermore, in one embodiment, the building block is specifically used for:

[0124] Obtain the actual distance between the laser radar and the measured point, and the measured distance between the laser radar and the corresponding measurement value landing point of the measured point;

[0125] Based on the actual distance and the measured distance, a distance error between the measured point and a point where the measured value corresponding to the measured point falls is calculated.

[0126] Furthermore, in one embodiment, the building block is specifically used for:

[0127] Performing Taylor series expansion on the Gaussian distribution probability density function to obtain a Taylor series expanded probability density function;

[0128] The probability density function after Taylor series expansion is integrated to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid.

[0129] Furthermore, in one embodiment, the building block is specifically used for:

[0130] The Simpson formula is used to perform an approximate calculation of the definite integral of the third probability expression, and a map accuracy loss function with the grid size as a variable is constructed.

[0131] Further, in one embodiment, the second determining module is specifically configured to:

[0132] The grid size is taken as a variable, the total map accuracy loss function is derived with respect to the grid size, and the derived total map accuracy loss function is set to zero, and the grid size is solved to obtain the value of the grid size.

[0133] Among them, the functional implementation of each module in the above-mentioned grid size calculation device for grid map corresponds to each step in the above-mentioned grid size calculation method embodiment for grid map, and its functions and implementation processes are no longer repeated here one by one.

[0134] In a third aspect, an embodiment of the present application provides a device for calculating the grid size of a grid map. The device for calculating the grid size of a grid map may be a personal computer (PC), a laptop computer, a server, or other device having a data processing function.

[0135] Reference Figure 4 , Figure 4 The hardware structure diagram of the grid size calculation device of the grid map involved in the embodiment of the present application is shown in FIG. In the embodiment of the present application, the grid size calculation device of the grid map may include a processor, a memory, a communication interface and a communication bus.

[0136] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0137] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, which are used to interconnect the devices inside the grid size calculation device of the grid map, and an interface used to interconnect the grid size calculation device of the grid map with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber optic interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0138] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0139] The processor may be a general-purpose processor, and the general-purpose processor may call the grid size calculation program of the grid map stored in the memory, and execute the grid size calculation method of the grid map provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the grid size calculation program of the grid map is called may refer to the various embodiments of the grid size calculation method of the grid map of the present application, and will not be repeated here.

[0140] Those skilled in the art will understand that Figure 4 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0141] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0142] The computer-readable storage medium of the present application stores a grid size calculation program for a grid map, wherein when the grid size calculation program for a grid map is executed by a processor, the steps of the grid size calculation method for a grid map as described above are implemented.

[0143] Among them, the method implemented when the grid size calculation program of the grid map is executed can refer to the various embodiments of the grid size calculation method of the grid map of the present application, and will not be repeated here.

[0144] It should be noted that the serial numbers of the above-mentioned embodiments of the present application are only for description and do not represent the advantages or disadvantages of the embodiments.

[0145] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0146] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0147] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0148] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0149] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0150] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A method for calculating the grid size of a grid map, characterized in that: The grid size calculation method of the grid map includes: Get the pre-built map accuracy loss function and map information loss function whose variables are grid size; Adding the map accuracy loss function and the map information loss function to obtain a total map accuracy loss function; determining a grid size value based on the total map accuracy loss function; The variable is a process of constructing a map accuracy loss function of a grid size, including: Taking the grid size as a variable, determine the first probability expression that the measured points obey the uniform distribution; Calculate the distance error between the measured point and the point where the measured value corresponding to the measured point falls, and determine the Gaussian distribution probability density function corresponding to the distance error; Integrate the Gaussian distribution probability density function to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid; Based on the first probability expression and the second probability expression, determine a third probability expression where the measured value falls within the target grid; Based on the third probability expression, construct a map accuracy loss function whose variable is grid size; The step of calculating the distance error between the measured point and the point where the measured value corresponding to the measured point falls includes: Obtain the actual distance between the laser radar and the measured point, and the measured distance between the laser radar and the corresponding measurement value landing point of the measured point; Based on the actual distance and the measured distance, calculating the distance error between the measured point and the point where the measured value corresponding to the measured point falls; The step of integrating the Gaussian distribution probability density function to obtain a second probability expression that both the measured point and the measurement value corresponding to the measured point fall within the target grid includes: Performing Taylor series expansion on the Gaussian distribution probability density function to obtain a Taylor series expanded probability density function; Integrate the probability density function after Taylor series expansion to obtain a second probability expression that the measured point and the measured value corresponding to the measured point both fall within the target grid; Wherein, constructing a map accuracy loss function with a variable of grid size based on the third probability expression includes: Using Simpson's formula, approximate calculation of definite integral is performed on the third probability expression, and a map accuracy loss function with the variable being the grid size is constructed; Among them, the pre-constructed variable is the map information loss function of the grid size corresponding to the following formula (1): （1）; Among them, k is the weight coefficient, and k>0, which can be flexibly set according to actual needs; R is the grid size.

2. The method for calculating the grid size of a grid map according to claim 1, wherein: The determining of the value of the grid size based on the total map accuracy loss function includes: The grid size is taken as a variable, the total map accuracy loss function is derived with respect to the grid size, and the derived total map accuracy loss function is set to zero, and the grid size is solved to obtain the value of the grid size.

3. A grid size calculation device for a grid map, characterized in that: The grid size calculation device of the grid map comprises: A first acquisition module is used to acquire a map accuracy loss function and a map information loss function whose pre-constructed variables are grid size; A first determination module, configured to add the map accuracy loss function and the map information loss function to obtain a total map accuracy loss function; A second determination module is used to determine the value of the grid size based on the total map accuracy loss function; The grid size calculation device of the grid map further includes a construction module, and the construction module is specifically used for: Taking the grid size as a variable, determine the first probability expression that the measured points obey the uniform distribution; Calculate the distance error between the measured point and the point where the measured value corresponding to the measured point falls, and determine the Gaussian distribution probability density function corresponding to the distance error; Integrate the Gaussian distribution probability density function to obtain a second probability expression that the measured point and the measurement value corresponding to the measured point both fall within the target grid; Based on the first probability expression and the second probability expression, determine a third probability expression where the measured value falls within the target grid; Based on the third probability expression, construct a map accuracy loss function whose variable is grid size; The building blocks are specifically used for: Obtain the actual distance between the laser radar and the measured point, and the measured distance between the laser radar and the corresponding measurement value landing point of the measured point; Based on the actual distance and the measured distance, calculating the distance error between the measured point and the point where the measured value corresponding to the measured point falls; The building blocks are specifically used for: Performing Taylor series expansion on the Gaussian distribution probability density function to obtain a Taylor series expanded probability density function; Integrate the probability density function after Taylor series expansion to obtain a second probability expression that the measured point and the measured value corresponding to the measured point both fall within the target grid; The building blocks are specifically used for: Using Simpson's formula, approximate calculation of definite integral is performed on the third probability expression, and a map accuracy loss function with the variable being the grid size is constructed; The building blocks are also used to: According to the following formula (1), a map information loss function with the variable as the grid size is constructed: （1）; Among them, k is the weight coefficient, and k>0, which can be flexibly set according to actual needs; R is the grid size.

4. A grid size calculation device for a grid map, characterized in that: The grid size calculation device for the grid map includes a processor, a memory, and a grid size calculation program for the grid map stored in the memory and executable by the processor, wherein when the grid size calculation program for the grid map is executed by the processor, the steps of the grid size calculation method for the grid map as described in any one of claims 1 to 2 are implemented.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a grid size calculation program for a grid map, wherein when the grid size calculation program for a grid map is executed by a processor, the steps of the grid size calculation method for a grid map according to any one of claims 1 to 2 are implemented.

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