A method for filling internal holes of a road surface elevation map in order based on bilateral filtering

Abstract

The application discloses a kind of inside cavity sequence methods of road surface elevation map based on bilateral filtering, focus on improving the efficiency of existing elevation map interpolation method low problem, can quickly, accurately complete map inside cavity repair and fill service.This application constructs the invalid grid neighborhood information quality function for formulating bilateral filtering filling sequence by combining the inside space point sparsity of neighborhood effective grid and elevation variance;Adopt positive gradient projection method to optimize the parameters of information quality function;Then according to the macroscopic cavity filling path, an adaptive repair window is made;Using bilateral filter with high real-time to fill the cavity in sequence.Provides a new way for road surface elevation map inside cavity filling.

Description

Technical Field

[0001] This invention relates to a method for sequentially filling voids inside a road surface elevation map, and more particularly to a method for sequentially filling voids inside a road surface elevation map based on bilateral filtering, belonging to the field of 2.5D road surface reconstruction. Background Technology

[0002] In recent years, the boom in autonomous vehicles has triggered a series of technological innovations. Acquiring geographic road data is a crucial area in the field of autonomous driving. Currently, to reduce costs and data storage scale, cameras are typically used as information acquisition sensors, and 2.5D elevation grid maps are used to store road surface information in front of the vehicle. However, cameras suffer from low resolution at long distances and poor image quality in low-light conditions. Furthermore, different types of cameras have their own problems; for example, the disparity map generated by feature matching in binocular cameras contains white holes, and the scattering of outdoor light can lead to missing depth information acquired by RGB-D cameras. All of these problems ultimately result in holes within the elevation map, affecting the overall map quality. However, traditional elevation map interpolation methods are inefficient and cannot meet the needs of daily vehicle operation. Therefore, a fast and accurate method for repairing holes within elevation grid maps is needed to improve map quality. Summary of the Invention

[0003] The technical problem to be solved by this invention is that existing technologies cannot quickly and accurately repair the voids inside elevation maps. Therefore, this invention proposes a method for sequentially filling voids inside road elevation maps based on bilateral filtering.

[0004] To solve the above-mentioned technical problems, the present invention adopts the following technical solution:

[0005] A method for sequentially filling voids inside a road surface elevation map based on bilateral filtering, the specific steps of which are as follows:

[0006] Step 1), combining the number of spatial points PN and the elevation variance σ 2 Construct the information quality function for a single effective raster;

[0007] Step 2) Construct the weight functions ω1 and ω2 of the information quality function, and combine the weight functions to construct the weight difference function D;

[0008] Step 3) Randomly generate multiple sets of D parameters that meet the conditions, select the set of parameters that is closest to the optimization objective as the initial parameters, and then use positive gradient projection to further optimize the set of parameters;

[0009] Step 4) Determine the effective neighborhood R of the invalid grid and construct its total information quality function based on the information quality function;

[0010] Step 5): Based on the macro repair path, an adaptive repair sliding window is created using reverse topology logic;

[0011] Step 6) Use a binocular camera to acquire road surface information, construct the corresponding 3D point cloud and map it onto the elevation grid map;

[0012] Step 7) Use a bilateral filter to preprocess the initial elevation map from step 6) to eliminate discrete holes;

[0013] Step 8): Convert the elevation map into a binary image based on whether it contains spatial points, extract the internal hole contours of the binary image, and record the positions of all contours.

[0014] Step 9) Calculate the total information quality (TIQ) of each invalid raster and sort the invalid rasters by priority according to the value;

[0015] Step 10), use a bilateral filter to sort the TIQ before sorting. Invalid rasters are filled sequentially;

[0016] Steps 11), 9), and 10) are repeated q times;

[0017] Step 12) Use a bilateral filter to fill the remaining invalid grid cells.

[0018] Preferably, in step 1), the information quality (IQ) of different effective grids is made to satisfy the following conditions:

[0019] 1-1) When the number of internal spatial points is on the high order of magnitude, that is, when the number of spatial points PN>k2, the information quality (IQ) difference between different effective grids is determined by their elevation variance.

[0020] 1-2) When the number of internal spatial points is in the low order of magnitude, i.e. k1>PN>0, the information quality (IQ) difference between different effective grids is determined by the number of spatial points.

[0021] 1-3) When the difference in elevation variance between different effective rasters is small, the information quality (IQ) of effective rasters with a high number of internal spatial points should be greater than that of effective rasters with a low number of internal spatial points.

[0022] More preferably, in step 1), k1 takes the value 8 and k2 takes the value 30.

[0023] Preferably, the information quality function in step 1) is:

[0024] IQ=ω1-ω2Tσ 2 (1)

[0025] In equation (1), ω1(lgPN) and ω2(lgPN) are weight functions with respect to lgPN, and T is the amplification factor.

[0026] Preferably, in step 2), Tσ is first... 2 The weights are kept constant at 1, and the weight functions ω1, ω2, and the weight difference function D are constructed as functions of lgPN, as shown below:

[0027] ω1(lgPN)=a1 lg(PN)+b1 (2)

[0028] D=ω1(lgPN)-ω2(lgPN) (3)

[0029]

[0030] In equation (4), x is used to replace lgPN, D max It is the maximum value of the weight difference function D, and a1, a2, b1, b2, c2, and e2 are the coefficients of the equation.

[0031] Preferably, in step 3), the optimization object of the positive gradient projection is the weight difference function D, the optimization objective is to maximize (D(k2)-D(k1)), and the boundary conditions must ensure the continuity and smoothness of the curve. The constraints include:

[0032] 3-1) The weight difference function D curve is monotonically increasing on [0, k2), so the parameters a1 and a2 are required to be greater than 0. In order to avoid the curve changing too fast, a1 is not greater than 5.

[0033] 3-2) It is generally believed that the information quality provided by 8 effective neighboring rasters is greater than that of a single effective raster;

[0034] 3-3) When n is on a low order of magnitude, the information quality provided by a raster with n spatial points is lower than the sum of the information quality of n single spatial point rasteres. Therefore, the constraint is described by the following formula:

[0035]

[0036] In equation (5), n max It is the maximum number of spatial points that a single grid can contain. Based on the elevation map resolution, it is set to 1000. The formula (n*IQ(1,0)-IQ(n,0)) can take the minimum value when n=2.

[0037] Preferably, in step 4), the total information quality function is described in the following form:

[0038]

[0039] In equation (6), TIQ(u,v) represents the total quality of information in the region corresponding to the invalid raster (u,v), and IQ ij The effective neighborhood R represents the information quality of the effective grid (i,j);

[0040] R={(i,j)|u-1≤i≤u+1,v-1≤j≤v+1,E(i,j)≠Nan,(i,j)≠(u,v)} (7)

[0041] In equation (7), E(i,j) is the elevation value of the effective raster (i,j), and Nan represents a null value.

[0042] Preferably, the topological grid coordinates determined by the reverse topology logic in step 5) are determined by the following formula:

[0043]

[0044] Preferably, in step 6), the binocular camera model is ZED2, the elevation map length is limited to 4m, and the resolution is 0.02m.

[0045] Preferably, in steps 7) and 12), the repair order of the bilateral filter is from top to bottom and from left to right.

[0046] Preferably, the algorithm used for contour extraction of the binarized image in step 8) is the Canny algorithm.

[0047] Preferably, in step 11), the sequential filling round q is 10.

[0048] Compared with the prior art, the present invention has the following obvious outstanding technical effects and significant advantages:

[0049] 1) This invention uses a bilateral filter as a hole repair tool, which has higher real-time performance compared with traditional elevation map hole interpolation methods such as Kriging interpolation and natural neighborhood interpolation. Furthermore, this method considers the influence of the pixel domain and the spatial domain on the interpolation position, and can describe the relationship between invalid grids and neighboring elevations, thus ensuring the accuracy of hole repair.

[0050] 2) This invention combines the number of spatial points contained in the effective grid of the elevation map with its elevation variance to determine the filling order of the invalid grid. The quality of the effective reference data used in each round of repair is higher, which significantly improves the overall quality of the final repair result. It can also undertake the task of repairing holes in more scenarios (such as ramps), which enhances the robustness and adaptability of the method.

[0051] 3) This invention uses positive gradient projection to optimize the parameters of the total information quality function of invalid grids, so that the trend of the function curve can better meet expectations, and ultimately make the filling order determined by the function more reasonable, thereby improving the accuracy of hole repair.

[0052] 4) The filling sequence of the present invention is macroscopically from the hole edge to the hole center. Based on this characteristic, an adaptive repair sliding window was made, which effectively increases the reference data that the bilateral filter can use and further improves the accuracy of hole repair.

[0053] 5) The present invention uses the Canny algorithm for contour extraction. The Canny algorithm is a mature contour extraction algorithm with the advantages of high speed and high accuracy. Furthermore, this method can determine the position of the hole contour, which can effectively reduce the sliding distance of the repair window, thereby improving the real-time performance of the present invention. Attached Figure Description

[0054] Figure 1 This is a schematic diagram of a method for sequentially filling voids inside a road surface elevation map based on bilateral filtering, according to the present invention.

[0055] Figure 2 This is the adaptive repair sliding window reverse topology logic diagram of the method of the present invention. Detailed Implementation

[0056] To make the content of this invention easier to understand, the invention will be further described in detail below with reference to specific examples and accompanying drawings.

[0057] Example 1

[0058] In this embodiment, a method for sequentially filling holes inside a road surface elevation map based on bilateral filtering includes the following steps:

[0059] Step 1), combining the number of spatial points PN and the elevation variance σ 2 Construct the information quality function for a single effective raster;

[0060] Step 2) Construct the weight functions ω1 and ω2 of the information quality function, and combine the weight functions to construct the weight difference function D;

[0061] Step 3) Randomly generate multiple sets of D parameters that meet the conditions, select the set of parameters that is closest to the optimization objective as the initial parameters, and then use positive gradient projection to further optimize the set of parameters;

[0062] Step 4) Determine the effective neighborhood R of the invalid grid and construct its total information quality function based on the information quality function;

[0063] Step 5): Based on the macro repair path, an adaptive repair sliding window is created using reverse topology logic;

[0064] Step 6) Use a binocular camera to acquire road surface information, construct the corresponding 3D point cloud and map it onto the elevation grid map;

[0065] Step 7) Use a bilateral filter to preprocess the initial elevation map from step 6) to eliminate discrete holes;

[0066] Step 8): Convert the elevation map into a binary image based on whether it contains spatial points, extract the internal hole contours of the binary image, and record the positions of all contours.

[0067] Step 9) Calculate the total information quality (TIQ) of each invalid raster and sort the invalid rasters by priority according to the value;

[0068] Step 10), use a bilateral filter to sort the TIQ before sorting. Invalid rasters are filled sequentially;

[0069] Steps 11), 9), and 10) are repeated q times;

[0070] Step 12) Use a bilateral filter to fill the remaining invalid grid cells.

[0071] This embodiment uses a bilateral filtering-based method for determining the order of holes within road surface elevation maps. It aims to improve the efficiency of existing elevation map interpolation methods, enabling rapid and accurate hole filling. This method constructs a neighborhood information quality function for invalid raster areas, combining the sparsity of spatial points within effective neighboring raster cells with the elevation variance, to determine the bilateral filtering filling order. An adaptive repair window is created based on the macroscopic hole filling path, providing a novel approach to filling holes within road surface elevation maps.

[0072] Example 2

[0073] This embodiment is basically the same as Embodiment 1, except that:

[0074] In this embodiment, a method for sequentially filling holes inside a road surface elevation map based on bilateral filtering includes the following steps:

[0075] Step 1), combining the number of spatial points PN and the elevation variance σ 2 Construct the information quality function for a single effective raster; specifically:

[0076] The information quality (IQ) of different effective graticules must satisfy the following conditions:

[0077] 1-1) When the number of internal spatial points is on the high order of magnitude, that is, when the number of spatial points PN>k2, the information quality (IQ) difference between different effective grids is determined by their elevation variance; where k2 is 30.

[0078] 1-2) When the number of internal spatial points is in the low order of magnitude, i.e., k1>PN>0, the information quality (IQ) difference between different effective grids is determined by the number of spatial points; where k1 takes the value of 8.

[0079] 1-3) When the difference in elevation variance between different effective rasters is small, the information quality (IQ) of effective rasters with a high number of internal spatial points should be greater than that of effective rasters with a low number of internal spatial points.

[0080] Step 2) Construct the weight functions ω1 and ω2 of the information quality function, and combine the weight functions to construct the weight difference function D;

[0081] Step 3) Randomly generate multiple sets of D parameters that meet the conditions, select the set of parameters that is closest to the optimization objective as the initial parameters, and then use positive gradient projection to further optimize the set of parameters;

[0082] Step 4) Determine the effective neighborhood R of the invalid grid and construct its total information quality function based on the information quality function;

[0083] Step 5): Based on the macro repair path, an adaptive repair sliding window is created using reverse topology logic;

[0084] Step 6) Use a binocular camera to acquire road surface information, construct the corresponding 3D point cloud and map it onto the elevation grid map;

[0085] Step 7) Use a bilateral filter to preprocess the initial elevation map from step 6) to eliminate discrete holes;

[0086] Step 8): Convert the elevation map into a binary image based on whether it contains spatial points, extract the internal hole contours of the binary image, and record the positions of all contours.

[0087] Step 9) Calculate the total information quality (TIQ) of each invalid raster and sort the invalid rasters by priority according to the value;

[0088] Step 10), use a bilateral filter to sort the TIQ before sorting. Invalid rasters are filled sequentially;

[0089] Steps 11), 9), and 10) are repeated q times;

[0090] Step 12) Use a bilateral filter to fill the remaining invalid grid cells.

[0091] This embodiment of the road surface elevation map internal hole ordering method, based on bilateral filtering, constructs an invalid raster neighborhood information quality function to determine the bilateral filtering filling order by combining the sparsity of spatial points within the effective neighboring raster and the elevation variance. This embodiment aims to improve the low efficiency of existing elevation map interpolation methods, enabling rapid and accurate completion of map internal hole repair and filling tasks.

[0092] Example 3

[0093] This embodiment is basically the same as the above embodiments, except that:

[0094] In this embodiment, as Figure 1 As shown, a method for sequentially filling holes inside a road surface elevation map based on bilateral filtering includes the following steps:

[0095] Step S10: Combine the number of spatial points PN with the elevation variance σ 2 Construct the information quality function for a single effective raster; the information quality function can be constructed in the following form:

[0096] IQ=ω1-ω2Tσ 2 (1)

[0097] In equation (1), ω1(lgPN) and ω2(lgPN) are weight functions with respect to lgPN, and T is the amplification factor.

[0098] Step S20: Construct the weight functions ω1 and ω2 of the information quality function, and combine them to construct the weight difference function D. To facilitate discussion of the relationship between functions ω1 and ω2, first define Tσ... 2 The weights are constant at 1. The weight functions ω1, ω2, and the weight difference function D can be constructed as functions of lgPN, as shown below:

[0099] ω1(lgPN)=a1lg(PN)+b1 (2)

[0100] D=ω1(lgPN)-ω2(lgPN) (3)

[0101]

[0102] In equation (4), x is used to replace lgPN, D max It is the maximum value of the weight difference D. a1, a2, b1, b2, c2, and e2 are the coefficients of the equation.

[0103] Step S30: Randomly generate 10,000 sets of D parameters that meet the conditions, select the set of parameters that is closest to the optimization objective as the initial parameters, and then use positive gradient projection to further optimize the set of parameters.

[0104] The optimization object of the positive gradient projection is the weight difference function D, and the optimization objective is to maximize (D(k2)-D(k1)). The boundary conditions must ensure the continuity and smoothness of the curve, and the constraints include:

[0105] ① The curve D is monotonically increasing on [0, k2), so the parameters a1 and a2 are required to be greater than 0. In order to avoid the curve changing too fast, a1 is not greater than 5.

[0106] ②It is generally believed that the information quality provided by 8 effective neighboring grids is greater than that of a single effective grid;

[0107] ③ When n is on a low order of magnitude, the information quality provided by a raster with n spatial points is lower than the sum of the information quality of n single spatial point rasteres. In summary, the constraint can be described by the following formula:

[0108]

[0109] In equation (5), n max It is the maximum number of spatial points that a single grid can contain. Based on the elevation map resolution, it is set to 1000. The formula (n*IQ(1,0)-IQ(n,0)) can take the minimum value when n=2.

[0110] Step S40: Determine the effective neighborhood R of the invalid raster and construct its total information quality function based on the information quality function. The total information quality function can be described by the following formula:

[0111]

[0112] In equation (6), TIQ(u,v) represents the total quality of information in the region corresponding to the invalid raster (u,v), and IQ ij The effective neighborhood R represents the information quality of the effective grid (i,j).

[0113] R={(i,j)|u-1≤i≤u+1,v-1≤j≤v+1,E(i,j)≠Nan,(i,j)≠(u,v)} (7)

[0114] In equation (7), E(i,j) is the elevation value of the effective raster (i,j), and Nan represents a null value.

[0115] Step S50: Based on the macroscopic repair path, an adaptive repair sliding window is created using reverse topology logic. Figure 2 The process and effects were demonstrated.

[0116] The topological grid coordinates determined by the reverse topology logic are determined by the following formula:

[0117]

[0118] Step S60: Use a binocular camera to acquire road surface information, construct a corresponding 3D point cloud and map it onto an elevation grid map. In this embodiment, the binocular camera model is ZED2, the elevation map length is limited to 4m, and the resolution is 0.02m.

[0119] Step S70: Preprocess the initial elevation map from step S60 using a bilateral filter to eliminate discrete holes. During preprocessing and finalization, the bilateral filter repairs the map from top to bottom and from left to right.

[0120] Step S80: Convert the elevation map into a binary image based on whether it contains spatial points. Extract the internal hole contours of the binary image and record the positions of all contours. The algorithm used for contour extraction of the binary image is the Canny algorithm.

[0121] Step S90: Calculate the total information quality (TIQ) of each invalid raster and sort the invalid rasters by priority according to the value.

[0122] Step S100: Before sorting TIQ using a bilateral filter Invalid grid cells are filled sequentially.

[0123] Step S110: Steps S90 to S100 cycle q times. In this embodiment, the sequential filling cycle q is 10.

[0124] Step S120: Fill the remaining invalid grid cells using a bilateral filter. The bilateral filter repair order is from top to bottom and from left to right.

[0125] This embodiment employs a bilateral filter as the hole repair tool, offering higher real-time performance compared to traditional elevation map hole interpolation methods such as Kriging and natural neighborhood interpolation. Furthermore, this method considers both pixel and spatial domain influences on the interpolation position, describing the relationship between invalid rasters and their neighboring elevations, thus ensuring the accuracy of hole repair. Because this embodiment combines the number of spatial points contained in the effective rasters of the elevation map with their elevation variance to determine the filling order of invalid rasters, the quality of the effective reference data used in each round of repair is higher, significantly improving the overall quality of the final repair result. It can also handle hole repair tasks in more scenarios (such as ramps), enhancing the robustness and adaptability of the method. This embodiment uses an active gradient. The projection optimizes the parameters of the total information quality function of the invalid raster, making the curve trend of the function more consistent with expectations. This ultimately makes the filling order determined by the function more reasonable, thereby improving the accuracy of hole repair. In this embodiment, the filling order macroscopically points from the hole edge to the hole center. Based on this characteristic, an adaptive repair sliding window is created, effectively increasing the reference data available to the bilateral filter and further improving the accuracy of hole repair. This embodiment uses the Canny algorithm for contour extraction. The Canny algorithm is a mature contour extraction algorithm with advantages of high speed and high accuracy. Furthermore, this method can determine the hole contour position, effectively reducing the sliding distance of the repair window, thereby improving the real-time performance of the method.

[0126] In summary, this invention, based on a bilateral filtering method for sequential filling of holes within road surface elevation maps, aims to improve the efficiency of existing elevation map interpolation methods, enabling rapid and accurate hole filling tasks. This invention constructs an information quality function for the neighborhood of invalid raster areas, combining the sparsity of spatial points within effective neighboring raster cells with the elevation variance, to determine the bilateral filtering filling order. The parameters of the information quality function are optimized using an active gradient projection method. An adaptive repair window is then created based on the macroscopic hole filling path. Finally, a highly real-time bilateral filter is used to sequentially fill the holes. This provides a novel approach for filling holes within road surface elevation maps.

[0127] Obviously, the above embodiments are merely illustrative examples for clear explanation and are not intended to limit the implementation. Those skilled in the art will recognize that other variations or modifications can be made based on the above description. It is neither necessary nor possible to exhaustively list all possible implementations here. However, obvious variations or modifications derived therefrom are still within the scope of protection of this invention.

Claims

1. A method for sequentially filling voids inside a road surface elevation map based on bilateral filtering, characterized in that, The specific steps are as follows: Step 1), combining the number of spatial points PN With elevation variance Construct the information quality function for a single effective raster; Step 2), construct the weight function of the information quality function. , And construct a weight difference function by combining the weight function. D ; Step 3), randomly generate multiple sets of conditions. D The parameters are selected from the set of parameters that are closest to the optimization objective as the initial parameters, and then the positive gradient projection is used to further optimize the set of parameters. Step 4) Determine the valid neighborhood of the invalid grid. R And construct its total information quality function based on the information quality function; Step 5) Based on the macro repair path, an adaptive repair sliding window is created using reverse topology logic; Step 6) Use a binocular camera to acquire road surface information, construct the corresponding 3D point cloud and map it onto the elevation grid map; Step 7) Preprocess the initial elevation map from Step 6) using a bilateral filter to eliminate discrete holes; Step 8) Convert the elevation map into a binary image based on whether it contains spatial points, extract the internal hole contours of the binary image, and record the positions of all contours; Step 9), calculate the total information quality of each invalid raster. TIQ And sort the invalid rasters by priority according to the value; Step 10), using a bilateral filter pair TIQ Before sorting Invalid rasters are filled sequentially; Steps 11), 9), and 10 are repeated. q wheel; Step 12) Use a bilateral filter to fill the remaining invalid grid cells.

2. The method for sequentially filling internal voids in road surface elevation maps based on bilateral filtering according to claim 1, characterized in that, In step 1), the information quality of different effective gratings is... IQ The following conditions must be met: 1-1) The number of internal spatial points is on the high order of magnitude, that is, the number of spatial points is... PN > k At time 2, the information quality of different effective rasters IQ The difference is determined by its elevation variance; 1-2) The number of internal spatial points is on a low order of magnitude, that is... k 1> PN When >0, the information quality of different effective rasters IQ The difference is determined by the number of spatial points; 1-3) When the difference in elevation variance between different effective rasters is small, the information quality of effective rasters with a high number of internal spatial points is high. IQ The information quality should be greater than that of an effective raster with a low number of internal spatial points. IQ .

3. The method for sequentially filling internal voids in road surface elevation maps based on bilateral filtering according to claim 2, characterized in that, In step 1) k 1 takes the value 8. k 2 takes the value 30.

4. The method for sequentially filling internal voids in road surface elevation maps based on bilateral filtering according to claim 1, characterized in that, The information quality function in step 1) is: （1） In formula (1) and They are about The weight function, T It is the magnification factor.

5. The method for sequentially filling internal voids in road surface elevation maps based on bilateral filtering according to claim 2, characterized in that, In step 2), first... The weight is kept constant at 1, and a weight function is constructed. , and weighted difference function D For about The function, in its specific form, is shown below: （2） （3） （4） In equation (4), the following is used x Alternative , It is a weighted difference function D The maximum value, a 1 , a 2 , b 1 , b 2 , c 2 , d 2. e 2 These are the coefficients of the equation. T It is the magnification factor.

6. The method for sequentially filling internal voids in a road surface elevation map based on bilateral filtering according to claim 5, characterized in that, In step 3), the optimization object of the positive gradient projection is the weight difference function. D The optimization objective is ( The maximum value is taken, and the boundary conditions must ensure the continuity and smoothness of the curve. The constraints include: 3-1) Weighted Difference Function D The curve is The parameter is monotonically increasing, therefore the parameter is required. , All are greater than 0, and to avoid the curve changing too quickly, let No more than 5; 3-2) The information quality provided by 8 effective neighboring rasters is greater than that of a single effective raster; 3-3) When n When it belongs to a low order of magnitude, it has n The information quality provided by a single spatial point grid is lower than that of a single spatial point grid. n The sum of the information quality of each individual spatial point raster is thus the constraint condition described by the following equation: （5） In equation (5), This is the maximum number of spatial points that a single grid cell can contain, set to 1000 based on the elevation map resolution. exist n The minimum value can be obtained when the value is equal to 2.

7. The method for sequentially filling internal voids in road surface elevation maps based on bilateral filtering according to claim 1, characterized in that, In step 4), the total information quality function is described in the following form: （6） In equation (6), Invalid grid Overall quality of information in the corresponding region Represents a valid grid Information quality; the effective neighborhood R for: （7） In equation (7), It is an effective grid Elevation value, Nan Represents a null value.

8. The method for sequentially filling internal voids in a road surface elevation map based on bilateral filtering according to claim 7, characterized in that, The topological grid coordinates determined by the reverse topology logic in step 5) are determined by the following formula: （8）。 9. The method for sequentially filling internal voids in road surface elevation maps based on bilateral filtering according to claim 1, characterized in that, In step 6), the binocular camera model is ZED2, the elevation map length is limited to 4m, and the resolution is 0.02m.

10. The method for sequentially filling internal voids in a road surface elevation map based on bilateral filtering according to claim 1, characterized in that, In steps 7) and 12), the repair order of the bilateral filter is from top to bottom and from left to right.

Images