CN114485560B - Road gradient rapid detection method for off-highway sightseeing vehicle - Google Patents
Road gradient rapid detection method for off-highway sightseeing vehicle Download PDFInfo
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Abstract
A road gradient rapid detection method for an off-highway sightseeing vehicle belongs to the technical field of special equipment inspection and detection. According to the invention, the ground image of the sightseeing vehicle driving route is photographed by utilizing the unmanned aerial vehicle oblique photographing technology, the ground image is subjected to three-dimensional modeling, coordinates are extracted on the three-dimensional model to generate the sightseeing vehicle gradient-slope length curve, and the gradient of the sightseeing vehicle driving route can be rapidly detected through the gradient-slope length curve optimization treatment.
Description
Technical Field
The invention belongs to the technical field of special equipment inspection and detection, and particularly relates to a road gradient rapid detection method for an off-highway sightseeing vehicle.
Background
The sightseeing vehicle for off-highway is an open vehicle running in factories, tourist attractions and amusement parks, the condition of the running road and the surrounding running environment are far lower than those of national standard roads, and certain risks of collision, sliding, overturning and the like exist when the vehicle runs on a road with a large gradient. In order to ensure the operation safety of sightseeing vehicles and reduce the accident rate, the slope of the running route of sightseeing vehicles is regulated by TSG N0001-2017, which is issued by the national quality supervision and inspection and quarantine administration in 2017, namely, the technical supervision regulations of safety technology of motor vehicles in the field (factories): the maximum driving gradient of the sightseeing train is not more than 10 percent (except for short slopes with the gradient length less than 20 m), and the maximum driving gradient of the sightseeing train is not more than 4 percent (except for short slopes with the gradient length less than 20 m). According to the requirements, the gradient of the driving route of the sightseeing vehicle needs to be measured no matter when the sightseeing vehicle route is developed and planned in a scenic spot or when the sightseeing vehicle route is checked by a special equipment checking mechanism. Accurate measurement of the route gradient can be achieved by using the total station, but the effective distance of single measurement is generally about 20m, the monitoring point positions need to be frequently replaced for long-distance routes to be measured section by section, the task amount is large, and the input cost is high. For the GPS base station measurement method, the base station needs to be frequently built, and the problem of high input cost is also caused. In order to ensure the running safety of the sightseeing vehicle and meet the exemption requirement of ' short slope with the slope length smaller than 20m ' except ' the ' short slope ', the relation between ' local detail slope ' and ' whole average slope ' is processed during slope measurement, and the measurement cost of the traditional method is further increased.
Disclosure of Invention
The invention mainly solves the technical problems in the prior art and provides a road gradient rapid detection method for an off-highway sightseeing vehicle.
The technical problems of the invention are mainly solved by the following technical proposal: a road gradient rapid detection method of a sightseeing vehicle for off-highway comprises the following steps:
step 1, adopting an oblique photography technology based on an unmanned plane platform to shoot a ground image of a sightseeing vehicle route;
step 2, carrying out three-dimensional modeling on the photographed sightseeing bus route ground image;
step 3, extracting a three-dimensional space coordinate set of the ground points along the advancing direction of the sightseeing bus route on the three-dimensional model;
step 4, calculating the distance and the elevation difference of two adjacent points according to the three-dimensional space coordinate data, and generating a sightseeing vehicle gradient-gradient length curve;
step 5, carrying out interpolation fitting on the slope-slope length curve, expanding the number of ground points and increasing the slope detail data;
step 6, smoothing the interpolation fitting curve;
and 7, carrying out moving average on the interpolation fitting curve after smooth filtering, and confirming the condition of meeting the road gradient of the sightseeing bus and the standard.
Preferably, the step 1 specifically includes the following steps:
step S1, planning a flight route of the unmanned aerial vehicle according to the environment information along the sightseeing bus route;
step S2, setting flight parameters and photographing parameters of the unmanned aerial vehicle, so that the unmanned aerial vehicle performs oblique photographing;
and S3, setting necessary ground control points and standard size models.
Preferably, the step 2 specifically includes the following steps:
step S1, carrying out aerial triangulation calculation on a shot ground image, wherein the manufactured marker or a natural marker with obvious characteristics can be used as a control point;
and S2, reconstructing a three-dimensional model of the ground image after aerial triangulation calculation.
Preferably, in step 5, the interpolation fit uses a "piecewise cubic Hermite interpolation" method.
Preferably, in step 6, the interpolated fitted curve is filtered using a sliding median.
Preferably, the step 7 specifically includes the following steps:
step S1, marking that the gradient threshold Grad is exceeded on the smooth filtered gradient-length curve th Road segment S of (a);
step S2, confirming the gradient of the road section S; if the gradient Grad of the road section S is smaller than the gradient threshold Grad th If the road section S meets the requirement, the detection is finished, otherwise, the measurement is continued to exceed the gradient threshold Grad th Is a slope length;
step S3, calculating a sliding average value GradM of the gradient Grad on the smooth filtered slope-slope length curve according to the following formula, wherein the sliding window width W corresponds to the slope length threshold L th Drawing an Ln-GradM curve;
step S4, if the sliding average values GradM are smaller than the gradient threshold value Grad th The sightseeing bus route meets the requirement; if on a slopeLength L m The running average GradM at which exceeds the gradient threshold Grad th Then the road segment L is represented m -L th ，L m ]The gradient of the position may exceed the requirement, and the step S5 is entered;
and S5, performing on-site confirmation on the road section with the exceeding standard route.
Preferably, in step S2, the method for confirming the gradient of the road section S is as follows: selecting P with relatively far distance from each other on three-dimensional model diagram of road section S 1 、P 2 、P 3 Three points, P 1 Elevation h of (2) 1 And P 2 Elevation h of (2) 2 Equal, P 1 And P 2 The distance between them is L 12 ，P 3 Is lower than P 1 And P 2 ，P 3 The elevation of (2) is h 3 ，P 1 、P 2 And P 3 The area of the enclosed triangle is S 123 Calculating to obtain P 3 To P 1 And P 2 Vertical distance of connection l=2s 123 /L 12 The gradient Grad of the road section S is:
preferably, in step S2, the road section S is slope checked by a total station or a slope meter.
The invention has the beneficial effects that: according to the invention, the ground image of the sightseeing vehicle driving route is photographed by utilizing the unmanned aerial vehicle oblique photographing technology, the ground image is subjected to three-dimensional modeling, coordinates are extracted on the three-dimensional model to generate the sightseeing vehicle gradient-slope length curve, and the gradient of the sightseeing vehicle driving route can be rapidly detected through the gradient-slope length curve optimization treatment.
Drawings
FIG. 1 is a three-dimensional model of a ramp of the present invention;
FIG. 2 is a plot of sample points of a three-dimensional model of a ramp in accordance with the present invention;
FIG. 3 is a graph of grade versus grade length for the present invention;
FIG. 4 is a graph of an interpolation fit of the present invention;
FIG. 5 is a graph of a sliding median filtered interpolation fit of the present invention;
FIG. 6 is a plot of the invention for selecting measurements at the upper portion of a three-dimensional model uphill;
FIG. 7 is a graph of the slope of the moving average process of the present invention;
FIG. 8 is a partial three-dimensional measurement of the present invention on a three-dimensional model.
Detailed Description
The technical scheme of the invention is further specifically described below through examples and with reference to the accompanying drawings.
A road gradient rapid detection method of a sightseeing vehicle for off-highway comprises the following steps:
step 1, adopting an oblique photography technology based on an unmanned plane platform to shoot a ground image of a sightseeing vehicle route;
step 2, carrying out three-dimensional modeling on the photographed sightseeing bus route ground image;
step 3, extracting a three-dimensional space coordinate set of the ground points along the advancing direction of the sightseeing bus route on the three-dimensional model;
step 4, calculating the distance and the elevation difference of two adjacent points according to the three-dimensional space coordinate data, and generating a sightseeing vehicle gradient-gradient length curve;
step 5, carrying out interpolation fitting on the slope-slope length curve, expanding the number of ground points and increasing the slope detail data; preferably, the interpolation fitting adopts a 'piecewise three-time Hermite interpolation' method;
step 6, carrying out smooth filtering on the interpolation fitting curve to reduce gradient abnormality caused by unevenness of a local micro-area of a sightseeing bus route and shaking of an unmanned aerial vehicle; preferably, the interpolation fitting curve adopts sliding median filtering;
step 7, the gradient threshold value is Grad th Slope exceeds Grad th The road section slope length threshold value of (2) is L th And carrying out moving average on the interpolation fitting curve after smooth filtering, and confirming the condition of meeting the road gradient of the sightseeing vehicle and the standard.
Wherein, step 1 specifically includes the following steps:
step S1, planning a flight route of the unmanned aerial vehicle according to the environment information along the sightseeing bus route;
step S2, setting flight parameters and photographing parameters of the unmanned aerial vehicle, so that the unmanned aerial vehicle performs oblique photographing;
and S3, setting necessary ground control points and standard size models.
Wherein, step 2 specifically includes the following steps:
step S1, carrying out aerial triangulation calculation on a shot ground image, wherein the manufactured marker or a natural marker with obvious characteristics can be used as a control point;
and S2, reconstructing a three-dimensional model of the ground image after aerial triangulation calculation.
Wherein, the step 7 specifically comprises the following steps:
step S1, marking that the gradient threshold Grad is exceeded on the smooth filtered gradient-length curve th Road segment S of (a);
step S2, confirming the gradient of the road section S; if the gradient Grad of the road section S is smaller than the gradient threshold Grad th If the road section S meets the requirement, the detection is finished, otherwise, the measurement is continued to exceed the gradient threshold Grad th Is a slope length;
step S3, calculating a sliding average value GradM of the gradient Grad on the smooth filtered slope-slope length curve according to the following formula, wherein the sliding window width W corresponds to the slope length threshold L th Drawing an Ln-GradM curve;
step S4, if the sliding average values GradM are smaller than the gradient threshold value Grad th The sightseeing bus route meets the requirement; if at the slope length L m The running average GradM at which exceeds the gradient threshold Grad th Then the road segment L is represented m -L th ，L m ]The gradient of the position may exceed the requirement, and the step S5 is entered;
and S5, performing on-site confirmation on the road section with the exceeding standard route.
In step S2, the road section S may perform slope confirmation through instruments such as a total station and a gradiometer, or may perform slope confirmation by the following method: the road section S gradient confirmation method comprises the following steps: selecting P with relatively far distance from each other on three-dimensional model diagram of road section S 1 、P 2 、P 3 Three points, P 1 Elevation h of (2) 1 And P 2 Elevation h of (2) 2 Equal, P 1 And P 2 The distance between them is L 12 ，P 3 Is lower than P 1 And P 2 ，P 3 The elevation of (2) is h 3 ，P 1 、P 2 And P 3 The area of the enclosed triangle is S 123 Calculating to obtain P 3 To P 1 And P 2 Vertical distance of connection l=2s 123 /L 12 The gradient Grad of the road section S is:
in step S5, the route section exceeding the standard may be measured and confirmed by a total station, or may be measured and confirmed by the gradient confirmation method in step S2.
Examples: the test site is selected as a sightseeing vehicle examination base, the ramp consists of an ascending section, a horizontal section and a descending section, and the nominal gradient is 10%. The intelligent robot is subjected to oblique photography by adopting a large-scale eidolon Phantom 4RTK unmanned aerial vehicle, the flight height of the unmanned aerial vehicle is set to 25 meters, the average resolution of images to the ground is 6.70mm/pixel, the inclination angle of a lens is 45 degrees, the flight speed is 5m/s, and the transverse overlapping is 80%. The unmanned aerial vehicle performs shooting tasks according to preset parameters, 216 images are obtained in total, and 196 images which can be used for three-dimensional reconstruction through aerial triangulation calculation are obtained. Table 1 shows the position uncertainty statistics of the aerial triangulation photographs, with average position uncertainties in both X, Y and Z directions not exceeding 3mm.
TABLE 1
| Position uncertainty statistics | X direction (m) | Y direction (m) | Z direction (m) |
| Minimum value | 0.00041 | 0.00048 | 0.00051 |
| Average value of | 0.00282 | 0.00215 | 0.00204 |
| Maximum value | 0.07953 | 0.07055 | 0.07103 |
The three-dimensional model of the ramp is shown in fig. 1, three-dimensional coordinates of physical points on the ramp are extracted along a path shown by an arrow in the figure, and a space graph is drawn as shown in fig. 2.
On the sightseeing vehicle driving path, two adjacent physical points P m 、P n Is (x) m ，y m ，z m ) And (x) n ，y n ，z n ) The elevation difference is equal to the difference Z of the Z-direction coordinates n -z m With a path length approximately equal to the spatial distance L of two points mn Path P m P n The gradient of the segment is (z n -z m )/L mn According toThe gradient of the path between each two adjacent points is next obtained as shown in fig. 3.
In calculating the travel path of the sightseeing vehicle, the gradient data points shown in fig. 3 are processed by adopting the combination of interpolation fitting and sliding median filtering:
(1) Expanding the number of gradient calculation points on a path by adopting a 'segmentation three-time Hermite interpolation' method, wherein the interpolation interval in the gradient length direction is 0.1m, and a curve after interpolation fitting is shown in fig. 4;
(2) The slope curve is subjected to smooth filtering by adopting a sliding median filtering method, the width of a sliding window is 7 (equivalent to 0.7m slope length), and the slope abnormality caused by the unevenness of a micro-area of a sightseeing bus route and the shaking of an unmanned aerial vehicle is reduced, as shown in fig. 5.
The gradient of the 6m-12m slope section is secondarily confirmed, and three measurement methods are adopted for comparison:
(1) Oblique photography three-dimensional model measurement: as shown in FIG. 6, P is selected at the upper part of the ramp on the three-dimensional model 1 、P 2 Two points, P 1 And P 2 The distance between them is L 12 ，P 1 Elevation h of (2) 1 And P 2 Elevation h of (2) 2 Equal, difference in elevation H 12 =0, then select P at the bottom of the ramp 3 One point, P 1 、P 2 And P 3 The area of the enclosed triangle is S 123 Thereby obtaining P 3 To P 1 And P 2 The vertical distance LH of the connecting line and the gradient i;
(2) Single point measurement of gradiometer: selecting three A, B, C points on a section of artificial ramp, and measuring the gradient of the three A, B, C points by using a gradiometer;
(3) Total station continuous measurement: four points A, B, C, D are selected on a section of artificial ramp, and the gradient of A-B, A-C, A-D is directly measured by using a total station without moving an instrument.
Table 2 is the comparison of total powerstation, gradiometer and unmanned aerial vehicle oblique photography measurement result, can see from it that unmanned aerial vehicle oblique photography measurement result is relatively close with total powerstation measurement result, and the error of gradiometer measurement is great, and the data is comparatively dispersed.
TABLE 2
| Measurement method | Measurement value 1 | Measurement value 2 | Measurement value 3 | Average value of | Standard value |
| Total station | 9.48％ | 9.36％ | 9.40％ | 9.41％ | 0.04％ |
| Slope meter | 9.98％ | 11.58％ | 10.50％ | 10.69％ | 0.67％ |
| Unmanned aerial vehicle oblique photography | 9.19％ | 9.33％ | 9.13％ | 9.22％％ | 0.08％ |
In fig. 5, the travel route of the sightseeing vehicle is about 35 m, the longest slope length of the overstock slope is assumed to be 2m (20 m in practice), the slope curve is subjected to the moving average treatment, the moving average window width is 20 (2 m/0.1 m), the slope length corresponding to 2m is obtained, and as a result, as shown in fig. 7, the slope sections with the slope lengths exceeding 2m and the slope exceeding 10% are found at the two positions of 8m-10m and 28m-30 m.
The first out-of-limit slope segment in fig. 7 was confirmed, and local three-dimensional measurements were made on a three-dimensional model of an 8m-10m slope segment, as shown in fig. 8, with a slope of 10.42% being obtained.
In summary, the invention uses unmanned plane oblique photography technique to shoot the ground image of the sightseeing vehicle driving route, and carries out three-dimensional modeling on the ground image, extracts coordinates on the three-dimensional model to generate the sightseeing vehicle gradient-slope length curve, and can rapidly detect the sightseeing vehicle driving route gradient through optimizing the gradient-slope length curve.
Finally, it should be noted that the above embodiments are merely representative examples of the present invention. Obviously, the invention is not limited to the above-described embodiments, but many variations are possible. Any simple modification, equivalent variation and modification of the above embodiments according to the technical substance of the present invention should be considered to be within the scope of the present invention.
Claims (5)
1. The method for rapidly detecting the road gradient of the off-highway sightseeing vehicle is characterized by comprising the following steps of:
step 1, adopting an oblique photography technology based on an unmanned plane platform to shoot a ground image of a sightseeing vehicle route;
step 2, carrying out three-dimensional modeling on the photographed sightseeing bus route ground image;
step 3, extracting a three-dimensional space coordinate set of the ground points along the advancing direction of the sightseeing bus route on the three-dimensional model;
step 4, calculating the distance and the elevation difference of two adjacent points according to the three-dimensional space coordinate data, and generating a sightseeing vehicle gradient-gradient length curve;
step 5, carrying out interpolation fitting on the slope-slope length curve, expanding the number of ground points and increasing the slope detail data;
step 6, smoothing the interpolation fitting curve;
step 7, carrying out moving average on the interpolation fitting curve after smooth filtering, and confirming the condition of meeting the road gradient of the sightseeing vehicle and the standard;
the step 7 specifically comprises the following steps:
step S1, marking that the gradient threshold Grad is exceeded on the smooth filtered gradient-length curve th Road segment S of (a);
step S2, confirming the gradient of the road section S; if the gradient Grad of the road section S is smaller than the gradient threshold Grad th If the road section S meets the requirement, the detection is finished, otherwise, the measurement is continued to exceed the gradient threshold Grad th Is a slope length;
step S3, calculating a sliding average value GradM of the gradient Grad on the smooth filtered slope-slope length curve according to the following formula, wherein the sliding window width W corresponds to the slope length threshold L th Drawing an Ln-GradM curve;
；
step S4, if the sliding average values GradM are smaller than the gradient threshold value Grad th The sightseeing bus route meets the requirement; if at the slope length L m The running average GradM at which exceeds the gradient threshold Grad th Then the road segment L is represented m -L th ，L m ]The gradient of the position may exceed the requirement, and the step S5 is entered;
s5, performing on-site confirmation on the road section with the exceeding standard route;
in step S2 of the specific step of step 7, the method for confirming the slope of the road section S is as follows: selecting P with relatively far distance from each other on three-dimensional model diagram of road section S 1 、P 2 、P 3 Three points, P 1 Elevation h of (2) 1 And P 2 Elevation h of (2) 2 Equal, P 1 And P 2 The distance between them is L 12 ，P 3 Is lower than P 1 And P 2 ，P 3 The elevation of (2) is h 3 ，P 1 、P 2 And P 3 The area of the enclosed triangle is S 123 Calculating to obtain P 3 To P 1 And P 2 Vertical distance of connection l=2s 123 /L 12 The gradient Grad of the road section S is:
。
2. the method for rapidly detecting the road gradient of the off-highway sightseeing vehicle according to claim 1, wherein the step 1 specifically comprises the following steps:
step S1, planning a flight route of the unmanned aerial vehicle according to the environment information along the sightseeing bus route;
step S2, setting flight parameters and photographing parameters of the unmanned aerial vehicle, so that the unmanned aerial vehicle performs oblique photographing;
and S3, setting necessary ground control points and standard size models.
3. The method for rapidly detecting the road gradient of the off-highway sightseeing vehicle according to claim 1, wherein the step 2 specifically comprises the following steps:
step S1, carrying out aerial triangulation calculation on a shot ground image, wherein the manufactured marker or a natural marker with obvious characteristics can be used as a control point;
and S2, reconstructing a three-dimensional model of the ground image after aerial triangulation calculation.
4. The method for rapidly detecting the road gradient of the off-highway sightseeing vehicle according to claim 1, wherein in the step 5, the interpolation fitting adopts a "piecewise three-time Hermite interpolation" method.
5. The method for rapid detection of road grade for off-highway sightseeing vehicles according to claim 1, wherein in step 6, the interpolated fitted curve is filtered by sliding median.
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