CN111721302B - Method for recognizing and sensing complex terrain features on surface of irregular asteroid - Google Patents

Method for recognizing and sensing complex terrain features on surface of irregular asteroid Download PDF

Info

Publication number
CN111721302B
CN111721302B CN202010591984.0A CN202010591984A CN111721302B CN 111721302 B CN111721302 B CN 111721302B CN 202010591984 A CN202010591984 A CN 202010591984A CN 111721302 B CN111721302 B CN 111721302B
Authority
CN
China
Prior art keywords
area
image
features
region
asteroid
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202010591984.0A
Other languages
Chinese (zh)
Other versions
CN111721302A (en
Inventor
朱圣英
修义
崔平远
徐瑞
梁子璇
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beijing Institute of Technology BIT
Original Assignee
Beijing Institute of Technology BIT
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Beijing Institute of Technology BIT filed Critical Beijing Institute of Technology BIT
Priority to CN202010591984.0A priority Critical patent/CN111721302B/en
Publication of CN111721302A publication Critical patent/CN111721302A/en
Application granted granted Critical
Publication of CN111721302B publication Critical patent/CN111721302B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/24—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for cosmonautical navigation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Astronomy & Astrophysics (AREA)
  • Automation & Control Theory (AREA)
  • Image Analysis (AREA)
  • Traffic Control Systems (AREA)

Abstract

The invention discloses a method for identifying and sensing complex terrain features on the surface of an irregular asteroid, and belongs to the field of deep space exploration. The implementation method of the invention comprises the following steps: and detecting and extracting a binary dark and bright area of the complex topographic features on the surface of the asteroid by using a navigation image shot by a deep space probe. And setting a search window, and pairing potential dark and light areas belonging to the same terrain feature. And estimating the illumination direction in the image according to the imaging characteristics of the dark and bright regions of the terrain features. And identifying and sensing the meteorite crater and the rock by utilizing the estimated illumination direction, and accurately predicting the illumination direction of the image, thereby realizing the identification and sensing of the complex terrain features on the surface of the asteroid. Based on the asteroid surface topographic characteristics that detect, solve the relevant engineering problem in the deep space exploration field, include: providing an optical road sign for the navigation process of the detector, and performing optical road sign navigation; and the landing obstacle avoidance is realized, and the landing safety of the detector is improved. Compared with a laser radar, the laser radar has the advantages of wide view field, low energy consumption and light weight.

Description

Method for recognizing and sensing complex terrain features on surface of irregular asteroid
Technical Field
The invention relates to a method for identifying and sensing complex terrain features on the surface of a minor planet, wherein the terrain features refer to natural geographic features of meteorite pits, rocks and the like with obvious optical contrast on the surface of the minor planet, and the method is particularly suitable for detecting navigation signposts and landing obstacles of a deep space probe and belongs to the field of deep space detection.
Background
Asteroid exploration is one of the most core tasks of future deep space exploration, wherein a complex terrain feature recognition and perception technology is one of key technologies of the planet exploration. The deep space detection is long in distance and time, and the traditional measurement and control mode has larger communication delay. In addition, the deep space dynamics environment is complex, and the navigation mode based on ground remote control cannot meet the requirement of realizing high-precision detection. With the breakthrough of computer hardware technology and the development of optical sensitive devices, the terrain feature recognition perception based on the spaceborne computer and the optical navigation camera becomes a research hotspot. The natural topographic features such as meteorite craters and rocks are widely present on the surface of the asteroid, the light and shade contrast change is obvious under the illumination condition, the dark and bright area formed by optical imaging is convenient to extract and process, and compared with other modes, the identification sensing method based on the optical information of the natural topographic feature image has the advantages of few sensors, simplicity in processing and wide application prospect.
The asteroid surface is widely distributed with natural topographic features such as meteorite pits and rocks, has small change of illumination conditions, has higher visibility and distinguishability, is easy to extract from the background, and is an important feature for navigation and obstacle avoidance in deep space exploration. Therefore, the method for identifying and sensing the detection target such as meteorite crater and rock is widely researched and applied to the aspect of asteroid detection. Whether the topographic features of the surfaces of the minor planets can be correctly identified and sensed becomes one of the key technologies for success or failure of tasks. The method comprises the steps of utilizing an asteroid surface optical image to identify and sense topographic features, extracting relevant information of the asteroid surface complex topographic features from the image, and determining the types, sizes, positions and the like of the topographic features, so that subsequent autonomous navigation and obstacle avoidance are realized.
In the developed method for identifying and sensing the complex topographic features on the surface of the asteroid, the sunlight illumination direction is a very important judgment condition for feature detection and class distinction, and is often applied as a known quantity. However, since the direction of the sun light in the planet surface image captured by the probe is closely related to the attitude information of the probe at the time of capturing the image, the direction of the sun light is generally difficult to obtain in the case that navigation information such as the attitude of the probe is unknown.
In the prior art [1] (M.Yu, H.Cui, Y.Tian, A new apreach based on classifier detection and matching for visual interpretation in planar mapping, advance in Space research.53(2014)1810 and 1821.), a meteor crater detection algorithm based on region matching is provided, firstly, image features are extracted by an MSER method, the extracted features are divided into binary regions based on an image merging method, meteor craters correctly matched with a shadow region and a bright region are found by setting empirical parameters, the sunlight direction is assumed to be known, and wrongly matched meteor craters are removed according to the relation between a connecting line of the dark region of the meteor craters and the illumination direction, so that the detection of the meteor craters is realized. The algorithm needs a large amount of prior information or experience threshold to ensure a high detection rate, can only identify single terrain features of meteorite craters, and is not suitable for the situation of complex terrain on the surface of the asteroid.
In the prior art [2] (Li national celebration. Planet surface obstacle detection and terrain related navigation method research [ D ]. Harbin Industrial university, 2018,33-44.), a method based on multi-scale regional image contrast enhancement is provided, firstly, a super-pixel segmentation technology is adopted to segment an image homogeneity region, then regional feature contrast information is adopted to enhance the image contrast, the detection of rocks is realized, and the rock detection precision is improved.
In the developed method for detecting the topographic features with unknown sun illumination directions, in the prior art [3] (Zhusheng Ying, Chi Pingyuan, high-altitude Ejection, and the like, China, CN104913784A [ P ],2015-09-16.), in order to realize the extraction of the planetary surface navigation features, firstly, a method for obtaining the sun illumination directions is provided by utilizing the known conditions of extracted shadow areas, light areas and the like. And then, matching the shadow area and the bright area of the navigation feature by using the solved sunlight illumination direction as a constraint condition, and extracting the navigation feature. The method has excessive constraint setting in the dark and bright area pairing process, is difficult to operate and complex to realize, and is not suitable for the situation that various terrain features exist in the image at the same time.
Disclosure of Invention
The invention aims to provide a method for identifying and sensing complex topographic features on the surface of an irregular asteroid.
The purpose of the invention is realized by the following technical scheme.
The invention discloses a method for identifying and sensing complex topographic features on the surface of an irregular asteroid, which is used for detecting and extracting a binary dark and bright area of the complex topographic features on the surface of the asteroid by using a navigation image shot by a deep space probe. And setting a search window, and pairing potential dark and light areas belonging to the same terrain feature. And estimating the illumination direction in the image according to the imaging characteristics of the dark and bright regions of the terrain features. And identifying and sensing the meteorite crater and the rock by utilizing the estimated illumination direction, and accurately predicting the illumination direction of the image, thereby realizing the identification and sensing of the complex terrain features on the surface of the asteroid. Based on the asteroid surface topographic characteristics that detect, solve the relevant engineering problem in the deep space exploration field, include: providing an optical road sign for the navigation process of the detector, and performing optical road sign navigation; and the landing obstacle avoidance is realized, and the landing safety of the detector is improved.
The invention discloses a method for identifying and sensing complex terrain features on the surface of an irregular asteroid, which comprises the following steps:
step 1: and detecting and extracting a binary dark and bright area of the complex topographic features on the surface of the asteroid.
Under the irradiation of solar rays, meteorite crater and rock features are processed in an image binarization mode, the data volume in the image is greatly reduced through binarization of the image, so that the outline of a target can be highlighted, and the extracted binary images of a shadow area and a bright area of the navigation features are respectively marked as BS(u, v) and BI(u, v), (u, v) are coordinates of image pixel points, wherein,the number of the shadow areas and the light areas extracted from the asteroid surface image is respectively recorded as nSAnd nI。
Preferably, the implementation method of the step 1 is as follows:
after an optical navigation image on the surface of the asteroid is captured, due to the complex terrain of the surface of the asteroid, a certain degree of distortion and noise exist in the optical camera during shooting, and the perception effect is influenced, so that the optical image needs to be preprocessed firstly to eliminate or reduce the noise and high-frequency signals in the image.
After a gray level image of a target area is obtained through preprocessing, image processing of the asteroid surface topographic features is carried out based on threshold segmentation and a morphological processing algorithm, the gray level image is initially segmented into a shadow area image and a bright area image through a maximum between-class variance method (Ostu), and then dark and bright area segmentation is carried out on the image based on a two-dimensional maximum entropy threshold segmentation algorithm. The two-dimensional maximum entropy threshold segmentation algorithm is to determine an optimal threshold by using two-dimensional maximum entropy in a background area and a target area, so that the entropy values of the gray level distribution of the background and the target are maximum. The two-dimensional maximum entropy principle image threshold segmentation process is as follows:
by selecting a threshold vector (s, t), the entropy value is made
Figure BDA0002555933740000031
Maximum to get the maximum entropy for the background region and the target region, as shown in the following equation:
Figure BDA0002555933740000032
and (4) dividing the navigation feature shadow area and the light area by using the optimal threshold vector (s, t), and eliminating irrelevant areas to improve the perception precision.
Step 2: and setting a search window, and pairing potential dark and light areas belonging to the same terrain feature.
Because the number of potential topographic features existing in the optical image is large, the amount of identification perception calculation of the topographic features in the whole image is large, and in addition, the consideration is also given toShadow areas and bright areas belonging to the same topographic features are often close to each other, so that the search amount is reduced by arranging a search window based on the idea of local search, and the center C of the shadow areaSpAnd as the center of the search window, designing the size of the search window according to the area size of the shadow area, and searching a potential bright area in the search window. The calculation formula of the area center is as follows:
definition BS(u, v) wherein the p (p ═ 1,2, 3.., n)S) The central coordinate of the area of each shadow area is CSp＝(uSp,vSp)，BI(u, v) wherein (q) is 1,2,3I) The area center coordinate of each bright area is CIq＝(uIq,vIq). For any region, the coordinate value C of the region centercenter＝(ucenter,vcenter) Calculated from the following formula:
Figure BDA0002555933740000033
wherein t is the number of pixels contained in the shadow region or the bright region, pixel (u, v) represents the coordinates of the pixel at the (x, y) position of the image plane,
Figure BDA0002555933740000034
representing a rounding symbol.
Based on the method, the search window is set to be the center C of the shadow areaSpAs a circle center, with RpIs a circular area of radius. Search radius R of p-th shaded areapThe calculation formula of (a) is as follows:
Figure BDA0002555933740000035
where λ is a custom search parameter. t is tSpIs the number of pixels contained in the p-th shadow region.
Within the search window, the p-th shaded area and the q-th bright area of the potential topographic feature are paired by selecting a distance shaded areaDistance d between domain centerspqThe shortest bright area is used as a matching bright area to realize the matching of the meteorite crater and the dark and bright area of the rock.
Figure BDA0002555933740000041
Thereby obtaining paired topographic features Pi(i ═ 1,2,3, …, l), where l is the number of all successfully paired topographical features in the image, and the shortest distance in the ith search window is denoted as di. The pointing direction of the ith pairing feature
Figure BDA0002555933740000042
When the pairing distance is shortest (d)pq＝di) Center of shaded area (u)Sp,vSp) To the center of the bright area (u)Iq,vIq) The pointing direction of (2):
Figure BDA0002555933740000043
and step 3: and estimating the illumination direction in the image according to the imaging characteristics of the dark and bright regions of the terrain features.
By the imaging characteristics of the meteorite crater and the rock characteristics, along the parallel rays of the sun, the meteorite crater sequentially appears a bright area and a shadow area, and the rock sequentially appears a shadow area and a bright area, so that the direction of the shadow area of the meteorite crater characteristics to the bright area is consistent with the illumination direction, and the direction of the shadow area of the rock characteristics to the bright area is opposite to the illumination direction. The characteristics are utilized to estimate the correct illumination direction, and the specific implementation method is as follows:
because the imaging characteristics of meteorite craters and rocks are different, the matching region of the meteorite craters is closer to a circle, the shape of the matching region of the rocks is not fixed, more meteorite crater topographic features are screened by the area ratio J, and the pointing direction from the center of the screened region shadow region to the center of the bright region is utilized
Figure BDA0002555933740000044
Determine bigThe direction of illumination. Wherein, the mating region PiThe minimum circumscribed circle area of all the pixel points is recorded as SciPaired region PiThe fitted elliptical area of the outer edge is denoted Sei. The estimated illumination direction
Figure BDA0002555933740000045
Solving by:
Figure BDA0002555933740000046
Ji＝Sci/Sei (7)
Figure BDA0002555933740000047
Sei＝π·ai·bi (9)
wherein n (n is less than or equal to l) is the number of the features with the area ratio J smaller than the threshold epsilon in all the paired features, riIs the radius of the minimum circumscribed circle of the ith pairing region, aiAnd biThe semi-major and semi-minor axes of the ellipse are fitted to the ith pairing region, respectively.
And 4, step 4: and identifying and sensing the meteorite crater and the rock by using the estimated illumination direction, and accurately predicting the illumination direction of the image.
Considering that the direction of the meteorite crater features points in the same direction as the illumination direction, and the direction of the rock features points in the opposite direction to the illumination direction, judging the type of the paired terrain features in the step 2 based on the illumination direction estimated in the step 3:
Figure BDA0002555933740000051
final accurate image illumination direction
Figure BDA0002555933740000052
Calculated from the following formula:
Figure BDA0002555933740000053
and 5: terrain feature based on recognition perception in step 4 and solved illumination direction
Figure BDA0002555933740000054
For the meteor crater, carrying out ellipse fitting on the outer edge of the matched region to characterize the region; for the rock features, the region is represented by the minimum circumscribed rectangle, so that the complex terrain features on the surface of the irregular asteroid are recognized and sensed.
Further comprising the step 6: solving the related engineering problem in the deep space exploration field based on the asteroid surface topographic characteristics detected in the step 5, comprising: providing an optical road sign for the navigation process of the detector, and performing optical road sign navigation; and the landing obstacle avoidance is realized, and the landing safety of the detector is improved.
Advantageous effects
1. The invention discloses a method for identifying and sensing complex topographic features on the surface of an irregular asteroid. And setting a search window for each shadow region according to the geometric characteristics of the topographic features. And matching potential dark and light areas belonging to the same topographic feature based on the principle that the distance between the dark and light areas is shortest. And estimating the illumination direction in the image according to the imaging characteristics of the dark and bright regions of the terrain features. The distinguishing perception of different terrain features is realized, the accurate image illumination direction is further solved, and the accuracy of the deep space probe for recognizing and perceiving the different terrain features is improved.
2. According to the method for identifying and sensing the complex topographic features on the surface of the irregular asteroid, when the illumination incidence angle changes, the areas of the shadow area and the bright area in the meteorite crater change, but the combination of the shadow area and the bright area is always approximate to an ellipse, the areas of the bright area and the shadow area of the rock change greatly along with the solar incidence angle, and the imaging characteristics of the meteorite crater and the rock are beneficial to image binarization processing and detection, so that the method is suitable for the situation of any solar incidence angle except for vertical irradiation of the sun.
3. The method for recognizing and sensing the complex topographic features on the surface of the irregular asteroid only depends on an optical image shot by a camera, and other sensors are not needed. Compared with a laser radar mode, the method based on the optical image has the advantages of wide field of view, low energy consumption and light weight.
Drawings
FIG. 1 is a schematic flow chart of a method for recognizing and sensing complex topographic features on the surface of an irregular asteroid of the present invention;
FIG. 2 is an original optical image taken by a deep space probe used in the simulation of the present invention;
FIG. 3 is a schematic cross-sectional view of an image of a dark and light area of a meteorite crater and rock feature on the surface of a minor planet in an example of the invention under sunlight;
FIG. 4 is a graph of image binarization results for shaded and bright areas of the topographical feature of FIG. 2 in an embodiment of the present invention;
FIG. 5 is a graph of the recognition and perception result of the complex topographic features on the surface of the planets with irregular minor planets in step 5 and the calculation result of the illumination direction in the optical image in the embodiment of the invention.
Detailed Description
For a better understanding of the objects and advantages of the present invention, reference should be made to the following detailed description taken in conjunction with the accompanying drawings and examples.
To verify the feasibility of the invention, a mathematical simulation was performed using images of the Eros 433 asteroid surface meteorite crater taken at 35km orbital height on 1 month 7 of 2001 by NEAR Shoemaker, as shown in FIG. 2.
As shown in fig. 1, the method for identifying and sensing the complex topographic features on the surface of the irregular asteroid disclosed in this embodiment includes the following specific implementation steps:
step 1: and detecting and extracting a binary dark and bright area of the complex topographic features on the surface of the asteroid.
The optical camera shoots the terrain image of the surface of the target planet, as shown in figure 2, and the imaging characteristics of the terrain feature of the meteor crater under the illumination condition are shown in figure 3. When the sun is not vertically irradiated, as most of meteorite craters are bowl-shaped features and the rocks are convex, along the illumination direction, shadow areas and bright areas appear in sequence on one meteorite crater feature, and the bright areas and the shadow areas appear in sequence on the rock features opposite to the meteorite crater feature. Due to the difference of the illumination incident angles, the areas of the shadow area and the bright area in the meteorite crater can change, but the combination of the shadow area and the bright area is always approximate to an ellipse, and the combined area of the shadow area and the bright area of the rock changes greatly under the condition of different illumination incident angles due to the fact that the rock features protrude out of the surface of the asteroid.
Under the irradiation of solar rays, meteorite craters and rock features are processed in an image binarization mode, the data volume in an image is greatly reduced through binarization of the image, so that the outline of a target can be highlighted, and the extracted binary images of shadow areas and bright areas of the topographic features are respectively marked as BS(u, v) and BI(u, v) and (u, v) are coordinates of pixel points of the image, wherein the number of the shadow areas and the number of the bright areas extracted from the asteroid surface image are respectively marked as nSAnd nI. The specific treatment process is as follows:
after an optical navigation image on the surface of the asteroid is captured, due to the complex terrain of the surface of the asteroid, a certain degree of distortion and noise exist in the optical camera during shooting, and the perception effect is influenced, so that the optical image needs to be preprocessed firstly to eliminate or reduce the noise and high-frequency signals in the image.
After a gray level image of a target area is obtained through preprocessing, image processing of the asteroid surface topographic features is carried out based on threshold segmentation and a morphological processing algorithm, the gray level image is initially segmented into a shadow area image and a bright area image through a maximum between-class variance method (Ostu), and then dark and bright area segmentation is carried out on the image based on a two-dimensional maximum entropy threshold segmentation algorithm. The two-dimensional maximum entropy threshold segmentation algorithm is to determine an optimal threshold by using two-dimensional maximum entropy in a background area and a target area, so that the entropy values of the gray level distribution of the background and the target are maximum. The two-dimensional maximum entropy principle image threshold segmentation process is as follows:
by selecting a threshold vector (s, t), the entropy value is made
Figure BDA0002555933740000061
Maximum to get the maximum entropy for the background region and the target region, as shown in the following equation:
Figure BDA0002555933740000062
and (4) dividing the navigation feature shadow region and the light region by using the optimal threshold vector (s, t), and eliminating irrelevant regions so as to improve the recognition perception accuracy.
Step 2: and setting a search window, and pairing potential dark and light areas belonging to the same terrain feature.
The method has the advantages that the potential topographic features existing in the optical image are more, the fact that the recognition perception calculation amount of the features in the whole image is large and the shadow area and the bright area belonging to the same topographic feature are often close to each other is considered, the original image is divided into a plurality of areas to be perceived based on the idea of local search, therefore, the calculation amount of global search imaging matching is reduced, and the detection efficiency of the topographic features is improved. Therefore, the idea of local search is applied by setting the search window, which uses the center C of the shaded areaSpAnd as the center of the search window, designing the size of the search window according to the area size of the shadow area, and searching a potential bright area in the search window. The calculation formula of the area center is as follows:
definition BS(u, v) wherein the p (p ═ 1,2, 3.., n)S) The central coordinate of the area of each shadow area is CSp＝(uSp,vSp)，BI(u, v) wherein (q) is 1,2,3I) The area center coordinate of each bright area is CIq＝(uIq,vIq). For any region, the coordinate value C of the region centercenter＝(ucenter,vcenter) Calculated from the following formula:
Figure BDA0002555933740000071
wherein t is a pixel included in a shadow region or a bright regionThe number of points, pixel (u, v) representing the coordinates of the pixel point at the location of the image plane (x, y),
Figure BDA0002555933740000072
representing a rounding symbol. The respective centers of the shaded area and the bright area are marked with blue dots by the area center formula of equation (13), as shown in fig. 4(a) and 4(b), respectively.
Based on the method, the search window is set to be the center C of the shadow areaSpAs a circle center, with RpIs a circular area of radius. Search radius R of p-th shaded areapThe calculation formula of (a) is as follows:
Figure BDA0002555933740000073
wherein, λ is a self-defined search parameter, and λ is 3. t is tSpIs the number of pixels contained in the p-th shadow region.
Within the search window, the p-th shadow area and the q-th light area of the potential topographic feature are paired by selecting a distance d from the center of the shadow areapqThe shortest bright area is used as a matching bright area to realize the matching of the meteorite crater and the dark and bright area of the rock.
Figure BDA0002555933740000074
Thereby obtaining paired topographic features Pi(i ═ 1,2,3, …, l), where l is the number of all successfully paired topographical features in the image, and the shortest distance in the ith search window is denoted as di. The pointing direction of the ith pairing feature
Figure BDA0002555933740000075
When the pairing distance is shortest (d)pq＝di) Center of shaded area (u)Sp,vSp) To the center of the bright area (u)Iq,vIq) The pointing direction of (2):
Figure BDA0002555933740000076
and step 3: and estimating the illumination direction in the image according to the imaging characteristics of the dark and bright regions of the terrain features.
By the imaging characteristics of the meteorite crater and the rock characteristics, along the parallel rays of the sun, the meteorite crater sequentially appears a bright area and a shadow area, and the rock sequentially appears a shadow area and a bright area, so that the direction of the shadow area of the meteorite crater characteristics to the bright area is consistent with the illumination direction, and the direction of the shadow area of the rock characteristics to the bright area is opposite to the illumination direction. The correct illumination direction is estimated by utilizing the characteristic, and the specific implementation method is as follows:
because the imaging characteristics of meteorite craters and rocks are different, the matching region of the meteorite craters is closer to a circle, the shape of the matching region of the rocks is not fixed, more meteorite crater topographic features are screened by the area ratio J, and the pointing direction from the center of the screened region shadow region to the center of the bright region is utilized
Figure BDA0002555933740000081
An approximate lighting direction is determined. Wherein, the mating region PiThe minimum circumscribed circle area of all the pixel points is recorded as SciPaired region PiThe fitted elliptical area of the outer edge is denoted Sei. The estimated illumination direction
Figure BDA0002555933740000082
Solving by:
Figure BDA0002555933740000083
Ji＝Sci/Sei (18)
Figure BDA0002555933740000084
Sei＝π·ai·bi (20)
wherein n is the number of features with area ratio J smaller than threshold epsilon in all the paired features, where epsilon is 2 in this example, and r isiIs the radius of the minimum circumscribed circle of the ith pairing region, aiAnd biThe semi-major and semi-minor axes of the ellipse are fitted to the ith pairing region, respectively.
And 4, step 4: and identifying and sensing meteor craters and rocks by using the estimated illumination direction, and accurately calculating the illumination direction of the image.
Considering that the direction of the meteorite crater features points in the same direction as the illumination direction, and the rock features are opposite to the illumination direction, judging the type of the paired terrain features in the step 2 based on the illumination direction estimated in the step 3:
Figure BDA0002555933740000085
final accurate image illumination direction
Figure BDA0002555933740000086
Calculated from the following formula:
Figure BDA0002555933740000087
the pairing result and the calculation result of the illumination direction are shown in fig. 5. The resolved direction of illumination is
Figure BDA0002555933740000088
Since the individual shadow areas have no bright areas in the search window, the situation of no pairing occurs, and the other most shadow areas search for the bright areas which are correctly paired, and the final pairing result is shown in the following table.
Table 1 simulation parameters and results
Figure BDA0002555933740000089
And 5: based on the perceived features of the terrain as identified in step 4 andsolved illumination direction
Figure BDA00025559337400000810
For meteorite craters, the outer edges of the matched regions are used for carrying out ellipse fitting to represent the regions, and for rock features, the regions are marked by the minimum circumscribed rectangle, so that the complex terrain features on the surfaces of the irregular asteroids are recognized and perceived.
Step 6: solving the related engineering problem in the deep space exploration field based on the asteroid surface topographic characteristics detected in the step 5, comprising: providing an optical road sign for the navigation process of the detector, and performing optical road sign navigation; and the landing obstacle avoidance is realized, and the landing safety of the detector is improved.
The embodiment identifies and perceives different terrain features by estimating the illumination direction based on the relation between meteorite craters and the geometrical shapes of rock features. Meanwhile, the sun illumination direction can be automatically solved through pairing search, the problem that the illumination direction is required to be known in the deep space exploration task based on the optical image is solved, and the method is simple to implement and easy to operate. As can be seen from FIG. 5, the fitted ellipse and the minimum circumscribed rectangle are basically matched with the outline of the meteorite crater and the rock feature, and the correctness and the effectiveness of the method for identifying and sensing the complex terrain features on the surface of the irregular asteroid are verified.
And then, completing the identification and perception method of the complex terrain features on the surface of the irregular asteroid required by the deep space probe.
The above detailed description is intended to illustrate the objects, aspects and advantages of the present invention, and it should be understood that the above detailed description is only exemplary of the present invention and is not intended to limit the scope of the present invention, and any modifications, equivalents, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.

Claims (7)

1. The method for recognizing and sensing the complex terrain features on the surface of the irregular asteroid is characterized by comprising the following steps of: comprises the following steps of (a) carrying out,
step 1: detecting and extracting a binary dark and bright area of the complex topographic features on the surface of the asteroid;
step 2: setting a search window, and pairing potential dark and bright areas belonging to the same topographic feature;
and step 3: estimating the illumination direction in the image according to the imaging characteristics of the dark and bright regions of the topographic features;
and 4, step 4: identifying and sensing meteorite craters and rocks by using the estimated illumination direction, and accurately predicting the illumination direction of the image;
and 5: based on the perceived topographic features and the solved illumination direction identified in the step 4, for the meteor crater, performing ellipse fitting on the outer edge of the pairing region to represent the region where the pairing region is located; for the rock features, the region is represented by the minimum circumscribed rectangle, so that the complex terrain features on the surface of the irregular asteroid are recognized and sensed.
2. The method for recognizing and sensing the complex topographic features on the surface of the irregular asteroid as claimed in claim 1, wherein: further comprising the step 6: solving the related engineering problem in the deep space exploration field based on the asteroid surface topographic characteristics detected in the step 5, comprising: providing an optical road sign for the navigation process of the detector, and performing optical road sign navigation; and the landing obstacle avoidance is realized, and the landing safety of the detector is improved.
3. The method for recognizing and sensing the complex topographic features on the surface of the irregular asteroid as claimed in claim 1 or 2, wherein: the step 1 is realized by the method that,
under the irradiation of solar rays, meteorite crater and rock features are processed in an image binarization mode, the data volume in the image is greatly reduced through binarization of the image, so that the outline of a target can be highlighted, and the extracted binary images of a shadow area and a bright area of the navigation features are respectively marked as BS(u, v) and BI(u, v) and (u, v) are coordinates of pixel points of the image, wherein the number of the shadow areas and the number of the bright areas extracted from the asteroid surface image are respectively marked as nSAnd nI。
4. The method for recognizing and sensing the complex topographic features on the surface of the irregular asteroid as claimed in claim 3, wherein: after an optical navigation image on the surface of the asteroid is captured, due to the fact that the topography of the surface of the asteroid is complex, distortion and noise exist to a certain degree in the shooting process of an optical camera, and the perception effect is affected, the optical image needs to be preprocessed to eliminate or reduce the noise and high-frequency signals in the image;
after a gray level image of a target area is obtained through preprocessing, image processing of the asteroid surface topographic features is carried out based on threshold segmentation and morphological processing algorithms, the gray level image is initially segmented into a shadow area image and a bright area image through a maximum inter-class variance method Ostu, and then dark and bright area segmentation is carried out on the image based on a two-dimensional maximum entropy threshold segmentation algorithm; the two-dimensional maximum entropy threshold segmentation algorithm is to determine an optimal threshold by using two-dimensional maximum entropy in a background area and a target area so as to enable the grayscale distribution entropy values of the background and the target to be maximum; the two-dimensional maximum entropy principle image threshold segmentation process is as follows:
by selecting a threshold vector (s, t), the entropy value is made
Figure FDA0003249844290000011
Maximum to get the maximum entropy for the background region and the target region, as shown in the following equation:
Figure FDA0003249844290000012
and (4) dividing the navigation feature shadow area and the light area by using the optimal threshold vector (s, t), and eliminating irrelevant areas to improve the perception precision.
5. The method for recognizing and sensing the complex topographic features on the surface of the irregular asteroid as claimed in claim 4, wherein: the step 2 is realized by the method that,
because the potential topographic features existing in the optical image are more, the amount of identification perception calculation of the topographic features in the whole image is large, and in addition, the consideration is takenConsidering that the shadow area and the bright area belonging to the same topographic feature are often close to each other, the search amount is reduced by arranging the search window based on the thought of local search, and the center C of the shadow area is usedSpAs the center of the search window, designing the size of the search window according to the area size of the shadow area, and searching a potential bright area in the search window; the calculation formula of the area center is as follows:
definition BS(u, v) wherein the center coordinate of the region of the p-th shaded area is CSp＝(uSp,vSp)，BI(u, v) the region center coordinate of the qth bright field is CIq＝(uIq,vIq) Wherein p is 1,2,3S，q＝1,2,3,...,nI(ii) a For any region, the coordinate value C of the region centercenter＝(ucenter,vcenter) Calculated from the following formula:
Figure FDA0003249844290000021
wherein t is the number of pixels contained in the shadow region or the bright region, pixel (u, v) represents the coordinates of the pixel at the (x, y) position of the image plane,
Figure FDA0003249844290000022
represents a rounding symbol;
based on the method, the search window is set to be the center C of the shadow areaSpAs a circle center, with RpA circular area of radius; search radius R of p-th shaded areapThe calculation formula of (a) is as follows:
Figure FDA0003249844290000023
wherein λ is a self-defined search parameter; t is tSpThe number of pixels contained in the p-th shadow region;
within the search window, the p-th shaded area and the q-th light of the potential topographic feature are illuminatedArea pairing is carried out by selecting the distance d from the center of the shadow areapqThe shortest bright area is used as a matching bright area to realize the matching of the meteorite crater and the dark and bright area of the rock;
Figure FDA0003249844290000024
thereby obtaining paired topographic features PiI is 1,2,3, …, l, l is the number of all successfully paired topographic features in the image, and the shortest distance in the ith search window is recorded as di(ii) a The pointing direction of the ith pairing feature
Figure FDA0003249844290000025
When the pairing distance is shortest dpq＝diCenter of shaded area (u)Sp,vSp) To the center of the bright area (u)Iq,vIq) The pointing direction of (2):
Figure FDA0003249844290000026
6. the method for recognizing and sensing the complex topographic features on the surface of the irregular asteroid as claimed in claim 5, wherein: the step 3 is realized by the method that,
by the imaging characteristics of meteorite craters and rock characteristics, along the parallel rays of the sun, the meteorite craters sequentially appear a bright area and a shadow area, and the rock sequentially appears a shadow area and a bright area, so that the direction of the shadow area of the meteorite crater characteristics to the bright area is consistent with the illumination direction, and the direction of the shadow area of the rock characteristics to the bright area is opposite to the illumination direction; the characteristics are utilized to estimate the correct illumination direction, and the specific implementation method is as follows:
because the imaging characteristics of meteorite craters and rocks are different, the matching region of the meteorite craters is closer to a circle, the shape of the matching region of the rocks is not fixed, more meteorite crater topographic features are screened by the area ratio J, and the screened region shadow is utilizedDirection from center of shadow area to center of light area
Figure FDA0003249844290000031
Determining an approximate illumination direction; wherein, the mating region PiThe minimum circumscribed circle area of all the pixel points is recorded as SciPaired region PiThe fitted elliptical area of the outer edge is denoted Sei(ii) a The estimated illumination direction
Figure FDA0003249844290000032
Solving by:
Figure FDA0003249844290000033
Ji＝Sci/Sei (7)
Sci＝π·ri 2 (8)
Sei＝π·ai·bi (9)
wherein n, n is equal to or less than l, r is the number of the features with the area ratio J smaller than the threshold epsilon in all the paired featuresiIs the radius of the minimum circumscribed circle of the ith pairing region, aiAnd biThe semi-major and semi-minor axes of the ellipse are fitted to the ith pairing region, respectively.
7. The method for recognizing and sensing the complex topographic features on the surface of the irregular asteroid as claimed in claim 6, wherein: step 4, the method is realized by the following steps,
considering that the direction of the meteorite crater features points in the same direction as the illumination direction, and the direction of the rock features points in the opposite direction to the illumination direction, judging the type of the paired terrain features in the step 2 based on the illumination direction estimated in the step 3:
Figure FDA0003249844290000034
final accurate image illumination direction
Figure FDA0003249844290000035
Calculated from the following formula:
Figure FDA0003249844290000036
CN202010591984.0A 2020-06-24 2020-06-24 Method for recognizing and sensing complex terrain features on surface of irregular asteroid Active CN111721302B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010591984.0A CN111721302B (en) 2020-06-24 2020-06-24 Method for recognizing and sensing complex terrain features on surface of irregular asteroid

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010591984.0A CN111721302B (en) 2020-06-24 2020-06-24 Method for recognizing and sensing complex terrain features on surface of irregular asteroid

Publications (2)

Publication Number Publication Date
CN111721302A CN111721302A (en) 2020-09-29
CN111721302B true CN111721302B (en) 2021-11-09

Family

ID=72570056

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010591984.0A Active CN111721302B (en) 2020-06-24 2020-06-24 Method for recognizing and sensing complex terrain features on surface of irregular asteroid

Country Status (1)

Country Link
CN (1) CN111721302B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112862800B (en) * 2021-02-25 2023-01-24 歌尔科技有限公司 Defect detection method and device and electronic equipment
CN114187529B (en) * 2021-12-06 2024-05-31 北京理工大学 Small celestial body surface complex terrain feature detection method
CN115272769A (en) * 2022-08-10 2022-11-01 中国科学院地理科学与资源研究所 Automatic moon impact pit extraction method and device based on machine learning

Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102944226A (en) * 2012-12-03 2013-02-27 哈尔滨工业大学 Meteor crater detecting method based on bright and dark area pairing
CN102999915A (en) * 2012-12-03 2013-03-27 哈尔滨工业大学 Meteorite crater matching method based on area ratio
CN103093463A (en) * 2013-01-15 2013-05-08 南京航空航天大学 Meteor crater detecting method based on gray level image
CN103645480A (en) * 2013-12-04 2014-03-19 北京理工大学 Geographic and geomorphic characteristic construction method based on laser radar and image data fusion
CN104913784A (en) * 2015-06-19 2015-09-16 北京理工大学 Method for autonomously extracting navigation characteristic on surface of planet
CN105043395A (en) * 2015-07-17 2015-11-11 浙江工业大学 Real-time dynamic location method for moon surface soft landing of aircraft
CN105547287A (en) * 2016-01-22 2016-05-04 北京理工大学 Irregular coelonavigation sight line information extracting method
CN107389072A (en) * 2017-08-15 2017-11-24 北京理工大学 A kind of small feature loss surface navigation characteristic method for detecting area
CN108871349A (en) * 2018-07-13 2018-11-23 北京理工大学 A kind of deep space probe optical guidance pose weight determination method
CN110619368A (en) * 2019-09-23 2019-12-27 北京理工大学 Planet surface navigation feature imaging matching detection method

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP3671220B2 (en) * 2002-04-30 2005-07-13 独立行政法人 宇宙航空研究開発機構 Moving object detection method

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102944226A (en) * 2012-12-03 2013-02-27 哈尔滨工业大学 Meteor crater detecting method based on bright and dark area pairing
CN102999915A (en) * 2012-12-03 2013-03-27 哈尔滨工业大学 Meteorite crater matching method based on area ratio
CN103093463A (en) * 2013-01-15 2013-05-08 南京航空航天大学 Meteor crater detecting method based on gray level image
CN103645480A (en) * 2013-12-04 2014-03-19 北京理工大学 Geographic and geomorphic characteristic construction method based on laser radar and image data fusion
CN104913784A (en) * 2015-06-19 2015-09-16 北京理工大学 Method for autonomously extracting navigation characteristic on surface of planet
CN105043395A (en) * 2015-07-17 2015-11-11 浙江工业大学 Real-time dynamic location method for moon surface soft landing of aircraft
CN105547287A (en) * 2016-01-22 2016-05-04 北京理工大学 Irregular coelonavigation sight line information extracting method
CN107389072A (en) * 2017-08-15 2017-11-24 北京理工大学 A kind of small feature loss surface navigation characteristic method for detecting area
CN108871349A (en) * 2018-07-13 2018-11-23 北京理工大学 A kind of deep space probe optical guidance pose weight determination method
CN110619368A (en) * 2019-09-23 2019-12-27 北京理工大学 Planet surface navigation feature imaging matching detection method

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
一种基于图像的星体表面撞击坑自动提取方法;王栋等;《宇航学报》;20151031;第36卷(第10期);1164-1171 *
行星表面陨石坑检测与匹配方法;冯军华等;《航空学报》;20100930;第31卷(第9期);1858-1863 *

Also Published As

Publication number Publication date
CN111721302A (en) 2020-09-29

Similar Documents

Publication Publication Date Title
CN111721302B (en) Method for recognizing and sensing complex terrain features on surface of irregular asteroid
CN110619368B (en) Planet surface navigation feature imaging matching detection method
Benarchid et al. Building extraction using object-based classification and shadow information in very high resolution multispectral images, a case study: Tetuan, Morocco
CN103020649A (en) Forest type identification method based on texture information
CN103020605A (en) Bridge identification method based on decision-making layer fusion
CN104063702A (en) Three-dimensional gait recognition based on shielding recovery and partial similarity matching
Ok A new approach for the extraction of aboveground circular structures from near-nadir VHR satellite imagery
CN110147778B (en) Rare earth ore mining identification method, device, equipment and storage medium
Bors et al. Terrain analysis using radar shape-from-shading
Xie et al. A comparison and review of surface detection methods using MBL, MABEL, and ICESat-2 photon-counting laser altimetry data
CN103793715B (en) The personnel in the pit's method for tracking target excavated based on scene information
Aslani et al. Automatic identification of utilizable rooftop areas in digital surface models for photovoltaics potential assessment
Hui et al. An active learning method for DEM extraction from airborne LiDAR point clouds
Kadhim et al. Shadow detection from very high resoluton satellite image using grabcut segmentation and ratio-band algorithms
Yao et al. Automatic extraction of road markings from mobile laser-point cloud using intensity data
Fu et al. LiDAR-based vehicle localization on the satellite image via a neural network
Tian et al. Detection of signal and ground photons from ICESat-2 ATL03 data
CN103093214B (en) A kind of pedestrian detection method based on vehicle mounted infrared camera
Wang et al. A novel approach for multiscale lunar crater detection by the use of path-profile and isolation forest based on high-resolution planetary images
CN103093241B (en) Based on the remote sensing image nonuniformity cloud layer method of discrimination of homogeneity process
Arefi From LiDAR point clouds to 3D building models
Koc-San et al. A model-based approach for automatic building database updating from high-resolution space imagery
CN114187529B (en) Small celestial body surface complex terrain feature detection method
Sun et al. A vision system based on TOF 3D imaging technology applied to robotic citrus harvesting
CN115063698A (en) Automatic identification and information extraction method and system for slope surface deformation crack

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant