Description:FIELD OF INVENTION
[001] The present disclosure relates to reconstructing missing geo-coordinates in drone survey images, and more particularly to a system and method for fixing erroneous or missing geo-coordinates in images without revisiting the survey location.
BACKGROUND
[002] Drone technology has revolutionized aerial surveying and mapping applications across various industries. These unmanned aerial vehicles equipped with high-resolution cameras and GPS systems can efficiently capture georeferenced images of large areas. The captured images, along with their associated geographic coordinates, are crucial for creating accurate maps, 3D models, and other geospatial products.
[003] In typical drone survey operations, the aircraft follows a predetermined flight path, capturing images at regular intervals. Each image is tagged with metadata including its geographic coordinates, typically obtained from the onboard GPS system. This process allows for the creation of comprehensive datasets that can be used for analysis, measurement, and visualization of the surveyed area.
[004] However, drone surveys can sometimes encounter technical issues that result in missing or erroneous geographic coordinates for some of the captured images. This can occur due to various factors such as GPS signal interference, temporary loss of satellite connection, or equipment malfunctions. When such issues arise, the affected images become significantly less valuable for geospatial applications, as their precise location within the survey area is unknown.
[005] The traditional solution to address missing or erroneous coordinates has been to resurvey the affected areas. This approach, while effective, is often time-consuming and costly. It requires mobilizing equipment and personnel back to the survey location, repeating the flight mission, and potentially disrupting project timelines. For large-scale or remote area surveys, the logistical challenges and expenses associated with revisiting the site can be substantial.
[006] Furthermore, in some cases, the conditions present during the original survey may have changed by the time a resurvey is conducted. This can lead to inconsistencies in the dataset and potentially compromise the accuracy of the final geospatial products.
[007] It has been appreciated that a system for reconstructing missing geo-coordinates is needed that overcomes one or more of these problems.
OBJECTIVES OF THE INVENTION
[008] The primary objective of the present invention is to provide a system and method for reconstructing missing or erroneous geo-coordinates in drone survey images without the need to revisit the survey location.
[009] Another objective of the present invention is to improve the efficiency and cost-effectiveness of drone-based surveying operations by eliminating the need for resurveys due to missing coordinate data.
[0010] Yet another objective of the present invention is to enhance the completeness and accuracy of geospatial datasets by reconstructing missing coordinate information using mathematical techniques such as pattern-matched interpolation, extrapolation, and intelligent symmetric approximation.
[0011] Yet another objective of the present invention is to provide a solution that can address various scenarios of missing or erroneous coordinate data, including isolated missing points, sequences of missing coordinates during direction changes, and larger clusters of missing data.
[0012] Yet another objective of the present invention is to maintain the integrity and consistency of survey data by reconstructing missing coordinates based on the available information from surrounding valid data points.
[0013] Yet another objective of the present invention is to reduce the time and resources required for the post-processing of drone survey data by automating the coordinate reconstruction process.
[0014] Yet another objective of the present invention is to enable the salvaging and improvement of datasets that may otherwise be incomplete or unusable due to coordinate errors or omissions.
[0015] Yet another objective of the present invention is to provide a versatile solution that can be applied to various types of drone surveys and across different industries relying on accurate geospatial information.
[0016] Other objectives and advantages of the present invention will become apparent from the following description taken in connection with the accompanying drawings, wherein, by way of illustration and example, the aspects of the present invention are disclosed.
SUMMARY
[0017] This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description.
[0018] The present invention provides a system and method for reconstructing missing geo-coordinates in drone survey images. The system comprises: a drone configured to follow a survey path; a camera mounted on the drone and configured to capture images at image capture points along the survey path; and a processor configured to identify missing or erroneous geo-coordinates in the captured images and reconstruct the missing or erroneous geo-coordinates using at least one of: pattern based interpolation between known geo-coordinates, extrapolation based on known geo-coordinates, and intelligent symmetric approximation based on the symmetry of the survey path. This system enables efficient reconstruction of missing geo-coordinates without the need to revisit the survey location, saving time and resources while maintaining the integrity of the survey data.
[0019] The system may further include features such as a serpentine survey path with parallel tracks, validation of recorded geo-coordinates using error parameters concerning Dilution of Precision (DOP) in GPS systems, like Position Dilution of Precision (PDOP), Horizontal Dilution of Precision (HDOP), or Geometric Dilution of Precision (GDOP), and image capture with frontal and side overlaps. The processor may be configured to apply pattern based interpolation for individual missing geo-coordinates along straight path portions, extrapolation for multiple sequential missing geo-coordinates during direction changes, and intelligent symmetric approximation for missing geo-coordinate clusters during turns. These additional features enhance the system’s ability to accurately reconstruct missing geo-coordinates in various scenarios encountered during drone surveys, improving the overall quality and completeness of the survey data.
BRIEF DESCRIPTION OF FIGURES
[0020] Embodiments of the invention will be described, by way of example, with reference to the following drawings, in which:
[0021] FIG. 1 illustrates a drone survey flight path and locations of images captured with frontal and side overlaps.
[0022] FIG. 2 illustrates a drone flight survey path with erroneous coordinate recordings belonging to an Interpolate correction method.
[0023] FIG. 3 illustrates a drone survey flight path with erroneous coordinate recordings belonging to an Extrapolated correction method.
[0024] FIG. 4 illustrates a drone survey flight path with erroneous coordinate recordings belonging to an Intelligent Symmetric Approximation method.
[0025] FIG. 5 illustrates a comprehensive drone survey flight path pattern with various data collection points and error scenarios.
[0026] Common reference numerals are used throughout the figures to indicate similar features. Reference numerals: 1 refers to a survey path of a drone; 2 refers to image capturing points; 3 refers to a frontal overlap; 4 refers to a direction indicator; 5 refers to a side overlap; 6 & 10 refers to erroneous coordinates belonging to the Interpolate correction method; 7 & 11 refers to erroneous coordinates belonging to the Extrapolated correction method; and 8 & 9 refers to erroneous coordinates belong to intelligent Symmetric Approximation method.
DETAILED DESCRIPTION
[0027] The following description describes various features and functions of the disclosed invention with reference to the accompanying figures. In the figures, similar symbols identify similar components, unless context dictates otherwise. The illustrative aspects described herein are not meant to be limiting. It may be readily understood that certain aspects of the disclosed mobility and walking assistance device can be arranged and combined in a wide variety of different configurations, all of which are contemplated herein.
[0028] Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope of the invention. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
[0029] Features that are described and/or illustrated with respect to one embodiment may be used in the same way or in a similar way in one or more other embodiments and/or in combination with or instead of the features of the other embodiments.
[0030] The terms and words used in the following description and claims are not limited to the bibliographical meanings but are merely used to enable a clear and consistent understanding of the invention. Accordingly, it should be apparent to those skilled in the art that the following description of exemplary embodiments of the present invention are provided for illustration purpose only and not for the purpose of limiting the invention.
[0031] It is to be understood that the singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise.
[0032] It should be emphasized that the term “comprises/comprising” when used in this specification is taken to specify the presence of stated features, integers, steps or components but does not preclude the presence or addition of one or more other features, integers, steps, components or groups thereof. The equations used in the specification are only for computation purposes.
[0033] While this invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not limited to the disclosed embodiments, but, on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0034] The present invention relates to a system and method for reconstructing missing or erroneous geo-coordinates in drone survey images without the need to revisit the survey location. During drone surveys, it is not uncommon for some images to have missing or incorrect geo-coordinate data due to various factors such as signal interference, equipment malfunction, or environmental conditions. Traditionally, addressing this issue would require resurveying the area, which is both time-consuming and costly.
[0035] The system and method described herein provide solutions for reconstructing missing geo-coordinates using three primary techniques: pattern based interpolation, extrapolation, and intelligent symmetric approximation. These techniques allow for the accurate estimation of missing coordinate data based on the available information from surrounding images in the survey sequence. While the UAV’s path may appear to follow a straight line, it can exhibit slight zig-zag motion. Therefore, interpolation and extrapolation rely on pattern-matching methods.
[0036] The invention requires that image files follow a continuous naming sequence and that at least one parameter is available to determine the validity of recorded locations in the images. This parameter may include Dilution of Precision (DOP) indicators such as Positional Dilution of Precision (PDOP), Horizontal Dilution of Precision (HDOP), Geometric Dilution of Precision (GDOP) or other error indicators.
[0037] The system is designed to handle different scenarios of missing data, from isolated missing coordinates to larger gaps in the survey path. By offering multiple reconstruction techniques, the invention provides a comprehensive solution adaptable to various survey patterns and data loss situations encountered in real-world applications.
[0038] FIG. 1 illustrates a diagram showing a drone survey flight path or drone path 1 and locations of images captured with frontal and side overlaps. The drone surveys a desired area by following the drone path 1 in a specific pattern, e.g., a systematic back-and-forth pattern across a survey area. Along the drone path 1, multiple images are captured in linear image capturing points 2 indicated by hollow circles, representing locations where a drone captures images during the survey.
[0039] A frontal overlap 3 is shown by rectangular boxes, which ensures continuous coverage between successive images. One or more direction indicator 4 are shown by arrows marking the drone’s movement direction throughout the survey pattern. The system also includes a side overlap 5 represented by rectangular boxes, which ensures proper coverage between adjacent flight lines.
[0040] The drone moves in a serpentine pattern from top to bottom of the survey area with the image capture points 2 distributed at regular intervals along the drone path 1 to maintain consistent coverage of the surveyed area. The drone path 1 includes moving right to left and left to right across a selected area, creating a systematic coverage pattern.
[0041] The image capture points 2 are arranged in a continuous series with sequential file names. This sequential arrangement allows for easier identification and processing of the captured images. The consistent spacing between the image capture points 2, combined with the frontal overlap 3 and side overlap 5, ensures comprehensive coverage of the survey area without gaps in the collected data.
[0042] FIG. 2 illustrates a system diagram showing a drone survey path with locations of image captures and erroneous coordinate recordings. The diagram depicts a serpentine path that a drone follows during a survey operation, with multiple parallel tracks moving across the survey area. The path is marked by circular indicators representing image capture locations, where empty circles indicate correctly recorded coordinates and filled circles marked as erroneous location indicators 6 show positions where coordinate data was recorded incorrectly.
[0043] An erroneous location indicator 6 may be determined by at least one parameter such as Position Dilution of Precision (PDOP), Horizontal Dilution of Precision (HDOP), Geometric Dilution of Precision (GDOP), or other error parameters. These parameters help identify locations where the recorded coordinates may be inaccurate or unreliable.
[0044] The pattern based interpolation correction method addresses missing or incorrect coordinate data in scenarios where there are erroneous coordinates between two known points in a line. As shown in FIG. 2, the erroneous location indicators 6 appear at various points along the survey path, particularly in areas where the drone changes direction. The pattern based interpolation method utilizes the known coordinates of adjacent correctly recorded points to estimate the coordinates of the erroneous or missing data points.
[0045] In the pattern based interpolation method, the coordinates of the erroneous location indicators 6 are calculated based on the coordinates of the nearest valid data points. This calculation may involve linear pattern based interpolation or more complex algorithms depending on the specific requirements of the survey and the nature of the terrain being surveyed. The pattern based interpolation method provides a reliable means of reconstructing missing coordinate data without the need to revisit the survey location, thereby saving time and resources.
[0046] FIG. 3 illustrates a drone survey flight path pattern with missing coordinate data points. The diagram shows a systematic back-and-forth survey pattern where the drone moves in parallel lines across a designated area. Along the flight path, a series of circles represent image capture points. Empty circles indicate valid coordinate data, while filled black circles, denoted as missing coordinates 7, show locations where coordinate data is missing or erroneous.
[0047] The flight path begins at the top left of the survey area and follows a serpentine pattern downward, with arrows indicating the direction of movement. The path demonstrates regular spacing between capture points with consistent turns at the edges of the survey area. Several clusters of missing coordinates 7 appear in different sections of the path, representing various scenarios where coordinate reconstruction may be necessary.
[0048] One particular scenario addressed by the extrapolation method occurs when the drone is moving straight to the right and rotating to the left. During this transition, multiple sequential points may have missing coordinates. For example, in FIG. 3, a cluster of missing coordinates 7 can be observed near one of the turns in the lower portion of the diagram.
[0049] The extrapolation method may be employed to estimate the missing data for these images. This method utilizes the known coordinates of nearby valid capture points to project or predict the likely positions of the missing coordinates 7. By analyzing the trajectory and speed of the drone based on the surrounding valid data points, the extrapolation method can provide reasonable approximations for the missing coordinate values.
[0050] The extrapolation method may be particularly useful in scenarios where there are gaps in coordinate data during direction changes or at the edges of the survey area. By applying this method, the system can reconstruct a more complete set of coordinate data for the entire survey without the need to revisit the site, potentially saving time and resources in the data collection process.
[0051] FIG. 4 illustrates a drone survey flight path pattern with image capture locations and missing coordinate scenarios. The diagram shows a systematic back-and-forth flight path indicated by arrows at the top and bottom, with multiple parallel survey lines. Along these lines are circular markers representing image capture locations - empty circles indicate properly recorded coordinates while filled black circles represent locations with missing or erroneous coordinate data.
[0052] A missing coordinate cluster 8 is shown in the lower portion of the diagram, exemplifying a type of data gap that may occur during a drone survey. The missing coordinate cluster 8 can include multiple consecutive missing coordinates, typically arising when the drone is moving to the left and rotating to the right. This scenario presents a challenge for coordinate reconstruction, as a larger group of coordinates is missing in sequence.
[0053] The Intelligent Symmetric Approximation method is designed to address these larger missing coordinate clusters. This method leverages the symmetry of the drone’s flight path and the surrounding valid coordinate data to estimate the missing coordinates within the cluster. By analyzing the pattern of valid coordinates before and after the missing coordinate cluster 8, the method can approximate the likely positions where images were captured but coordinates were not recorded or were recorded erroneously.
[0054] The Intelligent Symmetric Approximation method may consider factors such as the drone’s known flight speed, the typical distance between image capture points, and the curvature of the flight path during turns. By combining these factors with the symmetry of the overall survey pattern, the method can generate estimated coordinates for each missing point within the missing coordinate cluster 8.
[0055] This approach is particularly useful for reconstructing coordinates in areas where the drone’s path involves more complex movements, such as when transitioning between parallel survey lines or navigating around obstacles. The Intelligent Symmetric Approximation method provides a means to maintain the continuity and completeness of the survey data without the need for costly and time-consuming resurveying of the area.
[0056] FIG. 5 illustrates a comprehensive drone survey flight path pattern with various data collection points and error scenarios. The diagram shows a systematic back-and-forth survey pattern where a drone moves in parallel lines across a designated area. The path is marked by a series of circles connected by lines, with empty circles representing successful data collection points and filled black circles indicating problematic or missing coordinate data.
[0057] The pattern begins at the top with an arrow indicating the initial direction and continues in a serpentine pattern down to the bottom of the survey area. Three specific regions are highlighted, representing different types of coordinate correction scenarios.
[0058] A symmetric approximation region 9 shows a cluster of missing coordinates that would require Intelligent Symmetric Approximation. This method may be applied when a large group of consecutive coordinates is missing, typically occurring during turns or changes in direction of the drone's flight path.
[0059] An pattern based interpolation region 10 demonstrates points requiring Interpolation correction. The pattern based Interpolation may be used when individual coordinate points are missing between valid coordinates along a relatively straight flight path segment.
[0060] An extrapolation region 11 indicates an area where Extrapolation methods may be needed. Extrapolation may be applied when multiple sequential coordinate points are missing, often during direction changes or at the beginning or end of a flight line.
[0061] The flight lines maintain consistent spacing throughout the survey area, with clear turning points at each end of the parallel runs. This systematic layout ensures complete coverage of the survey area while allowing for the application of different correction methods to specific sections where coordinate data is missing or erroneous.
[0062] The comprehensive survey pattern illustrated in FIG. 5 demonstrates how the three coordinate reconstruction methods - pattern based Interpolation, Extrapolation, and Intelligent Symmetric Approximation - may be applied in various scenarios encountered during a drone survey. By utilizing these methods, the system can reconstruct missing or erroneous coordinate data without the need to revisit the survey location, potentially saving time and resources in the data collection process.
[0063] In some examples, the coordinate reconstruction system may be adapted for use with various types of unmanned aerial vehicles (UAVs) beyond traditional quadcopter drones. Fixed-wing drones, multi-rotor drones, or hybrid VTOL (vertical takeoff and landing) drones may be utilized for aerial surveys, each offering different flight characteristics and survey capabilities.
[0064] The survey patterns may be modified to accommodate different terrain types or survey requirements. For example, in addition to the serpentine pattern described previously, circular patterns, spiral patterns, or grid patterns may be employed depending on the specific survey needs and geographical constraints of the area being mapped.
[0065] Alternative coordinate reconstruction algorithms may be implemented to improve accuracy or efficiency. Machine learning techniques, such as neural networks or support vector machines, may be trained on existing survey data to predict missing coordinates. Additionally, advanced pattern based interpolation methods like kriging or inverse distance weighting may be utilized for more complex terrain or when higher precision is required.
[0066] The invention may find applications in various industries beyond traditional aerial mapping. In agriculture, the system may be used for precision farming, allowing farmers to reconstruct missing data points in crop health surveys. In forestry, the technology could assist in monitoring deforestation or assessing forest health by filling gaps in canopy coverage data. Urban planners may utilize the system for comprehensive city mapping, even when certain areas have restricted access or poor GPS reception.
[0067] In some examples, the coordinate reconstruction system may be integrated with other geospatial technologies. Geographic Information Systems (GIS) software may incorporate the reconstruction algorithms to automatically process and correct survey data. Real-time kinematic (RTK) GPS systems may be combined with the invention to further enhance coordinate accuracy and reduce the occurrence of missing data points.
[0068] The system may be adapted for use in dynamic environments where rapid surveying and data reconstruction are crucial. For instance, in disaster response scenarios, drones equipped with this technology could quickly map affected areas, reconstructing missing data points to provide a complete picture for emergency responders, even in areas with compromised infrastructure or GPS interference.
[0069] The present invention provides a system and method for fixing erroneous or missing geo-coordinates in images captured during drone surveys without the need to revisit the survey location. This innovative approach offers several key advantages that significantly enhance the efficiency and cost-effectiveness of drone-based surveying operations.
[0070] One of the primary advantages of the invention is its ability to reconstruct missing or erroneous coordinate data using various mathematical techniques, including pattern based interpolation, extrapolation, and intelligent symmetric approximation. These methods allow for accurate estimation of missing coordinates based on the available data from surrounding image capture points, eliminating the need for costly and time-consuming resurveys.
[0071] The system’s versatility is demonstrated through its capacity to address different scenarios of missing or erroneous data. Whether dealing with isolated missing points, sequences of missing coordinates during direction changes, or larger clusters of missing data, the invention provides appropriate solutions tailored to each situation.
[0072] By enabling the correction of coordinate data without revisiting the survey location, the invention significantly reduces operational costs and time requirements associated with traditional surveying methods. This efficiency gain is particularly valuable in remote or difficult-to-access locations where repeated surveys would be logistically challenging or expensive.
[0073] The invention also enhances the reliability and completeness of survey data. By providing methods to fill in gaps in coordinate information, it ensures a more comprehensive and accurate representation of the surveyed area, which is crucial for various applications such as mapping, infrastructure planning, and environmental monitoring.
[0074] Furthermore, the system’s ability to work with existing survey data makes it a valuable tool for post-processing and quality control. It allows surveyors to salvage and improve datasets that might otherwise be incomplete or unusable due to coordinate errors or omissions.
[0075] In summary, the invention offers a robust, efficient, and cost-effective solution for addressing common challenges in drone-based surveying. Its ability to reconstruct missing coordinate data without additional field work represents a significant advancement in surveying technology, potentially leading to improved data quality, reduced operational costs, and enhanced productivity in various industries relying on accurate geospatial information.
[0076] Features of any of the examples or embodiments outlined above may be combined to create additional examples or embodiments without losing the intended effect. It should be understood that the description of an embodiment or example provided above is by way of example only, and various modifications could be made by one skilled in the art. Furthermore, one skilled in the art will recognise that numerous further modifications and combinations of various aspects are possible. Accordingly, the described aspects are intended to encompass all such alterations, modifications, and variations that fall within the scope of the appended claims.
, Claims:WE CLAIM:
1. A system for reconstructing missing geo-coordinates in drone survey images, the system comprising:
a drone configured to follow a survey path (1);
a camera mounted on the drone and configured to capture images at image capture points (2) along the survey path; and
a processor configured to:
identify missing or erroneous geo-coordinates in the captured images; and
reconstruct the missing or erroneous geo-coordinates using at least one of:
pattern based interpolation between known geo-coordinates;
extrapolation based on known geo-coordinates; and
intelligent symmetric approximation based on symmetry of the survey path (1).
2. The system as claimed in claim 1, wherein the survey path (1) comprises a serpentine pattern with parallel tracks.
3. The system as claimed in claim 2, wherein the processor is configured to determine the validity of recorded geo-coordinates using at least one error parameter selected from the group consisting of: position dilution of precision (PDOP), horizontal dilution of precision (HDOP), and geometric dilution of precision (GDOP).
4. The system as claimed in claim 1, wherein the camera is configured to capture images with a frontal overlap (3) between successive images and a side overlap (5) between adjacent tracks of the survey path.
5. The system as claimed in claim 1, wherein the processor is configured to apply the pattern based interpolation when individual geo-coordinates (6, 10) are missing between known geo-coordinates along a substantially straight portion of the survey path (1).
6. The system as claimed in claim 1, wherein the processor is configured to apply the extrapolation when multiple sequential geo-coordinates (7, 11) are missing during a direction change of the drone (1).
7. The system as claimed in claim 1, wherein the processor is configured to apply the intelligent symmetric approximation when a cluster of geo-coordinates (8, 9) is missing during a turn in the survey path.
8. A method for reconstructing missing geo-coordinates in drone survey images, the method comprising:
capturing, using a drone, survey images at image capture points (2) along a survey path (1);
identifying missing or erroneous geo-coordinates (9, 10, 11) in the captured survey images; and
reconstructing the missing or erroneous geo-coordinates using at least one of:
pattern based interpolation between known geo-coordinates;
extrapolation based on known geo-coordinates; and
intelligent symmetric approximation based on symmetry of the survey path (1).
9. The method as claimed in claim 8, comprising determining validity of recorded geo-coordinates using at least one error parameter selected from the group consisting of: position dilution of precision (PDOP), horizontal dilution of precision (HDOP), and geometric dilution of precision (GDOP).
10. The method as claimed in claim 9, wherein the survey path (1) comprises a serpentine pattern with parallel tracks, and wherein capturing the survey images comprises capturing images with a frontal overlap (3) between successive images and a side overlap (5) between adjacent tracks of the survey path.