Method for forming trust layer of digital elevation map for autonomous movement of unmanned aerial vehicle

Abstract

FIELD: unmanned aerial vehicles.SUBSTANCE: invention relates to data preparation for unmanned aerial vehicles (UAVs). The method for forming a trust layer of a digital elevation map (DEM) for autonomous UAV movement consists of dividing the digital elevation map into fragments for which metadata is obtained containing: the source of elevation data, the date of receipt or update, spatial resolution, vertical accuracy, horizontal reference, information on the method of constructing the elevation surface, the presence of interpolated, filled or smoothed sections, the class of the underlying surface and signs of terrain change. The elevation data are converted to a single system of coordinates and heights and their local consistency is determined. Particular indicators of metric accuracy, relevance, origin, consistency, relief information content and resistance to changes are calculated for the fragments. A trust indicator for the DEM fragment is formed. A usage mode is assigned to the fragment, including the use of weights. The specified indicators and modes are saved in the form of a trust layer associated with the DEM.EFFECT: reduction in the probability of navigation error.1 cl, 1 dwg

Description

[0001] The invention relates to geographic information systems, digital cartography, autonomous navigation, and data preparation for unmanned aerial vehicles. It is intended to create an additional trust layer for a digital elevation map used in autonomous navigation, given the variable reliability of the original cartographic data and navigation sources.

[0002] The prior art discloses patent RU 2 475 852 C1 "Method for constructing calculated shading zones by controlled enumeration of elements of a relief height matrix", in which the subject of action is a digital terrain map, the properties of which are changed by introducing calculated shading zones into it, characterizing the possibility of visibility of distant objects from a given observation point, created on the basis of the terrain heights, averaged over the area of ​​a square selected as the minimum discrete unit of measurement of the height difference, reduced to a matrix tied to the coordinate grid of the applied coordinate system and allowing, based on the coordinates of a point on the map, to select a specific value of the averaged height from a two-dimensional array of the height matrix, the point of the object, the elevation of the observed object above the relief level and the elevation of the observed objects above the relief level,and which, unlike known methods, forms the said shading zones by processing the data of the calculation control matrix, obtained and distributed among the matrix positions before the start of constructing the calculated shading zones, allows, when influencing the information product of a digital terrain map, to replace the calculations with the selection of data, to eliminate duplication and, ultimately, to achieve a significant reduction in the time for constructing the calculated shading zones used in organizing the automated control of information collection systems.

[0003] As an analogy, well-known terrain navigation systems that use a digital elevation map in conjunction with a radio altimeter and navigation data can be considered, such as US7409293B2 "Methods and systems for enhancing the accuracy of terrain-aided navigation systems" and US20200025571A1 "Navigation system." General approaches to assessing the quality of DEMs are also known, which analyze errors, artifacts, and internal and external quality control methods for digital elevation models.

[0004] A disadvantage of the known prior art is that the known solutions evaluate the suitability of the terrain primarily within the contour of the current navigation correlation, and not as an independent result of the pre-launch preparation of the route.

[0005] A disadvantage of the prior art is that in known navigation systems, a digital elevation map is usually used as a single source, while the heterogeneity of its quality across the territory is not sufficiently taken into account.

[0006] A disadvantage of the prior art is that metadata about the origin, date, resolution, vertical accuracy and method of constructing the elevation surface are not transformed into a formalized navigation trust layer.

[0007] A disadvantage of the prior art is that there is no mechanism for assigning to each map fragment the status of acceptable use in autonomous navigation.

[0008] The technical challenge is to create a method for forming a trust layer for a digital height map, which allows the height map to be used not as a homogeneous array of values, but as a structured source of navigation information with locally defined reliability, suitable for automatic selection of an autonomous driving mode.

[0009] The solution to the technical problem is ensured due to the fact that the Method for forming a trust layer of a digital height map for the autonomous movement of an unmanned aerial vehicle includes obtaining a digital height map of the autonomous movement area, characterized in that the digital height map is divided into fragments, for each fragment metadata is obtained or calculated, containing at least the source of the height data, the date of receipt or update, spatial resolution, vertical accuracy, horizontal reference, information on the method for constructing the height surface, the presence of interpolated, filled or smoothed sections, the class of the underlying surface and signs of a possible change in the terrain, in the presence of several sources, the height data are reduced to a single system of coordinates and heights, the local consistency of the height data is determined by comparing the fragment with alternative sources or control measurements,calculate for each fragment private indicators of metric accuracy, relevance, origin, consistency, relief information content and resistance to changes, form on their basis a complex indicator of trust of the fragment of the digital height map, assign to the fragment a mode of use in autonomous navigation, including use with normal weight, use with reduced weight, use only upon confirmation by another source or prohibition of use for navigation correction, and save the specified indicators and modes in the form of a trust layer associated with the digital height map and used in the preparation or execution of autonomous movement.

[0010] The technical result consists in reducing the probability of a navigation error caused by the non-uniform quality of a digital height map, due to the formation of a confidence indicator for each fragment of the map and the mode of its use in autonomous navigation.

[0011] Essence of the invention

[0012] The essence of the invention consists of forming an additional confidence layer above the digital elevation map, containing a confidence indicator and a set of reasons determining this confidence for each cell, group of cells, or route segment. Unlike a simple elevation matrix, such a layer allows the navigation system to distinguish between reliable, conditionally reliable, and unreliable sections of the elevation base.

[0013] To form a confidence layer, one or more digital elevation maps are obtained, along with information on the data source, survey date, surface construction method, spatial resolution, vertical and horizontal accuracy, presence of control points, land surface class, seasonal conditions, and known terrain changes. If multiple sources are available, they are converted to a single coordinate and elevation system, after which mutual elevation discrepancies and local consistency between the sources are calculated.

[0014] For each map fragment, partial indicators are calculated: a metric indicator, a time indicator, a source-based indicator, a consistency indicator with alternative data, a relief information indicator, and a robustness indicator to terrain changes. Based on these indicators, a comprehensive confidence indicator is formed. The confidence layer stores not only the final score but also the underlying cause structure: data aging, low resolution, high vertical error, inconsistency between sources, unstable surface type, or insufficient relief clarity.

[0015] The generated confidence layer is linked to the digital elevation map and used in route preparation, the navigation filter, the autonomous navigation reliability control system, and subsequent map baseline updates. If the confidence level is high, the elevation map can be used as the primary correction source. If the confidence level is moderate, it is used with reduced weight and must be confirmed by another source. If the confidence level is low, the corresponding fragment is excluded from navigation correction or requires additional updating.

[0016] Thus, the invention transfers the assessment of the map quality from the level of descriptive metadata to the level of the machine-usable navigation layer, which provides a functional connection between the origin of the cartographic data and the operating mode of the autonomous navigation system.

[0017] Description of the operating principle

[0018] The principle of operation is to create a digital layer that accompanies each elevation value with a reliability indicator. First, the source data is normalized: differences in coordinate systems, vertical datums, units of measurement, and grid spacing are eliminated. The map is then divided into fragments, such as square tiles, strips along a route, or adaptive sections corresponding to zones of uniform data quality.

[0019] The metric properties of the data are analyzed for each fragment. If the source has high resolution, confirmed vertical accuracy, and a recent acquisition date, the metric and temporal properties are increased. If the data is older, contains filled voids, has a coarse grid spacing, or is not confirmed by control measurements, the properties are decreased. If multiple DEMs are present, a discrepancy calculation is performed. A persistent discrepancy between sources is grounds for lowering confidence, even if one source is technically high-resolution.

[0020] Next, the system determines the surface type. For stable landforms, trust can be maintained for a long time, while for quarries, construction sites, embankments, industrial zones, coastlines, snow and vegetation cover, a higher depreciation factor is introduced. The result is a layer in which each fragment is assigned a trust score, a cause category, and an acceptable mode of use.

[0021] When planning a route, the system considers not only the altitude but also the confidence layer. In reliable sections, terrain correction is enabled as standard. In relatively reliable sections, the map size is reduced. In unreliable sections, the map is used only for reference or is not used for navigation corrections. During subsequent updates, the new data replaces the low-confidence sections and automatically recalculates the layer.

[0022] The invention is explained by a figure.

[0023] Figure 1 shows the flowchart for generating a trust layer for a digital elevation map. The flowchart shows multiple data sources, an update archive, and verification results fed into the digital elevation map, which is linked to modules for metadata extraction, quality assessment, cross-source comparison, trust score calculation, and trust layer generation.

[0024] The figure demonstrates that the survey date, spatial resolution, elevation uncertainty, coverage completeness, and source consistency are taken into account when generating the confidence layer. The output is an elevation map with a confidence layer, in which terrain segments are ranked by confidence level from very high to very low. The diagram also reflects the presence of a feedback loop, through which the results of confidence layer generation can be used to update the original digital elevation map and its supporting information.

[0025] Implementation example

[0026] For an autonomous power line inspection area, there are three elevation sources: a global DEM with a resolution of approximately 30 m, a regional model with a resolution of 10 m, and a local model previously acquired by a UAV. This method converts all data to a unified coordinate and elevation system. In a forested area, the global and regional models diverge due to vegetation, and the local model is outdated; the fragment is assigned a medium confidence level and is used only with altimeter confirmation. In the area of ​​a new road junction, a persistent discrepancy between the sources exceeding the acceptable threshold is detected; the fragment receives a low confidence level and is excluded from navigation correction. In open, hilly terrain, the sources are consistent, and the fragment receives a high confidence level.

[0027] Industrial applicability

[0028] The method can be implemented in software for geoinformation data preparation, in UAV ground control systems, in onboard computing modules, in cloud-based map update systems, and in autonomous navigation testbeds. Applications include infrastructure monitoring, agriculture, search and rescue, environmental monitoring, delivery, and scientific surveying.

Claims

A method for forming a trust layer of a digital elevation map for the autonomous movement of an unmanned aerial vehicle, including obtaining a digital elevation map of an area of ​​autonomous movement, characterized in that the digital elevation map is divided into fragments, for each fragment metadata is obtained containing the source of elevation data, the date of receipt or update, spatial resolution, vertical accuracy, horizontal reference, information on the method for constructing the elevation surface, the presence of interpolated, filled or smoothed sections, the class of the underlying surface and signs of a possible change in the terrain, in the presence of several sources the elevation data are brought to a single system of coordinates and elevations, local consistency of the elevation data is determined by calculating the discrepancies in elevations between a fragment and at least one alternative source of elevation data brought to the same system of coordinates and elevations,for each fragment, private indicators of metric accuracy, relevance, origin, consistency, relief information content and resistance to changes are calculated; based on the set of private indicators, a complex indicator of trust for a fragment of a digital elevation map is formed; according to the value of the complex indicator of trust, the fragment is classified as having a high, medium or low level of trust; with a high level of trust, the fragment's elevation data are used as the main source of navigation correction; with a medium level of trust, they are used with a reduced contribution to the navigation correction compared to the high-trust mode and with mandatory confirmation by another source; with a low level of trust, they are excluded from the navigation correction; the specified indicators, trust level and mode of use are saved in the form of a trust layer associated with the digital elevation map and used in the preparation or execution of autonomous movement.

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