Low-altitude aircraft multi-sensor fusion high-precision positioning method and system

Abstract

The invention discloses a low-altitude aircraft multi-sensor fusion high-precision positioning method and system, and relates to the technical field of aircraft positioning, and the method comprises the steps: obtaining the environment parameter information of a low-altitude aircraft, and the reference object position information and physical feature information of a passive physical reference object; judging whether the low-altitude aircraft is in an interference area or not to obtain an interference area judgment result; if the interference area judgment result is that the passive physical reference object is in the interference area, acquiring current sensing data of the passive physical reference object by utilizing near-field sensing equipment according to the physical characteristic information; and determining aircraft position information of the low-altitude aircraft in a global coordinate system according to the environmental parameter information, the current sensing data and the reference object position information. According to the method, the position information of the aircraft can be determined by combining the passive physical reference and various sensing data, so that the aircraft is positioned, and the accuracy and reliability are improved.

Description

Technical Field

[0001] This invention relates to the field of aircraft positioning technology, and in particular to a high-precision positioning method and system for low-altitude aircraft using multi-sensor fusion. Background Technology

[0002] When low-altitude aircraft perform sophisticated autonomous tasks, such as structural inspections of large industrial facilities, the requirements for navigation and positioning accuracy and reliability are extremely high. Traditional methods rely on Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMUs) for positioning. However, in complex and variable environments, especially in areas with strong electromagnetic interference, GNSS signals may degrade under strong electromagnetic interference, leading to a decrease in positioning accuracy and forcing the system to rely more heavily on IMUs. However, the inherent errors of IMUs accumulate over time, causing positioning drift. Simultaneously, strong electromagnetic interference introduces high-frequency noise and transient voltage fluctuations into the aircraft's internal power supply and data transmission lines through electromagnetic induction, thereby causing continuous bias or noise interference to the raw IMU measurement data. This interference is random and covert, manifesting as a difficult-to-detect systematic deviation, resulting in low positioning accuracy and low reliability.

[0003] In summary, the technical problems existing in the relevant technologies need to be improved. Summary of the Invention

[0004] The main objective of this invention is to propose a high-precision positioning method and system for low-altitude aircraft using multi-sensor fusion, which can combine passive physical reference objects and multiple sensing data to determine the aircraft's position information, thereby achieving aircraft positioning and improving accuracy and reliability.

[0005] On one hand, embodiments of the present invention provide a high-precision positioning method for low-altitude aircraft using multi-sensor fusion, comprising the following steps:

[0006] Acquire environmental parameter information, reference object position information, and physical characteristic information of low-altitude aircraft;

[0007] Determine whether the low-altitude aircraft is in the interference zone, and obtain the interference zone determination result;

[0008] If the interference area determination result is that it is in the interference area, then based on the physical feature information, the current sensing data of the passive physical reference object is obtained by using a near-field sensing device. The current sensing data includes lidar sensing data, thermal imaging image sensing data and millimeter-wave radar sensing data.

[0009] Based on the environmental parameter information, the current sensing data, and the reference object position information, the aircraft position information of the low-altitude aircraft in the global coordinate system is determined.

[0010] On the other hand, embodiments of the present invention provide a multi-sensor fusion high-precision positioning system for low-altitude aircraft, comprising:

[0011] The information acquisition module is used to acquire environmental parameter information, reference object position information, and physical characteristic information of the low-altitude aircraft.

[0012] The interference area determination module is used to determine whether the low-altitude aircraft is in an interference area and obtain the interference area determination result.

[0013] The perception data acquisition module is used to acquire the current perception data of the passive physical reference object based on the physical feature information if the interference area judgment result is that it is in the interference area. The current perception data includes lidar perception data, thermal imaging image perception data and millimeter-wave radar perception data.

[0014] The position information determination module is used to determine the position information of the low-altitude aircraft in the global coordinate system based on the environmental parameter information, the current sensing data, and the reference object position information.

[0015] The embodiments of this application include at least the following beneficial effects: The embodiments of this application first obtain the environmental parameter information of the low-altitude aircraft, the reference position information and physical feature information of the passive physical reference, and then determine whether the low-altitude aircraft is in an interference area, and obtain the interference area judgment result. If the interference area judgment result is that it is in an interference area, then based on the physical feature information, the current sensing data of the passive physical reference is obtained using a near-field sensing device, and then based on the environmental parameter information, the current sensing data and the reference position information, the aircraft position information of the low-altitude aircraft in the global coordinate system is determined. Thus, the aircraft position information can be determined by combining the passive physical reference and multiple sensing data to achieve aircraft positioning, thereby improving accuracy and reliability.

[0016] Other features and advantages of the invention will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the invention. The objects and other advantages of the invention may be realized and obtained by means of the structures particularly pointed out in the description and the drawings. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.

[0018] Figure 1 This is a flowchart of a high-precision positioning method for low-altitude aircraft using multi-sensor fusion, according to an embodiment of the present invention.

[0019] Figure 2This is a schematic diagram of the structure of a multi-sensor fusion high-precision positioning system for low-altitude aircraft according to an embodiment of the present invention. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments.

[0021] In related technologies, low-altitude aircraft performing sophisticated autonomous tasks, such as structural inspections of large industrial facilities, face extremely high requirements for the accuracy and reliability of navigation and positioning. Traditional navigation methods, such as fusion schemes relying on Global Navigation Satellite Systems (GNSS) and Inertial Measurement Units (IMUs), often face severe challenges in complex and variable environments, especially in areas with strong electromagnetic interference. These challenges include the degradation or even long-term unavailability of GNSS signals, as well as the hidden biases and noise in IMU data caused by electromagnetic induction. These problems collectively restrict the autonomous operation capability and flight safety of aircraft in high-precision missions.

[0022] For example, a low-altitude multi-rotor aircraft, equipped with an advanced multi-sensor fusion navigation system, is performing an autonomous structural integrity inspection of a large industrial complex. Its primary objective is to inspect factory roofs, overhead pipelines, and other critical infrastructure for potential defects. The aircraft is equipped with a high-precision Global Navigation Satellite System receiver, a high-performance inertial measurement unit, and a downward-viewing lidar for accurate altitude maintenance and local environmental awareness.

[0023] In the initial phase of the mission, the aircraft successfully took off from an open area on the edge of the industrial park. The navigation system operated smoothly, the data quality from various sensors was good, and the fusion processing efficiently integrated the information, ensuring the aircraft flew precisely along the planned route and smoothly entered the inspection area. As the inspection mission progressed, the aircraft gradually flew into the core area of ​​the industrial park. This area is densely populated with various large industrial equipment, including high-power frequency converters, large switching power supplies, electric arc furnaces, and multiple radio transmission towers used for internal communication and data transmission. These facilities generate a complex and high-intensity electromagnetic environment during daily operation, releasing broadband electromagnetic interference.

[0024] As the aircraft approached the area of ​​a large smelter, its Global Navigation Satellite System (GNSS) receiver began to be significantly affected. The strong electromagnetic fields generated by the high-power induction furnaces and substations within the smelter directly interfered with the weak signals from the satellites. The number of satellites captured by the GNSS receiver decreased sharply, the carrier-to-noise ratio dropped significantly, and even carrier phase lock-up and pseudorange measurement noise increased significantly. The monitoring module within the navigation system immediately detected the severe deterioration in the quality of the GNSS positioning information. Its fusion processing logic, according to a preset strategy, automatically reduced the influence of GNSS data in the final positioning calculation, and instead significantly increased reliance on inertial measurement unit (INS) data. During this emergency switchover phase, thanks to the high-frequency update capability of the INS, the aircraft was able to maintain the stability of its flight path and the accuracy of its attitude for a short period.

[0025] However, the aircraft needs to fly continuously for several minutes within this area of ​​strong electromagnetic interference to complete a detailed inspection of specific equipment (such as the top structure of a blast furnace or a cooling tower). The prolonged lack of high-quality Global Navigation Satellite System (GNSS) signals for correction has exacerbated the inherent error accumulation problem of the inertial measurement unit (INS). The INS calculates position and velocity by integrating angular velocity and linear acceleration, but its internal gyroscopes and accelerometers exhibit minute random drift and bias errors. Without external reference correction, these errors accumulate over time, leading to increasingly larger deviations between the calculated position and velocity and the aircraft's actual state, resulting in low positioning accuracy and reliability.

[0026] The embodiments of this application will be explained in detail below with reference to the accompanying drawings:

[0027] Figure 1 This is an optional flowchart of a multi-sensor fusion high-precision positioning method for low-altitude aircraft provided in an embodiment of this application. Figure 1 The method may include, but is not limited to, steps S101 to S104.

[0028] Step S101: Obtain environmental parameter information of the low-altitude aircraft, reference object position information and physical characteristic information of the passive physical reference object;

[0029] Step S102: Determine whether the low-altitude aircraft is in the interference zone and obtain the interference zone determination result;

[0030] Step S103: If the interference area judgment result is that it is in the interference area, then based on the physical feature information, the current sensing data of the passive physical reference object is obtained by using the near-field sensing device. The current sensing data includes lidar sensing data, thermal imaging image sensing data and millimeter-wave radar sensing data.

[0031] Step S104: Determine the position information of the low-altitude aircraft in the global coordinate system based on environmental parameter information, current sensing data, and reference object position information.

[0032] Steps S101 to S104 shown in the embodiments of this application can combine passive physical reference objects and multiple sensing data to determine the aircraft's position information, thereby achieving aircraft positioning and improving accuracy and reliability.

[0033] In some embodiments, steps S101-S104 can first acquire environmental parameter information of the low-altitude aircraft, reference object location information of passive physical reference objects, and physical characteristic information. The environmental parameter information can be acquired in real time through environmental sensors (such as a weather station module) onboard the aircraft, or through an external meteorological service interface. For example, the aircraft can be equipped with temperature sensors, humidity sensors, and barometric pressure sensors to monitor the flight environment in real time. The reference object location information and physical characteristic information can be pre-entered into the system during the mission planning phase to form a reference object database. For example, for an industrial park inspection mission, buildings, pipelines, equipment, etc., within the park can be pre-scanned in 3D to obtain their precise geographic coordinates (reference object location information) and detailed 3D models and material properties (physical characteristic information). This information can be stored in the aircraft's onboard memory or acquired from a ground station via wireless communication.

[0034] Then, it is determined whether the low-altitude aircraft is in an interference zone, thus obtaining the interference zone determination result. A digital geofence for the interference zone can be preset; when the aircraft enters this geofence, it is determined to be in an interference zone. Alternatively, the aircraft can be equipped with an electromagnetic spectrum analyzer to monitor the intensity and frequency distribution of electromagnetic signals in the environment in real time. When strong interference signals in a specific frequency band are detected, it is determined to be in an interference zone. For example, in industrial environments, strong electromagnetic interference may occur near certain large motors or high-voltage equipment. This can be determined by pre-mapping the electromagnetic field distribution in these areas or by monitoring the electromagnetic field intensity in real time.

[0035] If the interference area assessment result indicates that the object is located within an interference area, then based on physical feature information, near-field sensing devices are used to acquire current sensing data of passive physical reference objects. This current sensing data includes lidar sensing data, thermal imaging image sensing data, and millimeter-wave radar sensing data. Near-field sensing devices can include lidar, thermal imaging cameras, and millimeter-wave radar. For example, lidar can emit laser beams and receive reflected signals to generate high-precision 3D point cloud data for constructing a geometric model of the environment. Thermal imaging cameras can capture the thermal radiation from object surfaces to generate thermal images for identifying reference objects with specific thermal characteristics, such as operating equipment or pipes. Millimeter-wave radar can emit millimeter waves and receive echoes to acquire distance, velocity, and angle information of objects; it has strong penetration capabilities and is less affected by environmental factors such as smoke and dust. When acquiring sensing data, preset physical feature information can guide the near-field sensing devices to perform targeted scanning or imaging. For example, if the physical feature information indicates that a reference object has unique thermal radiation characteristics, then a thermal imaging camera can be used preferentially for sensing.

[0036] Based on environmental parameters, current sensing data, and reference object position information, the low-altitude aircraft's position in the global coordinate system is determined. For example, 3D point cloud data acquired by lidar can be matched with 3D models in a reference object database. Point cloud registration algorithms, such as the Iterative Closest Point (ICP) algorithm, are used to calculate the aircraft's position and attitude relative to the reference objects. Simultaneously, thermal imaging images and millimeter-wave radar data can serve as auxiliary information to further verify and correct the positioning results. For instance, thermal imaging images can be used to identify specific components of the reference object, and millimeter-wave radar data can provide distance information to the reference object. This information can be fused with lidar data to improve the robustness and accuracy of the positioning. Environmental parameters can be used to correct sensor data. For example, temperature changes may affect the ranging accuracy of lidar, and air pressure changes may affect the drift of the inertial measurement unit. Compensating for these effects with environmental parameters can improve positioning accuracy. Reference object position information provides a reference benchmark in the global coordinate system, enabling the aircraft to map local sensing results to the global coordinate system, thereby determining its precise position in the global coordinate system.

[0037] Through the above technical solution, this embodiment can solve the problem of insufficient accuracy and reliability of traditional positioning methods in complex interference environments. This embodiment introduces an interference area judgment mechanism, intelligently selecting and fusing multimodal near-field sensing data based on the judgment results to address the challenges of GPS signal degradation and inertial measurement unit data interference. This embodiment can proactively identify signal interference environments and switch to a positioning strategy primarily based on near-field sensing devices. By combining current sensing data acquired from multimodal sensors such as lidar, thermal imaging, and millimeter-wave radar with the physical characteristics and position information of preset passive physical reference objects, high-precision relative positioning can be achieved through fine perception of the local environment and matching with known reference objects, ultimately mapping to the global coordinate system. This embodiment significantly improves the autonomous operation capability and flight safety of low-altitude aircraft in complex and interference environments, avoiding collision risks and mission interruptions caused by positioning errors.

[0038] In some embodiments, in step S102, determining whether the low-altitude aircraft is in an interference zone and obtaining the interference zone determination result may include, but is not limited to, the following steps:

[0039] Step S201: Obtain environmental geometric features, including the relative position of the metal plane and the edge, and the relative orientation of the metal plane and the edge;

[0040] Step S202: Based on the environmental geometric features, identify the first and second parallel planes;

[0041] Step S203: Calculate the first distance between the low-altitude aircraft and the first plane;

[0042] Step S204: Calculate the second distance between the low-altitude aircraft and the second plane;

[0043] Step S205: Calculate the third distance between the first plane and the second plane;

[0044] Step S206: If the third distance is less than the preset plane distance, and the first distance and the second distance are both less than the preset flight distance, then obtain the metal plane signal reflection intensity and plane parallelism.

[0045] Step S207: Match the signal reflection intensity and parallelism of the metal plane with the metal channel structure model and calculate the confidence level;

[0046] Step S208: If the confidence level is greater than the confidence level threshold, then the judgment result of the interference region is determined to be in the interference region.

[0047] In some embodiments, relying solely on simple signal strength thresholds or single sensor data can easily lead to inaccurate judgments in complex environments, particularly areas with numerous metal structures (such as industrial plants, bridges, urban canyons, etc.). This inaccurate judgment may cause low-altitude aircraft to misjudge non-interference areas as interference areas, thus unnecessarily activating near-field sensing equipment and increasing computational burden; or it may fail to identify actual interference areas in a timely manner, resulting in decreased positioning accuracy or even loss of control.

[0048] To this end, environmental geometric features can be acquired first. These features include the relative position and orientation of the metal planes and edges. Environmental geometric features refer to objects or structures with specific geometric shapes present in the environment, with particular attention paid to metal structures that may interfere with the sensor signals of low-altitude aircraft. These features specifically include the relative position and orientation of the metal planes and edges, such as the distance between two metal walls and whether they are parallel. The purpose is to provide basic data for subsequent identification of potential interference sources.

[0049] Then, based on environmental geometric features, a first and second parallel plane are identified. By analyzing the acquired environmental geometric features, parallel metal surfaces in the environment that may form electromagnetic wave reflection or scattering channels can be screened. For example, in a narrow metal channel or pipe, two opposing metal walls constitute the first and second parallel planes. The purpose is to identify specific structures that may cause multipath effects or signal attenuation. Simultaneously, a first distance between the low-altitude aircraft and the first plane, a second distance between the low-altitude aircraft and the second plane, and a third distance between the first and second planes are calculated to quantify the spatial relationship between the low-altitude aircraft and potential interference structures. For example, the first and second distances are used to assess whether the aircraft is inside or near the structure, and the third distance is used to confirm whether the structure is a narrow channel. The purpose is to determine whether the low-altitude aircraft has entered a specific spatial region that may be subject to strong interference.

[0050] If the third distance is less than the preset planar distance, and both the first and second distances are less than the preset flight distance, it indicates that the low-altitude aircraft may have entered a narrow passage or similar structure composed of parallel metal planes. Under this condition, it is necessary to obtain the signal reflection intensity and parallelism of the metal planes. The signal reflection intensity of the metal planes refers to the ability of these metal planes to reflect sensor signals, and the parallelism quantifies the degree of parallelism between two planes. Its purpose is to more precisely assess the actual interference potential of the structure on the sensor signal. The distance to the metal passage structure can be evaluated based on the physical characteristics of a passive physical reference to obtain the preset planar distance. The safe flight distance between the aircraft and the object can be set as the preset flight distance according to flight requirements.

[0051] Next, the signal reflection intensity and parallelism of the metal plane are matched with the metal channel structure model to calculate the confidence level. The metal channel structure model is a pre-established mathematical or empirical model describing the influence of different metal channel structures on sensor signals. By comparing the real-time acquired reflection intensity and parallelism with this model, the similarity between the current environment and a typical interference channel can be calculated, i.e., the confidence level. The purpose is to quantify the probability that the current environment constitutes an interference zone. If the confidence level is greater than the confidence level threshold, the interference zone is determined, indicating that the aircraft is in an interference zone. This means that the geometric features and signal reflection characteristics of the current environment highly match the known strong interference metal channel structure, confirming that the low-altitude aircraft is in an interference zone that severely affects its positioning accuracy. The average confidence level can be calculated by matching historical data with the metal channel structure model and used as the confidence level threshold.

[0052] To illustrate this technical solution more clearly, a specific example is used below. Assume a low-altitude aircraft is conducting an inspection inside a large metal structure factory. First, the aircraft's onboard sensors (such as lidar) scan the flight space, acquiring 3D point cloud data of the environment. By processing this point cloud data, the geometric features of the environment inside the factory, such as metal walls and beams, are extracted, including the relative positions and orientations of these metal surfaces. The system identifies two parallel metal walls inside the factory, labeling them as the first and second planes. Subsequently, the system calculates the first and second distances between the aircraft and these two walls, as well as the third distance between the two walls, in real time. For example, when the aircraft enters a metal passage 5 meters wide (the third distance is less than a preset plane distance, such as 10 meters), and the aircraft is less than 2 meters from both walls (both the first and second distances are less than a preset flight distance, such as 3 meters), the system further activates sensors to acquire the signal reflection intensity and plane parallelism of the metal plane. Assume that the measured reflection intensity is high and the plane parallelism is close to 1. The system matches this data with a preset "strong interference model for metal channels" and calculates a confidence level, for example, 0.95. Since this confidence level is greater than a preset confidence threshold (for example, 0.8), the system will determine that the current area is an interference area and immediately activate the near-field sensing device for high-precision positioning, thereby ensuring the safe and stable flight and precise operation of the aircraft in the complex metal environment inside the factory.

[0053] Through the above technical solution, this embodiment can significantly improve the accuracy and reliability of low-altitude aircraft in judging interference areas in complex environments, especially in areas with a large number of metal structures (such as industrial plants, bridges, urban canyons, etc.). This embodiment, through in-depth analysis of environmental geometric features and matching with metal passageway structure models, can identify potential strong interference areas earlier and more accurately, thereby promptly activating near-field sensing equipment and ensuring positioning accuracy within the interference area. This effectively avoids resource waste due to misjudgment or positioning failure due to missed judgment, and enhances the autonomous navigation and operational capabilities of low-altitude aircraft in complex environments.

[0054] In some embodiments, obtaining environmental geometric features in step S201 may include, but is not limited to, the following steps:

[0055] The flight space is scanned using lidar to obtain three-dimensional point cloud data;

[0056] Downsampling of 3D point cloud data;

[0057] The random sample consensus algorithm is used to perform plane and edge detection on the downsampled 3D point cloud data to obtain metal structure data;

[0058] Extracting environmental geometric features from metal structure data.

[0059] In some embodiments, lidar can be used to scan the flight space to obtain three-dimensional point cloud data. Lidar is used to scan the flight space of low-altitude aircraft in all directions or specific areas. By emitting laser beams and receiving reflected signals, distance information of the surrounding environment can be accurately measured, thereby constructing three-dimensional point cloud data containing the position information of all detectable points in space. This three-dimensional point cloud data can comprehensively reflect the geometric structure of the environment surrounding the aircraft.

[0060] Then, the 3D point cloud data is downsampled. Methods such as voxel mesh downsampling, random downsampling, or uniform downsampling can be used to reduce the density of the point cloud data while preserving key geometric features.

[0061] The Random Sample Consensus (RSC) algorithm is then used to perform plane and edge detection on the downsampled 3D point cloud data to obtain metal structure data. The RSC algorithm is applied to the downsampled 3D point cloud data to identify and extract planar and edge features in the environment. The RSC algorithm effectively detects geometric primitives from noisy and outlier data by iteratively selecting a subset of data to fit a model and evaluating the model's fit with the remaining data. In this process, the focus is on the planes and edges generated by the metal structure, thus obtaining the metal structure data.

[0062] Finally, environmental geometric features are extracted from the metal structure data. These features specifically include the relative positions and orientations of the metal planes and edges. Accurate extraction of these geometric features provides crucial geometric information for subsequent determinations of whether low-altitude aircraft are located in interference zones.

[0063] Through the above technical solution, this embodiment can acquire the geometric features of the environment in which a low-altitude aircraft is located in a high-precision and high-efficiency manner, especially for metal structures that may cause interference. This embodiment ensures the accuracy and reliability of the extracted environmental geometric features through precise scanning by lidar, data optimization via point cloud downsampling, and robust detection using a random sample consistency algorithm. This lays a solid foundation for accurately determining whether a low-altitude aircraft is in an interference zone and improves the accuracy of interference zone identification.

[0064] In some embodiments, in step S104, determining the aircraft position information of the low-altitude aircraft in the global coordinate system based on environmental parameter information, current sensing data, and reference object position information may include, but is not limited to, the following steps:

[0065] Step S301: Obtain the target motion state information of the low-altitude aircraft;

[0066] Step S302: Based on the reference object position information, perform multi-path consistency evaluation on the current sensing data to obtain the geometric consistency of the passive physical reference object under different flight paths;

[0067] Step S303: Match the physical feature information with the current sensing data to evaluate the feature matching degree of the passive physical reference object;

[0068] Step S304: Assess the impact of environmental parameter information on the sensing capabilities of the near-field sensing device;

[0069] Step S305: Determine the confidence coefficient of the passive physical reference object based on geometric consistency, feature matching degree, and perceived influence degree;

[0070] Step S306: Determine the aircraft position information based on the target motion state information and the confidence coefficient.

[0071] In some embodiments, relying solely on acquired basic information for location determination may not adequately address the complex and ever-changing low-altitude flight environment. For example, sensor data may be affected by noise, obstruction, or multipath effects, leading to insufficient positioning accuracy and robustness. If these issues are not addressed, low-altitude aircraft may face risks of positioning drift, decreased accuracy, or even positioning failure during missions, especially in scenarios requiring high-precision positioning.

[0072] Therefore, the target motion state information of the low-altitude aircraft can be obtained first. Kinematic parameters such as velocity, attitude, and angular velocity of the low-altitude aircraft can be acquired in real-time or near real-time using devices such as inertial measurement units, visual odometry, or lidar odometry. This information provides important dynamic references for subsequent position determination.

[0073] Then, based on the reference object's position information, a multipath consistency assessment is performed on the current sensing data to obtain the geometric consistency of the passive physical reference object under different flight paths. This allows analysis of whether the geometric characteristics of signals received by near-field sensing devices from passive physical reference objects are consistent across different propagation paths. For example, by analyzing the geometry, size, and relative position of the same reference object in lidar sensing data at different scanning angles, it can be determined whether its geometric characteristics remain stable and consistent across different sensing paths. The purpose is to identify and eliminate abnormal sensing data caused by multipath effects, occlusion, or sensor errors, ensuring that the geometric information used for positioning is reliable.

[0074] Next, the physical feature information is matched with the current sensing data to evaluate the feature matching degree of the passive physical reference object. The physical features of the pre-stored passive physical reference object (e.g., size, shape, texture, reflectivity) can be compared with the current sensing data acquired in real time by near-field sensing devices (e.g., LiDAR, thermal imaging, millimeter-wave radar). For example, algorithms such as feature point extraction and descriptor matching are used to calculate the similarity between the sensed reference object features and the known reference object features. The purpose is to quantify the degree of agreement between the current sensing data and the known reference object features, thereby determining the validity of the sensing data and the accuracy of reference object identification.

[0075] This assessment evaluates the impact of environmental parameters on near-field sensing devices. It analyzes the effects of environmental factors such as illumination, temperature, humidity, dust, and smoke on the performance of near-field sensing devices, including LiDAR, thermal imaging, and millimeter-wave radar data. For example, high dust concentrations may attenuate LiDAR signals, and extreme temperatures may affect the accuracy of thermal imaging sensors. The aim is to quantify the potential negative impacts of environmental factors on sensor data quality, providing a basis for subsequent data fusion and the determination of confidence coefficients.

[0076] The confidence coefficient of passive physical references is determined based on geometric consistency, feature matching degree, and perceived impact. These evaluation results can be combined to assign a value between 0 and 1 to each passive physical reference, representing its reliability for localization in the current environment. For example, references with high geometric consistency, high feature matching degree, and low perceived impact will be assigned a higher confidence coefficient. The purpose is to weight the localization contribution of different references, prioritizing the use of references with high reliability for localization.

[0077] Finally, the aircraft's position information is determined based on the target's motion state information and the confidence coefficient. The target motion state information of the low-altitude aircraft can be fused with the passive physical reference positioning information after weighting by the confidence coefficient. For example, state estimation algorithms such as Kalman filtering, extended Kalman filtering, or particle filtering can be used to combine motion model predictions with weighted observation data, thereby obtaining more accurate and robust aircraft position information in the global coordinate system. The aim is to further improve the accuracy and stability of positioning through multi-source information fusion.

[0078] To illustrate this technical solution more clearly, a specific example is used below. Assume a low-altitude aircraft is performing an inspection mission in an industrial park with metallic structures and a complex electromagnetic environment. First, the aircraft acquires its own target motion state information, such as current speed, attitude, and angular velocity, through its onboard inertial measurement unit, visual odometry, and lidar odometry. Simultaneously, near-field sensing devices (such as lidar, thermal imaging cameras, and millimeter-wave radar) continuously acquire current sensing data of surrounding passive physical references (such as walls, pillars, and pipes). Specifically, the system performs a multipath consistency evaluation on the lidar sensing data based on preset reference location information. For example, if the lidar point cloud data shows significant geometric inconsistencies when scanning the same pipe from different angles, it indicates possible multipath reflection or occlusion, and the pipe's geometric consistency will be evaluated as low. Simultaneously, the system matches pre-stored pipe physical features (such as diameter and surface material) with the thermal imaging image sensing data and millimeter-wave radar sensing data to evaluate the feature matching degree. For example, if thermal imaging images show abnormal pipe surface temperatures or millimeter-wave radar echo intensity does not match expectations, the feature matching accuracy will decrease. Furthermore, environmental sensors detect high dust concentrations and electromagnetic interference within industrial parks; this environmental parameter information is used to assess the impact on near-field sensing devices. For instance, high dust concentrations increase lidar attenuation, and electromagnetic interference affects the performance of millimeter-wave radar.

[0079] Based on the above evaluation results, the system dynamically calculates a confidence coefficient for each passive physical reference. For example, a pillar with high geometric consistency, high feature matching, and low environmental influence will be assigned a higher confidence coefficient, while a pipe with low geometric consistency, poor feature matching, and high environmental influence will be assigned a lower confidence coefficient. Finally, the aircraft's target motion state information (such as the predicted position obtained through the inertial navigation system) is fused with the positioning information of these passive physical references, which have been weighted by confidence coefficients. For example, an extended Kalman filter is used to combine the motion prediction with the weighted observation data, thereby outputting a more accurate, stable, and robust aircraft position information in the global coordinate system. In this way, the aircraft can maintain high-precision positioning capabilities even when some sensor data is damaged or the environment is complex.

[0080] Through the above technical solution, this embodiment can significantly improve the high-precision positioning capability of low-altitude aircraft in complex environments. Specifically, by introducing a multi-dimensional evaluation mechanism (geometric consistency, feature matching degree, and perception impact degree), and dynamically adjusting the trust coefficient of passive physical reference objects accordingly, the system can intelligently identify and utilize the most reliable positioning information source. Furthermore, by fusing the target motion state information of the low-altitude aircraft, the dynamic tracking capability and anti-interference performance of the positioning are further enhanced. Therefore, this embodiment can provide more accurate, stable, and robust aircraft position information when facing challenges such as sensor data uncertainty, environmental interference, and differences in the reliability of reference objects, effectively avoiding potential positioning drift and accuracy degradation problems, thereby ensuring the safety and efficiency of low-altitude aircraft mission execution.

[0081] In some embodiments, in step S306, determining the aircraft position information based on the target motion state information and the confidence coefficient may include, but is not limited to, the following steps:

[0082] Acquire historical sensing data;

[0083] By comparing historical and current sensing data, the degree of interference with target sensing data is identified. The degree of interference with target sensing data includes the degree of interference with lidar sensing data, thermal imaging image sensing data, and millimeter-wave radar sensing data.

[0084] The trust coefficient is adjusted based on the degree of interference in the target perception data;

[0085] The aircraft's position information is determined based on the target's motion status information and the adjusted confidence coefficient.

[0086] In some embodiments, historical sensing data can be acquired first. Historical sensing data refers to the collection of sensing information about passive physical reference objects acquired by near-field sensing devices at different times or along different flight paths prior to the current moment. This data can be stored in the storage unit inside the low-altitude aircraft or acquired through communication with a ground station. Historical sensing data may include, but is not limited to, past lidar point cloud data, thermal imaging image sequences, and millimeter-wave radar echo data. Its purpose is to provide a temporal reference benchmark for assessing the quality of current sensing data.

[0087] Then, historical and current sensing data are compared to identify the degree of interference in the target sensing data. This interference includes interference with lidar, thermal imaging, and millimeter-wave radar data. The degree of interference experienced by different types of sensors (e.g., lidar, thermal imaging, millimeter-wave radar) can be quantified by analyzing the differences or patterns of change between current and historical sensing data. For example, the degree of interference in lidar sensing data can be identified by comparing the point cloud density and noise level of current lidar sensing data with historical data; the degree of interference in thermal imaging data can be identified by analyzing texture distortion and temperature anomalies in thermal imaging images with historical images; and the degree of interference in millimeter-wave radar sensing data can be identified by evaluating the intensity of the millimeter-wave radar echo signal, Doppler shift anomalies, and historical data. Identifying these levels of interference helps to accurately assess the real-time reliability of each sensor.

[0088] The trust coefficient is then adjusted based on the level of interference in the target perception data. The trust coefficient of passive physical references can be dynamically corrected based on the identified interference levels of lidar perception data, thermal imaging perception data, and millimeter-wave radar perception data. For example, if a sensor has a high level of interference, its weight in the fusion localization can be reduced accordingly, i.e., its corresponding trust coefficient can be lowered; conversely, if the interference level is low, its trust coefficient can be maintained or appropriately increased. This adjustment can be linear, non-linear, or based on a preset lookup table. The aim is to ensure that the trust coefficient more accurately reflects the actual perception quality and reliability of the sensors in the current environment.

[0089] Finally, the aircraft's position information is determined based on the target motion state information and the adjusted confidence coefficient. The dynamically adjusted confidence coefficient can be combined with the target motion state information of the low-altitude aircraft, and a fusion algorithm (such as extended Kalman filtering, unscented Kalman filtering, or particle filtering) can be used to calculate the precise position of the low-altitude aircraft in the global coordinate system. The adjusted confidence coefficient provides more reliable sensor weights for the fusion algorithm, thus enabling the output of high-precision aircraft position information under different interference conditions.

[0090] To illustrate this technical solution more clearly, a specific example is used below. Assume a low-altitude aircraft is performing an inspection mission within an industrial park. At a certain moment, the aircraft enters an area containing numerous metal structures and high-temperature steam. First, the aircraft continuously acquires historical sensing data, such as sensing data from lidar, thermal imaging, and millimeter-wave radar over the past 10 seconds. When the aircraft enters the aforementioned area, the near-field sensing device acquires current sensing data. The system compares the current lidar sensing data with historical lidar sensing data, finding sparse point clouds and significantly increased noise, indicating a high degree of interference in the lidar sensing data. Simultaneously, comparing the current thermal imaging image sensing data with historical thermal imaging image sensing data reveals large areas of overexposure or blurring, indicating a high degree of interference in the thermal imaging image sensing data. However, comparing the millimeter-wave radar sensing data with historical data reveals relatively stable echo signals and a low degree of interference.

[0091] Based on the identified interference levels in the target perception data, the system adjusts the confidence coefficients previously determined based on geometric consistency, feature matching degree, and perception impact. Specifically, the confidence coefficients for lidar and thermal imaging sensors are significantly reduced, while the confidence coefficient for millimeter-wave radar remains at a high level or is slightly reduced. Finally, the system combines the target motion state information of the low-altitude aircraft with these adjusted confidence coefficients and calculates the aircraft's precise position using a fusion algorithm. Because heavily interfered sensor data is assigned lower weights during the calculation process, while relatively reliable millimeter-wave radar data is assigned higher weights, the aircraft can still obtain high-precision positioning results even in environments with strong interference, avoiding positioning drift or errors caused by the failure or interference of a single sensor.

[0092] Through the above technical solution, this embodiment can dynamically assess and identify the interference levels of different types of sensors based on the comparison results of historical and current sensing data, and then finely adjust the confidence coefficient of passive physical reference objects. Therefore, when determining the position information of low-altitude aircraft, it can more accurately reflect the real-time sensing quality of each sensor, effectively reduce positioning errors caused by local or instantaneous sensor interference, and significantly improve the positioning accuracy and system robustness of low-altitude aircraft in complex and variable environments.

[0093] In some embodiments, in step S306, determining the aircraft position information based on the target motion state information and the confidence coefficient may include, but is not limited to, the following steps:

[0094] Based on physical characteristic information, multiple passive physical reference objects are classified into categories, resulting in multiple reference object groups;

[0095] Based on the spatial distribution relationship of multiple reference objects in the global coordinate system, the contribution of multiple passive physical reference objects is ranked to obtain the contribution ranking result;

[0096] Based on the contribution ranking results, adjust the confidence coefficient corresponding to each passive physical reference.

[0097] The aircraft's position information is determined based on the target's motion state information and multiple adjusted confidence coefficients.

[0098] In some embodiments, when multiple passive physical references exist, failure to fully consider the differences in their physical characteristics and their spatial distribution in the global coordinate system may lead to the averaged or misjudged contribution of each reference to the positioning, thereby affecting the accuracy and reliability of the final aircraft position information. If the above problems are not addressed, in complex multi-reference environments, the positioning accuracy may not meet expectations, or even positioning deviations may occur.

[0099] Therefore, multiple passive physical reference objects can be categorized based on their physical characteristics, resulting in multiple reference object groups. Pre-acquired physical characteristics of the passive physical reference objects, such as their material, shape, size, reflectivity, and thermal radiation characteristics, can be used to classify multiple passive physical reference objects in the environment. For example, reference objects can be grouped into metallic, non-metallic, regular geometric, and irregular geometric categories. The purpose is to group reference objects with similar physical properties together for subsequent unified or differentiated processing and evaluation.

[0100] Then, based on the spatial distribution of multiple reference groupings in the global coordinate system, the contributions of multiple passive physical references are ranked, resulting in a contribution ranking. After classifying the reference groups, the spatial distribution relationships of these reference groupings in the global coordinate system, such as their relative positions, distances, densities, and geometric constraints on low-altitude aircraft positioning, can be further analyzed. For example, references that are close to low-altitude aircraft, evenly distributed, and provide good geometric configurations typically contribute more to positioning. By evaluating these spatial distribution relationships, the importance of each passive physical reference can be quantified, thus obtaining a contribution ranking result. The purpose is to identify the key references that have the greatest impact on positioning accuracy and to provide a basis for subsequent confidence coefficient adjustments.

[0101] Next, based on the contribution ranking results, the trust coefficient corresponding to each passive physical reference is adjusted. Based on the contribution ranking results obtained above, the trust coefficient of each passive physical reference is dynamically adjusted. For example, the trust coefficient of a reference with a higher contribution ranking can be appropriately increased to give it greater weight in the positioning calculation; while the trust coefficient of a reference with a lower contribution ranking can be appropriately decreased. This adjustment can be linear, non-linear, or based on preset rules. The purpose is to ensure that the trust coefficient of each reference more accurately reflects its actual value and reliability to the current positioning task.

[0102] Finally, based on the target motion state information and multiple adjusted confidence coefficients, the aircraft's position information is determined. After individually adjusting the confidence coefficients of each passive physical reference, these adjusted confidence coefficients are combined with the target motion state information of the low-altitude aircraft (e.g., motion state obtained by fusing data from inertial navigation, visual odometry, etc.). A multi-sensor fusion algorithm (such as extended Kalman filtering, particle filtering, etc.) is used for comprehensive calculation to ultimately determine the high-precision position information of the low-altitude aircraft in the global coordinate system. The aim is to achieve more accurate and robust positioning results by weighted fusion of positioning information from different references.

[0103] To illustrate this technical solution more clearly, a specific example is used below. Suppose a low-altitude aircraft is performing an inspection mission within an industrial park containing various passive physical references. These references may include metal pipes, concrete walls, glass windows, and some irregularly shaped equipment. First, based on the physical characteristics of these references—such as the reflective properties and regular geometry of metal pipes, the diffuse reflection properties and planar structure of concrete walls, and the transmission and reflection properties of glass windows—they are categorized into several groups, including "metal structures," "building structures," and "special materials." Second, the spatial distribution of these reference groups in the global coordinate system is analyzed. For example, if three metal pipes are stably distributed in a triangle around the aircraft and are relatively close to it, their contribution will be assessed as high. Conversely, if a concrete wall is far from the aircraft or its surface is obstructed, its contribution may be assessed as low. In this way, the contribution of all passive physical references is ranked.

[0104] Next, based on the obtained contribution ranking results, the confidence coefficient of each passive physical reference is adjusted. For example, the confidence coefficient of the metal pipe with the highest contribution is increased, while the confidence coefficient of the distant wall with a lower contribution is decreased. Finally, the target motion state information of the low-altitude aircraft (e.g., obtained by weighted fusion of inertial measurement unit data, visual odometry data, and lidar odometry data) is fused with the confidence coefficients of these individually adjusted passive physical references to determine the high-precision aircraft position information in the global coordinate system. In this way, even in complex and variable reference environments, the accuracy and robustness of the positioning results can be ensured.

[0105] Through the above technical solution, this embodiment overcomes the limitation that simple fusion may lead to a decrease in positioning accuracy in multi-reference environments. This embodiment classifies and ranks passive physical references by contribution, enabling a more refined and reasonable allocation of the weight of each reference in positioning. This dynamic trust coefficient adjustment mechanism based on the physical characteristics and spatial distribution of references ensures that high-contribution, high-reliability references play a greater role in positioning calculations, while the influence of low-contribution or potentially unreliable references is effectively suppressed. Therefore, this embodiment significantly improves the accuracy and stability of high-precision multi-sensor fusion positioning for low-altitude aircraft in complex and variable environments, especially in scenarios with a large number of references of diverse types and uneven distribution, enabling the acquisition of more accurate and reliable aircraft position information.

[0106] In some embodiments, obtaining the target motion state information of the low-altitude aircraft in step S301 may include, but is not limited to, the following steps:

[0107] Step S401: Obtain initial motion state information, which includes inertial measurement unit data, visual odometry data, and lidar odometry data;

[0108] Step S402: Evaluate the quality of the initial motion state information and identify abnormal data;

[0109] Step S403: Correct the abnormal data and update the initial motion state information;

[0110] Step S404: Based on the intensity of environmental interference, perform weighted fusion of the updated inertial measurement unit data, visual odometry data, and lidar odometry data to obtain target motion state information.

[0111] In some embodiments, when a low-altitude aircraft flies in a complex and variable environment, the raw motion state information acquired by its onboard sensors, such as inertial measurement units, visual odometry, and lidar odometry, is often affected by environmental noise, sensor drift, occasional malfunctions, or external interference (such as vibration, changes in lighting, and electromagnetic interference), leading to unstable data quality and even abnormal data. Directly using this unprocessed or simply fused motion state information may introduce significant errors, thereby affecting the accuracy of subsequent aircraft position determination and the overall robustness of the system.

[0112] To this end, initial motion state information can be obtained first. This initial motion state information refers to the raw or pre-processed motion data directly acquired from various motion sensors onboard the low-altitude aircraft. Initial motion state information includes inertial measurement unit (IMU) data, visual odometry (VOM) data, and lidar OOM data. IMU data typically includes measurements from accelerometers and gyroscopes, providing information on the aircraft's attitude, angular velocity, and linear acceleration. VOM data estimates the aircraft's attitude changes by analyzing the motion of feature points between consecutive image frames. LiDAR OOM data uses environmental point cloud data acquired through lidar scanning for matching and tracking to estimate the aircraft's trajectory. These multi-source data together constitute a preliminary description of the aircraft's motion state.

[0113] Then, the initial motion state information is quality-assessed to identify outlier data. The purpose is to identify, remove, or correct any abnormal data. Outlier data may originate from sensor malfunctions, environmental interference (such as strong light, smoke, or vibration), or data transmission errors. Quality assessment can be performed using statistical methods (such as mean, variance, and median filtering) or model-based prediction methods (such as Kalman filter residual analysis). Through quality assessment, outliers or inconsistent data segments that significantly deviate from the normal data pattern can be identified.

[0114] The abnormal data is then corrected to update the initial motion state information. After identifying abnormal data, appropriate measures can be taken to restore the data's validity or reduce its negative impact. Correction methods may include, but are not limited to: interpolating with adjacent normal data, replacing data with historical data or model predictions, or smoothing the abnormal data. The corrected data will be used to update the initial motion state information, making it closer to the aircraft's actual motion state.

[0115] Finally, based on the intensity of environmental interference, the updated inertial measurement unit (IMU) data, visual odometry (VOM) data, and lidar OOM data are weighted and fused to obtain the target motion state information. The aim is to comprehensively utilize the advantages of different sensors and dynamically adjust the weights of each sensor according to the current environmental interference to obtain the most reliable target motion state information. Environmental interference intensity can be assessed based on various factors, such as electromagnetic interference levels, lighting conditions, obstacle density, and vibration frequency. For example, in areas with blocked GPS signals or strong electromagnetic interference, the weight of IMU data may decrease, while the weight of VOM or lidar OOM data may increase accordingly; in environments with sparse texture or insufficient lighting, the weight of VOM may decrease. Through weighted fusion, the error accumulation of a single sensor and environmental sensitivity can be effectively suppressed, resulting in a more robust and accurate estimation of the aircraft's motion state.

[0116] To illustrate this technical solution more clearly, a specific example is used below. Suppose a low-altitude aircraft is performing an inspection mission in an urban canyon environment, which may present complex challenges such as weak GPS signals due to tall buildings, visual odometry mismatches caused by glass curtain walls, and electromagnetic interference. First, the aircraft continuously acquires inertial measurement unit (IMU) data (e.g., acceleration, angular velocity), visual odometry data (e.g., pose estimation based on feature point matching), and lidar odometry data (e.g., pose estimation based on point cloud registration). This data is considered initial motion state information. Second, the system performs real-time quality assessments on this initial motion state information. For example, for IMU data, abrupt changes in acceleration and angular velocity can be detected to identify shock or vibration anomalies; for visual odometry data, the inlier ratio or reprojection error of feature point matching can be monitored, and mismatches may exist if the ratio is below a preset threshold; for lidar odometry data, the convergence or matching error of point cloud registration can be evaluated. If a significant decrease in the data quality of a particular sensor or the appearance of outliers are detected, it is marked as abnormal data.

[0117] Subsequently, the identified abnormal data is corrected. For example, if the visual odometry experiences numerous mismatches due to sudden changes in illumination within a certain time period, causing a jump in pose estimation, the system can temporarily reduce its weight and perform short-term predictive interpolation using inertial measurement unit (IMU) data and lidar odometry data, or perform local optimization and reconstruction after the data returns to normal. Finally, based on the current environmental interference intensity, the updated IMU data, visual odometry data, and lidar odometry data are weighted and fused. For example, when the aircraft enters an area with weak GPS signals, the system increases the weight of lidar odometry and IMU data; when the aircraft approaches a glass curtain wall, the system decreases the weight of visual odometry and increases the weight of lidar odometry, because lidar is more robust to geometric structures. Through this dynamic weighted fusion, even in complex urban canyon environments, the system can continuously output high-precision and high-reliability target motion state information, thereby supporting the aircraft to achieve accurate global positioning.

[0118] Through the above technical solution, this embodiment can significantly improve the accuracy and reliability of acquiring target motion state information for low-altitude aircraft. By performing quality assessment and abnormal data correction on the initial motion state information, the accumulation of positioning errors caused by sensor noise, drift, or occasional failures is effectively avoided. Furthermore, the adaptive weighted fusion mechanism based on environmental interference intensity enables the system to intelligently adjust the contributions of different sensors according to the complexity of the actual operating environment. This allows the system to output high-confidence motion state information even in various harsh or challenging environments, such as strong electromagnetic interference, drastic changes in lighting, or areas with missing textures. This high-quality target motion state information serves as a key input for subsequent aircraft position determination, directly improving the accuracy and stability of the final aircraft position information in the global coordinate system, thus providing a solid guarantee for the safe flight and precise operation of low-altitude aircraft.

[0119] In some embodiments, step S402, which involves a quality assessment of the initial motion state information and the identification of anomalous data, may include, but is not limited to, the following steps:

[0120] Obtain local vibration frequency information of the area where the low-altitude aircraft is located;

[0121] Based on the local vibration frequency information, the initial motion state information is filtered to eliminate noise introduced by high-frequency vibration;

[0122] Outlier detection is performed on the filtered initial motion state information to identify abnormal data.

[0123] In some embodiments, low-altitude aircraft are often affected by factors such as their own structural vibration and airflow disturbance during flight, generating high-frequency vibration noise. This noise may confuse the real abnormal data in the initial motion state information, leading to a decrease in the accuracy of quality assessment, and thus affecting the subsequent correction of abnormal data and the determination of target motion state information.

[0124] To this end, the local vibration frequency information of the area where the low-altitude aircraft is located can be obtained first. Vibration sensors (such as accelerometers) installed on the low-altitude aircraft can collect vibration data in real time under specific flight conditions, and this data can be used for spectral analysis to obtain the vibration frequency characteristics of the current area. The local vibration frequency information refers to the frequency characteristics of mechanical vibration or structural resonance caused by factors such as the engine, propeller, and airflow disturbances under specific flight conditions. Its purpose is to provide a basis for subsequent filtering processing, ensuring that noise within a specific frequency range can be eliminated in a targeted manner.

[0125] Then, based on the local vibration frequency information, the initial motion state information is filtered to eliminate noise introduced by high-frequency vibrations. Digital signal processing technology can be used to process the initial motion state information (including inertial measurement unit data, visual odometry data, and lidar odometry data) to suppress or eliminate signal components within a specific frequency range. Band-stop filters, notch filters, or adaptive filters can be used, with their parameters dynamically adjusted based on the acquired local vibration frequency information. The aim is to accurately remove noise introduced by high-frequency vibrations and prevent this noise from interfering with the accuracy of the motion state information.

[0126] Outlier detection is then performed on the filtered initial motion state information to identify anomalous data. Statistical methods or machine learning algorithms can be used to identify observations in the dataset that significantly deviate from other data points. Specifically, distance-based methods (e.g., K-nearest neighbors), density-based methods (e.g., LOF), model-based methods (e.g., Gaussian mixture models), or isolation-based methods (e.g., Isolation Forest) can be employed. The aim is to identify erroneous data that still exists after filtering, caused by sensor malfunctions, transient strong interference, or other anomalies, ensuring the accuracy of subsequent data correction and fusion.

[0127] This embodiment first acquires the local vibration frequency information of the area where the low-altitude aircraft is located, enabling an accurate understanding of the characteristics of the main noise sources in the current environment. Subsequently, based on this frequency information, targeted filtering is applied to the initial motion state information, effectively eliminating noise introduced by high-frequency vibrations and thus purifying the data before outlier detection. Building upon this, outlier detection is then performed on the filtered data, allowing for more accurate identification of genuine anomalous data and avoiding interference from high-frequency noise in anomaly identification. This significantly improves the accuracy of the initial motion state information quality assessment.

[0128] Through the above technical solution, this embodiment can effectively reduce the interference of high-frequency vibration noise on the quality assessment of initial motion state information, and improve the accuracy and reliability of abnormal data identification. This provides a cleaner and more reliable data foundation for subsequent abnormal data correction and multi-sensor weighted fusion, thereby improving the overall accuracy of low-altitude aircraft target motion state information and the robustness of the system, especially its positioning performance in complex vibration environments.

[0129] In some embodiments, after determining the trust coefficient of the passive physical reference based on geometric consistency, feature matching degree, and perceived influence in step S305, the method may further include, but is not limited to, the following steps:

[0130] Acquire environmental dust concentration, reference object vibration signal, and environmental temperature fluctuation;

[0131] If the ambient dust concentration is greater than the preset dust threshold, the degradation problem of the reference object is determined to be surface dust accumulation.

[0132] If the vibration signal of the reference object is greater than the preset vibration threshold, the degradation problem of the reference object is determined to be due to loose mounting base.

[0133] If the ambient temperature fluctuation exceeds the preset fluctuation threshold, the degradation problem of the reference object is determined to be an abnormal thermal radiation characteristic.

[0134] Based on the degradation issues of the reference object, maintenance suggestions for the reference object are generated.

[0135] In some embodiments, passive physical reference objects may deteriorate due to environmental factors or long-term use, such as surface dust accumulation, loose mounting base, or abnormal thermal radiation characteristics. These deterioration issues may alter the physical characteristics of the reference object, thereby affecting the sensing accuracy of the near-field sensing device. This can result in the determined confidence coefficient failing to accurately reflect the reliability of the reference object, ultimately potentially reducing positioning accuracy or increasing maintenance costs.

[0136] To achieve this, we can first obtain the ambient dust concentration, reference object vibration signal, and ambient temperature fluctuation. Ambient dust concentration refers to the content of suspended particulate matter in the air within the flight space of the low-altitude aircraft, which can be monitored in real time using dust sensors deployed on the aircraft or in the environment. Reference object vibration signal refers to the mechanical vibration generated by a passive physical reference object under operational or environmental influences, which can be collected using vibration sensors installed near the reference object. Ambient temperature fluctuation refers to the range of temperature changes around the reference object, which can be measured using temperature sensors.

[0137] If the ambient dust concentration exceeds the preset dust threshold, the reference object's degradation problem is determined to be surface dust accumulation. If the reference object's vibration signal exceeds the preset vibration threshold, the reference object's degradation problem is determined to be a loose mounting base. If the ambient temperature fluctuation exceeds the preset fluctuation threshold, the reference object's degradation problem is determined to be abnormal thermal radiation characteristics. The preset dust threshold, preset vibration threshold, and preset fluctuation threshold are critical values ​​pre-set based on factors such as the reference object's material properties, installation method, expected service life, and environmental conditions, through experimental testing, experience accumulation, or simulation analysis. When the actual monitored ambient dust concentration, reference object vibration signal, or ambient temperature fluctuation exceeds these preset thresholds, it indicates that the reference object may have a degradation problem. In practical applications, reference object degradation specifically refers to the situation where the physical properties or structural integrity of a passive physical reference object are damaged due to external factors. For example, surface dust refers to excessive dust adhering to the surface of the reference object, which may affect its optical or thermal radiation characteristics; loose mounting base means that the connection between the reference object and the mounting structure is no longer firm, which may cause slight changes in its position or orientation; abnormal thermal radiation characteristics mean that the thermal radiation behavior of the reference object deviates from the normal range at a specific temperature, which may affect the thermal imaging image sensing data.

[0138] Based on the degradation issues of the reference object, maintenance recommendations are then generated. For example, if the problem is identified as surface dust, cleaning is recommended; if the problem is identified as a loose mounting base, tightening or replacement is recommended; if the problem is identified as abnormal thermal radiation characteristics, inspection or calibration is recommended.

[0139] To illustrate this technical solution more clearly, a specific example is used below. Suppose a low-altitude aircraft is performing an inspection mission in an industrial park. The park contains numerous passive physical reference points used for positioning assistance. After the aircraft has been operating for a period of time, the system, after determining the confidence level of the passive physical reference points, further obtains data from a dust sensor near one of the reference points showing an ambient dust concentration of 150 micrograms per cubic meter, while the preset dust threshold is 100 micrograms per cubic meter. Simultaneously, the data from the vibration and temperature sensors are within normal ranges. At this point, based on the condition that the ambient dust concentration exceeds the preset dust threshold, the system automatically determines that the reference point has a "surface dust accumulation" degradation problem. Subsequently, the system generates a maintenance suggestion, such as "Reference point number A001 has severe surface dust accumulation; cleaning and maintenance are recommended." Upon receiving this suggestion, maintenance personnel can specifically clean the reference point, thereby restoring its normal physical characteristics and ensuring that it can provide accurate sensing data in subsequent positioning tasks, maintaining the reliability of high-precision positioning.

[0140] Through the above technical solution, this embodiment effectively solves the problem of decreased positioning accuracy caused by long-term degradation of passive physical reference objects. By real-time monitoring and evaluation of environmental parameters and the reference object's own condition, degradation problems can be detected and diagnosed in a timely manner, and targeted maintenance suggestions can be provided. This not only extends the service life of the reference object and reduces maintenance costs, but also ensures that the passive physical reference object can provide reliable positioning assistance information throughout its entire life cycle, thereby significantly improving the long-term stability, reliability, and accuracy of the multi-sensor fusion high-precision positioning method for low-altitude aircraft.

[0141] The beneficial effects of implementing the embodiments of the present invention include: the embodiments of this application first obtain the environmental parameter information of the low-altitude aircraft, the reference position information and physical feature information of the passive physical reference, and then determine whether the low-altitude aircraft is in an interference area, and obtain the interference area judgment result. If the interference area judgment result is that it is in an interference area, then based on the physical feature information, the current sensing data of the passive physical reference is obtained using a near-field sensing device, and then based on the environmental parameter information, the current sensing data and the reference position information, the aircraft position information of the low-altitude aircraft in the global coordinate system is determined. Thus, the aircraft position information can be determined by combining the passive physical reference and multiple sensing data to achieve aircraft positioning, thereby improving accuracy and reliability.

[0142] like Figure 2 As shown, this embodiment of the invention also provides a high-precision positioning system for low-altitude aircraft using multi-sensor fusion, comprising:

[0143] The information acquisition module 501 is used to acquire environmental parameter information, reference object position information, and physical characteristic information of the low-altitude aircraft;

[0144] The interference area judgment module 502 is used to determine whether the low-altitude aircraft is in the interference area and obtain the interference area judgment result.

[0145] The perception data acquisition module 503 is used to acquire the current perception data of the passive physical reference object based on the physical feature information if the interference area judgment result is that it is in the interference area. The current perception data includes lidar perception data, thermal imaging image perception data and millimeter-wave radar perception data.

[0146] The position information determination module 504 is used to determine the position information of the low-altitude aircraft in the global coordinate system based on environmental parameter information, current sensing data and reference object position information.

[0147] The content of the above method embodiments is applicable to this system embodiment. The specific functions implemented in this system embodiment are the same as those in the above method embodiments, and the beneficial effects achieved are also the same as those achieved in the above method embodiments.

[0148] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

Claims

1. A low-altitude aircraft multi-sensor fusion high-precision positioning method, characterized in that, The method comprises the following steps: obtaining environmental parameter information of a low-altitude aircraft, reference object position information and physical feature information of a passive physical reference object; determining whether the low-altitude aircraft is in an interference region to obtain an interference region determination result; if the interference region determination result is that the low-altitude aircraft is in the interference region, obtaining current sensing data of the passive physical reference object by using a near-field sensing device according to the physical feature information, wherein the current sensing data comprises laser radar sensing data, thermal imaging image sensing data and millimeter wave radar sensing data; determining aircraft position information of the low-altitude aircraft in a global coordinate system according to the environmental parameter information, the current sensing data and the reference object position information.

2. The method of claim 1, wherein, The step of determining whether the low-altitude aircraft is in the interference region to obtain the interference region determination result comprises: obtaining environmental geometric features, wherein the environmental geometric features comprise relative positions of metal planes and edges and relative directions of the metal planes and the edges; identifying a first plane and a second plane that are parallel to each other according to the environmental geometric features; calculating a first distance between the low-altitude aircraft and the first plane; calculating a second distance between the low-altitude aircraft and the second plane; calculating a third distance between the first plane and the second plane; if the third distance is less than a preset plane distance and the first distance and the second distance are both less than a preset flight distance, obtaining a metal plane signal reflection intensity and a plane parallelism degree; matching the metal plane signal reflection intensity, the plane parallelism degree and a metal channel structure model to calculate a confidence degree; if the confidence degree is greater than a confidence degree threshold, determining that the interference region determination result is that the low-altitude aircraft is in the interference region.

3. The method of claim 2, wherein, The step of obtaining the environmental geometric features comprises: scanning a flight space by using a laser radar to obtain three-dimensional point cloud data; down-sampling the three-dimensional point cloud data; detecting planes and edges in the down-sampled three-dimensional point cloud data by using a random sample consensus algorithm to obtain metal structure data; extracting the environmental geometric features from the metal structure data.

4. The method of claim 1, wherein, The step of determining the aircraft position information of the low-altitude aircraft in the global coordinate system according to the environmental parameter information, the current sensing data and the reference object position information comprises: obtaining target motion state information of the low-altitude aircraft; performing multi-path consistency evaluation on the current sensing data according to the reference object position information to obtain geometric consistency of the passive physical reference object under different flight paths; matching the physical feature information and the current sensing data to evaluate a feature matching degree of the passive physical reference object; evaluating a sensing influence degree of the environmental parameter information on the near-field sensing device; determining a trust coefficient of the passive physical reference object according to the geometric consistency, the feature matching degree and the sensing influence degree; determining the aircraft position information according to the target motion state information and the trust coefficient.

5. The method of claim 4, wherein, The step of determining the aircraft position information according to the target motion state information and the trust coefficient comprises: obtaining historical sensing data; The historical perception data and the current perception data are compared to identify a target perception data interference degree, the target perception data interference degree including a laser radar perception data interference degree, a thermal imaging image perception data interference degree, and a millimeter wave radar perception data interference degree; The trust coefficient is adjusted according to the target perception data interference degree; The aircraft position information is determined according to the target motion state information and the adjusted trust coefficient.

6. The method of claim 4, wherein, The aircraft position information is determined according to the target motion state information and the trust coefficient, including: The physical feature information is used to classify a plurality of passive physical reference objects to obtain a plurality of reference object groups; The spatial distribution relationship of the plurality of reference object groups in a global coordinate system is used to sort the contribution degrees of the plurality of passive physical reference objects to obtain a contribution degree sorting result; The trust coefficient corresponding to each passive physical reference object is adjusted according to the contribution degree sorting result; The aircraft position information is determined according to the target motion state information and the plurality of adjusted trust coefficients.

7. The method of claim 4, wherein, The target motion state information of the low-altitude aircraft is obtained, including: Initial motion state information is obtained, including inertial measurement unit data, visual odometry data, and laser radar odometry data; The initial motion state information is quality evaluated to identify abnormal data; The abnormal data is corrected to update the initial motion state information; The updated inertial measurement unit data, visual odometry data, and laser radar odometry data are weighted and fused according to the environmental interference intensity to obtain the target motion state information.

8. The method of claim 7, wherein, The initial motion state information is quality evaluated to identify abnormal data, including: Local vibration frequency information of an area where the low-altitude aircraft is located is obtained; The initial motion state information is filtered according to the local vibration frequency information to eliminate noise introduced by high-frequency vibration; Outlier detection is performed on the filtered initial motion state information to identify the abnormal data.

9. The method of claim 4, wherein, After the trust coefficient of the passive physical reference object is determined according to the geometric consistency, the feature matching degree, and the perception influence degree, the method further includes: Environmental dust concentration, reference object vibration signals, and environmental temperature fluctuations are obtained; If the environmental dust concentration is greater than a preset dust threshold, it is determined that the reference object degradation problem is surface area dust; If the reference object vibration signal is greater than a preset vibration threshold, it is determined that the reference object degradation problem is loose installation base; If the environmental temperature fluctuation is greater than a preset fluctuation threshold, it is determined that the reference object degradation problem is abnormal thermal radiation characteristics; Reference object maintenance recommendations are generated according to the reference object degradation problem.

10. A low altitude vehicle multi-sensor fusion high precision positioning system, characterized in that, Including: An information acquisition module is configured to acquire environmental parameter information of a low-altitude aircraft, reference object position information, and physical feature information of a passive physical reference object; An interference area judgment module is configured to determine whether the low-altitude aircraft is in an interference area to obtain an interference area judgment result; The perception data acquisition module is configured to, if the interference region determination result is that the low-altitude aircraft is in the interference region, acquire current perception data of the passive physical reference object by using a near-field perception device according to the physical feature information, wherein the current perception data includes laser radar perception data, thermal imaging image perception data and millimeter wave radar perception data. The position information determination module is configured to determine aircraft position information of the low-altitude aircraft in a global coordinate system according to the environment parameter information, the current perception data and the reference object position information.

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