CN-122017858-A - Unmanned aerial vehicle high-precision positioning method and system

CN122017858ACN 122017858 ACN122017858 ACN 122017858ACN-122017858-A

Abstract

The invention relates to the technical field of unmanned aerial vehicle positioning, and provides a high-precision unmanned aerial vehicle positioning method and system. The unmanned aerial vehicle navigation control method comprises the steps of monitoring the perceived data quality information of a visual perception unit and a laser perception unit carried by the unmanned aerial vehicle, judging that the unmanned aerial vehicle is in an industrial plume interference state, activating a UWB ranging unit, obtaining ranging information between the unmanned aerial vehicle and a plurality of preset UWB anchor points, further determining UWB positioning information of the unmanned aerial vehicle, generating position information of the unmanned aerial vehicle according to the UWB positioning information, the perceived data of the visual perception unit and the laser perception unit, and performing navigation control on the unmanned aerial vehicle according to the position information. Therefore, the unmanned aerial vehicle can still obtain stable and reliable high-precision positioning under severe environment, and the risks of out-of-control flight attitude, deviation from a route and even collision to equipment caused by positioning misalignment are avoided.

Inventors

LIN ZHIYUN

Assignees

东晟智云(北京)科技有限公司

Dates

Publication Date: 20260512
Application Date: 20260119

Claims (10)

1. The unmanned aerial vehicle high-precision positioning method is characterized in that the unmanned aerial vehicle is provided with a visual perception unit, a laser perception unit and a UWB ranging unit, and comprises the following steps: The method comprises the steps of monitoring perceived data quality information of a visual perception unit and a laser perception unit carried by an unmanned aerial vehicle, judging that the unmanned aerial vehicle is in an industrial plume interference state according to the perceived data quality information, and generating an interference confirmation signal; activating a UWB ranging unit according to the interference confirmation signal, and acquiring ranging information between the unmanned aerial vehicle and a plurality of preset UWB anchor points; Determining UWB positioning information of the unmanned aerial vehicle according to the ranging information and position information of a preset UWB anchor point; According to the UWB positioning information, the perception data of the visual perception unit and the laser perception unit, the position information of the unmanned aerial vehicle is generated in a fusion mode; and performing navigation control on the unmanned aerial vehicle according to the position information.
2. The unmanned aerial vehicle high-precision positioning method according to claim 1, wherein the visual perception unit comprises a visual inertial odometer VIO, and the laser perception unit comprises a laser radar LiDAR; The monitoring of the perceived data quality information of the visual perception unit and the laser perception unit carried by the unmanned aerial vehicle comprises the following steps: Determining the quantity of identifiable characteristic points in the visual image, the tracking stability of the identifiable characteristic points among continuous image frames and the local contrast of the visual image according to the visual image acquired by the camera in real time, and simultaneously acquiring the point cloud density of the laser sensing unit, the matching residual error of the point cloud and a pre-built map and the false echo proportion; the step of judging that the unmanned aerial vehicle is in an industrial plume interference state according to the perceived data quality information comprises the following steps: and if the number of the identifiable characteristic points, the tracking stability, the local contrast and the point cloud density are all lower than the corresponding threshold values, and the matching residual errors and the false echo proportions are all higher than the corresponding threshold values, judging that the unmanned aerial vehicle is in an industrial plume interference state.
3. The unmanned aerial vehicle high-precision positioning method of claim 2, wherein the tracking stability of the identifiable feature points between successive image frames is determined according to the following manner: Calculating the average pixel displacement of the tracked characteristic points between adjacent image frames; Calculating a standard deviation and/or variance of the average pixel displacement to quantify the jitter degree thereof; And when the average pixel displacement exceeds a preset displacement threshold value and/or the standard deviation or variance exceeds a preset jitter threshold value, judging that the tracking stability of the feature point is lower than a preset stability threshold value.
4. The method of claim 3, wherein the fusing generates positional information of the unmanned aerial vehicle, the generating being performed based on an extended kalman filter; according to the UWB positioning information, the sensing data of the visual sensing unit and the laser sensing unit, generating the position information of the unmanned aerial vehicle in a fusion manner, including: in the extended kalman filter, the UWB positioning information is used as a position observation value, the output information of the visual inertial odometer VIO is used as a visual observation value, the matching result of the laser radar LiDAR and a pre-built map is used as a point cloud matching observation value, and the data of an inertial measurement unit IMU is used for state prediction; In response to the interference confirmation signal, dynamically increasing measurement noise covariance corresponding to the visual observation value and the point cloud matching observation value in the extended Kalman filter; Setting the measurement noise covariance corresponding to the UWB positioning information to be lower than the measurement noise covariance of the increased visual observation value and the point cloud matching observation value; Based on the adjusted covariance of each measurement noise, calculating Kalman gain, updating the state vector of the unmanned aerial vehicle by using the UWB positioning information, and outputting the position information of the unmanned aerial vehicle.
5. The method according to claim 4, wherein calculating a kalman gain based on the adjusted measured noise covariance, updating a state vector of the unmanned aerial vehicle with the UWB positioning information, and outputting position information of the unmanned aerial vehicle, comprises: constructing a measurement matrix according to the corresponding relation between the UWB positioning information and the position state in the state vector; Calculating Kalman gain based on the measurement matrix, the prediction covariance matrix of the state vector and the measurement noise covariance matrix corresponding to the UWB positioning information; correcting the state vector according to the difference between the Kalman gain and the UWB positioning information in-state predicted value to finish measurement updating; and extracting position state information from the updated state vector, and outputting the position state information as the position information of the unmanned aerial vehicle.
6. The method of high-precision positioning of a drone of claim 4, wherein the determining UWB positioning information for the drone is followed by the method further comprising: Judging whether the UWB ranging unit has systematic deviation or not, if so, executing the correction operation of the UWB positioning information, wherein the judging whether the UWB ranging unit has systematic deviation or not comprises respectively calculating position estimation values of the unmanned aerial vehicle according to a preset plurality of UWB anchor point combinations, calculating differences among the position estimation values, and if the differences exceed a preset difference threshold, judging that the UWB ranging unit has systematic deviation.
7. The unmanned aerial vehicle high-precision positioning method of claim 6, wherein the performing the correction operation of the UWB positioning information comprises: Based on the angular velocity and the linear acceleration acquired by the inertial measurement unit IMU, predicting the relative displacement of the unmanned aerial vehicle in a preset time window to obtain inertial predicted displacement; acquiring the position change measured by the UWB ranging unit in the preset time window as UWB measurement displacement; Comparing the inertial prediction displacement with the UWB measurement displacement, and if the difference between the inertial prediction displacement and the UWB measurement displacement continuously exceeds a preset error threshold value in a plurality of continuous time windows, confirming that the UWB ranging unit has systematic deviation; And carrying out self-adaptive correction on the ranging information based on the systematic deviation, thereby obtaining corrected UWB positioning information.
8. The unmanned aerial vehicle high-precision positioning method of claim 7, wherein the adaptively correcting the ranging information based on the systematic deviation comprises: Modeling the systematic bias as a time-varying bias vector; According to the extended Kalman filter, the deviation vector is used as an extended component of a state variable to carry out real-time estimation, and an estimated deviation vector is obtained; And compensating the ranging information at the current moment according to the estimated deviation vector to obtain corrected ranging information.
9. The method for positioning the unmanned aerial vehicle with high precision according to any one of claims 1 to 8, wherein the determining the UWB positioning information of the unmanned aerial vehicle according to the ranging information and the position information of the preset UWB anchor point includes: Calculating ranging information from at least three non-collinear preset UWB anchor points by adopting a trilateral positioning method or a least square method to obtain preliminary UWB positioning coordinates of the unmanned aerial vehicle; and filtering and smoothing the preliminary UWB positioning coordinates to obtain UWB positioning information of the unmanned aerial vehicle.
10. The unmanned aerial vehicle high-precision positioning system is characterized in that the unmanned aerial vehicle is provided with a visual perception unit, a laser perception unit and a UWB ranging unit, and the system comprises: the monitoring module is used for monitoring the perceived data quality information of the visual perception unit and the laser perception unit carried by the unmanned aerial vehicle, judging that the unmanned aerial vehicle is in an industrial plume interference state according to the perceived data quality information, and generating an interference confirmation signal; the ranging module is used for activating the UWB ranging unit according to the interference confirmation signal and acquiring ranging information between the unmanned aerial vehicle and a plurality of preset UWB anchor points; the positioning module is used for determining UWB positioning information of the unmanned aerial vehicle according to the ranging information and position information of a preset UWB anchor point; And the fusion and control module is used for generating the position information of the unmanned aerial vehicle in a fusion way according to the UWB positioning information, the perception data of the visual perception unit and the laser perception unit, and performing navigation control on the unmanned aerial vehicle according to the position information.

Description

Unmanned aerial vehicle high-precision positioning method and system Technical Field The application relates to the technical field of unmanned aerial vehicle positioning, in particular to a high-precision unmanned aerial vehicle positioning method and system. Background In modern industrial production, unmanned aerial vehicle automatic inspection has become an important trend of large-scale chemical plant equipment inspection. The core task of the system is to accurately detect and locate faults of pipelines, valves, storage tanks and the like in a complex factory. In the open area, the unmanned plane can realize centimeter-level positioning through satellite navigation, however, in the factory area, high-rise equipment and dense pipelines often shield satellite signals and cause multipath effect, so that the positioning accuracy is seriously reduced. To address this challenge, drones are often equipped with multi-source information fusion positioning systems. When satellite signals are limited, the system automatically switches to a vision inertial odometer and laser radar fusion scheme. The VIO estimates the motion of the VIO through the camera and the inertial measurement unit, and the laser radar constructs a real-time point cloud through scanning and matches with a preset high-precision three-dimensional map, so that the position is corrected, and drift is restrained. In practice, however, when the unmanned aerial vehicle approaches a large cooling tower, the periodically discharged dense water vapor forms a white plume with serious optical interference. The laser beam is scattered and absorbed in a large amount when penetrating the steam, so that the real echo signal is weak, and meanwhile, the radar receives a large amount of false echoes from water drops, so that noise-filled ghost point clouds are generated. This causes the accuracy of matching the real-time point cloud with the preset map to drop drastically or even fail. At the same time, the steam also seriously interferes with a vision system, the visibility is reduced, the image blurring is caused, the feature point extraction is difficult, the motion estimation error of the VIO is rapidly accumulated, and the drift problem is remarkable. Under the condition that the laser radar and the vision system are simultaneously interfered, the fusion positioning system is difficult to distinguish real environment characteristics from false data introduced by steam. The system can give out completely deviated global correction based on the error point cloud, so that the unmanned aerial vehicle can be positioned to jump severely, deviate from the route, even cause collision or crash, and seriously threaten the inspection task and equipment safety. In view of the above, there is a need in the art for improvements. Disclosure of Invention The application discloses a high-precision positioning method and a high-precision positioning system for an unmanned aerial vehicle, and aims to solve the problems that in an industrial plume interference environment, the positioning precision of an unmanned aerial vehicle multi-source information fusion positioning system is rapidly reduced or even fails due to sensor data quality reduction and false information introduction. The technical scheme of the application is as follows: in a first aspect, the application discloses a high-precision positioning method of an unmanned aerial vehicle, wherein the unmanned aerial vehicle is provided with a visual perception unit, a laser perception unit and a UWB ranging unit, and the method comprises the following steps: The method comprises the steps of monitoring the perceived data quality information of a visual perception unit and a laser perception unit carried by an unmanned aerial vehicle, judging that the unmanned aerial vehicle is in an industrial plume interference state according to the perceived data quality information, and generating an interference confirmation signal; Activating a UWB ranging unit according to the interference confirmation signal, and acquiring ranging information between the unmanned aerial vehicle and a plurality of preset UWB anchor points; Determining UWB positioning information of the unmanned aerial vehicle according to the ranging information and position information of a preset UWB anchor point; According to the UWB positioning information, the perception data of the visual perception unit and the laser perception unit, position information of the unmanned aerial vehicle is generated in a fusion mode; and performing navigation control on the unmanned aerial vehicle according to the position information. Further, in the unmanned aerial vehicle high-precision positioning method, the visual perception unit comprises a visual inertial odometer VIO, and the laser perception unit comprises a laser radar LiDAR; The method comprises the steps of determining the number of identifiable characteristic points in a visual image, the tracking stability o