CN117572879A - Unmanned aerial vehicle based on laser radar SLAM positioning navigation - Google Patents

Abstract

The invention discloses a UAV based on lidar SLAM positioning and navigation, which includes: a UAV body, a multi-line three-dimensional lidar unit installed directly in front of the UAV body, an IMU inertial sensor, a high-performance data processing unit and Flight control system; the high-performance data processing unit performs calculations based on the point cloud data detected by lidar to generate the current relative position of the drone. The high-performance data processing unit performs real-time calibration and correction on the pose information of the IMU, and fuses the pose and motion information of the IMU and lidar based on the comparison results, and combines it with the currently updated global map model to obtain the fusion position of the current frame. Posture and motion information. The UAV provided by the present invention can fly indoors or under any conditions without satellite GPS signal reception. It is safe and reliable, is not easily interfered by wireless signals, and can fly autonomously without any communication connection.

Description

UAV based on lidar SLAM positioning and navigation

Technical field

The invention belongs to the technical field of drones, and specifically relates to a drone based on laser radar SLAM positioning and navigation.

Background technique

Unmanned aerial vehicles, referred to as drones, first appeared in the early 20th century and then gradually developed due to military needs. UAVs use radio remote control equipment and self-contained program control devices to control unmanned aircraft, or are fully or intermittently operated autonomously by on-board computers. UAVs are widely used in many fields such as post-disaster search and rescue, aerial photography, crop monitoring, and military operations due to their flexible maneuverability, small constraints on ground terrain, low cost, and no casualties. Different fields have a great impact on UAVs. Humans and machines have different requirements, such as requiring UAVs to perform complex tasks such as positioning, obstacle avoidance, tracking and trajectory planning.

In recent years, quad-rotor drones have the advantages of low cost and easy operation, and have become an important research direction in the drone industry. As a kind of flying robot, the application of quadcopter drones increasingly requires drones to have high-precision, intelligent, and autonomous flight capabilities. The basis for these capabilities is that the drone has accurate state estimation and environmental The ability to perceive. At present, various types of aircraft generally use GPS/RTK to achieve positioning. However, in some cases, such as in indoor environments or some crowded areas, the accuracy of GPS/RTK deteriorates or even fails, which is not enough to support autonomous flight of drones. Fixed-point hovering of UAVs using satellite navigation technology as a navigation method has errors that cannot be ignored, and ultimately it is difficult to achieve stable hovering in a fixed area. However, if a binocular vision system is installed on a drone, the drone will no longer need to transmit external signals to achieve autonomous control.

Because most of the current drone flights rely on GPS/RTK signals, and in indoor environments, GPS/RTK signals cannot be obtained. Therefore, the research drone can still perform obstacle avoidance flight when the GPS/RTK signal is lost. This is the main difficulty and a major challenge to drone flight safety. Therefore, the research and development of autonomous flight of multi-rotor UAV flight systems is of great significance.

Contents of the invention

Embodiments of the present invention provide a UAV based on lidar SLAM positioning and navigation, which can achieve the effect that the UAV can still perform obstacle avoidance flight without relying on GPS/RTK signals.

Embodiments of the present invention provide a UAV based on the positioning and navigation of the lidar SLAM system, including: a UAV body, a multi-line three-dimensional lidar unit installed directly in front of the UAV body, an IMU inertial sensor, and high-performance data processing Unit and flight control system; the high-performance data processing unit performs calculations based on the point cloud data detected by lidar, generates the current relative position of the UAV, and transmits the current relative position information of the UAV to the UAV flight control System, the flight control system generates flight routes in real time based on the position information of the UAV, and performs positioning and navigation of the UAV's flight; among them,

The high-performance data processing unit also handles the following functions:

Solve the point cloud data to obtain the lidar pose and motion information of the current frame and the local map information of the surrounding environment of the current frame; update the map in the lidar SLAM system based on the local map information, and fuse all local maps to generate 3D global map;

Generate a first motion trajectory by combining the lidar pose and motion information of the previous frame with the lidar pose and motion information of the current frame;

Combine the IMU pose and motion information of the previous frame with the IMU pose and motion information of the current frame to generate a second motion trajectory; compare the first motion trajectory and the second motion trajectory to obtain a comparison error, and compare the comparison error with the system's preset Compare laser SLAM calibration errors;

Based on the comparison results of the comparison error and the laser SLAM calibration error, the pose and motion information of the IMU and lidar are fused, and combined with the currently updated global map model, the fused pose and motion information of the current frame is obtained.

Preferably, the high-performance data processing unit also performs filtering and preprocessing on the collected point cloud data; and updates the temporary local map information based on the preprocessed point cloud data.

Preferably, the high-performance data processing unit compares the comparison error with the laser SLAM calibration error and also includes the following processing:

When the comparison error is greater than the laser SLAM calibration error, the IMU pose and motion information of the current frame are modified to the lidar pose and motion information of the current frame;

When the comparison error is less than the laser SLAM calibration error, the IMU pose and motion information of the current frame are stored.

Preferably, the embodiment of the present application also includes a GPS device and/or an RTK device. When detecting the presence of a GPS signal and/or an RTK signal, the high-performance data processing unit combines the fused pose and motion information of the current frame with the GPS signal. and/or RTK signals to obtain fused pose and motion information with the earth coordinate system.

Preferably, the embodiment of the present application also includes a route generation unit, which obtains the location information specified by the user, and generates the optimal route for flight control based on the location information specified by the user, map update information, fused pose and motion information. The unit controls the positioning and navigation of the drone.

Preferably, the three-dimensional lidar unit, IMU inertial sensor, and high-performance data processing unit are integrated into one frame.

The UAV provided by the present invention integrates the inertial navigation data of the IMU and uses the direction information measured by the inertial navigation as the input information of the SLAM system to avoid the problem of being unable to obtain the current movement direction when there is no GPS signal and improve the reliability of the system's autonomous positioning. sex. At the same time, the invention is based on multi-line three-dimensional laser scanning data and uses the SLAM data system to construct and update a three-dimensional map in real time for use in UAV navigation. The beneficial effects of the present invention are as follows:

1. It can be flown indoors or under any conditions without satellite GPS signal reception. It is safe and reliable and will not be easily interfered by wireless signals. It can fly autonomously without any communication connection.

2. Achieve high-reliability hovering and autonomous flight of the UAV, enable rapid route planning, and autonomously fly to the destination.

3. The system's lidar is an autonomous light-emitting sensor that can fly in a completely dark environment.

Description of the drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings in the following description are: For some embodiments of the present invention, those of ordinary skill in the art can also obtain other drawings based on these drawings without exerting creative efforts.

Figure 1 shows a UAV based on lidar SLAM positioning and navigation in an embodiment of the present invention.

Figure 2 is a module diagram of an unmanned aerial vehicle system based on lidar SLAM positioning and navigation in an embodiment of the present invention.

Figure 3 is a general flow chart of the laser radar SLAM positioning and navigation method in the embodiment of the present invention.

Detailed ways

In order to make the purpose, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present application. Obviously, the described embodiments are only These are part of the embodiments of this application, not all of them. The components of the embodiments of the present application generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Therefore, the following detailed description of the embodiments of the application provided in the appended drawings is not intended to limit the scope of the claimed application, but rather to represent selected embodiments of the application. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without any creative work shall fall within the scope of protection of this application.

Please refer to Figure 1. An embodiment of the present invention provides a UAV based on the positioning and navigation of a lidar SLAM system, including: a UAV body 1, and a multi-line three-dimensional lidar unit 2 installed directly in front of the UAV body. , IMU inertial sensor, high-performance data processing unit 3 and flight control system; the high-performance data processing unit 3 calculates based on the point cloud data detected by lidar, generates the current relative position of the drone, and sets the current relative position of the drone The relative position information is transmitted to the UAV flight control system, and the flight control system generates a flight route in real time based on the UAV's position information, and performs positioning and navigation on the UAV's flight; where,

The high-performance data processing unit 3 also handles the following functions:

Solve the point cloud data to obtain the lidar pose and motion information of the current frame and the local map information of the surrounding environment of the current frame; update the map in the lidar SLAM system based on the local map information, and fuse all local maps to generate 3D global map;

Generate a first motion trajectory by combining the lidar pose and motion information of the previous frame with the lidar pose and motion information of the current frame;

Combine the IMU pose and motion information of the previous frame with the IMU pose and motion information of the current frame to generate a second motion trajectory; compare the first motion trajectory and the second motion trajectory to obtain a comparison error, and compare the comparison error with the system's preset Compare laser SLAM calibration errors;

Based on the comparison results of the comparison error and the laser SLAM calibration error, the pose and motion information of the IMU and lidar are fused, and combined with the currently updated global map model, the fused pose and motion information of the current frame is obtained.

Further, in the embodiment of the present invention, the high-performance data processing unit 3 also performs filtering and preprocessing on the collected point cloud data; and updates the temporary local map information according to the preprocessed point cloud data.

Further, in the embodiment of the present invention, the high-performance data processing unit 3 compares the comparison error with the laser SLAM calibration error and also includes the following processing:

When the comparison error is greater than the laser SLAM calibration error, the IMU pose and motion information of the current frame are modified to the lidar pose and motion information of the current frame;

When the comparison error is less than the laser SLAM calibration error, the IMU pose and motion information of the current frame are stored.

Furthermore, the embodiment of the present invention also includes a GPS device and/or an RTK device. When detecting the presence of a GPS signal and/or an RTK signal, the high-performance data processing unit combines the fused pose and motion information of the current frame with the GPS signal. and/or RTK signals to obtain fused pose and motion information with the earth coordinate system.

Further, the embodiment of the present invention also includes a route generation unit, which obtains the location information specified by the user, and generates the optimal route for flight control based on the location information specified by the user, map update information, fused pose and motion information. The unit controls the positioning and navigation of the drone.

Further, in the embodiment of the present invention, the three-dimensional lidar unit, IMU inertial sensor, and high-performance data processing unit are integrated into one frame.

The UAV provided in this embodiment is equipped with a multi-line three-dimensional lidar system directly in front. Under the control of the internal scanning system, the radar quickly scans the front within a certain viewing angle range and generates dense point cloud data. These point cloud data reflect The precise position information of all obstacles within a range of tens of meters around the aircraft is obtained. The generated point cloud data is then transmitted to a high-performance data processing unit in real time. The processing unit uses these data to calculate and generate flight routes to achieve autonomous obstacle avoidance for the drone. Flying; the high-performance data processing unit installed in the drone has built-in high-speed CPU and GPU, which can preprocess the point cloud scanned by multi-line lidar in real time, and then send it to the SLAM computing unit for position calculation and Three-dimensional map model construction;

This invention can be based on the map model scanned by three-dimensional lidar. As long as the user points out the target location that the drone needs to reach, the drone can automatically and quickly generate the flight route of the drone. The route is an accurate map scanned based on the surrounding environment. The route generated on this basis is very safe, and the algorithm can ensure the most efficient flight route.

Compared with the existing technology, the embodiments of the present invention continuously improve the real-time pose information of the drone through continuously optimized IMU pose data in real time, and improve the positioning information of the drone and the information for constructing the map, so that the present invention The drones provided have the advantages of high precision positioning, independent navigation and obstacle avoidance without relying on GPS, simple operation, and high safety factor.

In order to further illustrate the solutions implemented by the present invention, this application further provides specific and detailed examples. The specific and detailed examples are described as follows.

On the other hand, embodiments of the present invention provide an unmanned aerial vehicle system based on lidar SLAM positioning and navigation. Figure 2 is a module diagram of an unmanned aerial vehicle system based on lidar SLAM positioning and navigation in an embodiment of the invention, including a three-dimensional laser SLAM system. 100 and a flight control unit 200, wherein the three-dimensional laser SLAM system 100 provides UAV positioning and navigation information, and the flight control unit 200 controls the UAV positioning and navigation flight according to the positioning and navigation information of the three-dimensional laser SLAM system. ;

The three-dimensional laser SLAM system 100 also includes a laser information collection unit 110 and a laser information processing unit 120, wherein,

The laser information collection unit 110 is used to collect information for the three-dimensional laser SLAM system 100, including an IMU unit 111 and at least one multi-line three-dimensional lidar module 112, wherein,

The IMU unit 111 is used to provide predicted pose and motion information for the three-dimensional laser SLAM system 100;

The lidar 112 is used to collect point cloud data of the surrounding environment;

The laser information processing unit 120 is used to process the information collected by the laser information collection unit, including:

Point cloud processing module 130, used to filter and preprocess point cloud data;

The SLAM data processing module 140 is used to solve the filtered and preprocessed data and output motion pose information and three-dimensional map model;

and a multi-sensor fusion module 180, used to fuse the motion pose information obtained by the SLAM data processing module with the three-dimensional map model information to generate the current pose and motion information.

In the embodiment of the present invention, the SLAM data processing module 140 also includes a radar calculation module 141, a trajectory generation module 142, a trajectory comparison module 143, a calibration module 144, and a lidar fusion module 145, where,

The radar calculation module 141 is used to calculate the filtered point cloud data to obtain the corresponding laser radar pose and motion information and the local map information of the surrounding environment of the current frame;

The trajectory generation module 142 is used to generate the first movement trajectory of the drone by combining the LiDAR pose and motion information of two consecutive frames, and generate the second motion trajectory of the drone by combining the IMU pose and motion information of two consecutive frames;

The trajectory comparison module 143 is used to compare the first movement trajectory and the second movement trajectory, obtain a comparison error, and compare the obtained comparison error with a preset laser SLAM calibration error;

The lidar fusion module 145 fuses the pose and motion information output by the IMU unit 111 and the lidar according to the comparison result of the trajectory comparison module; and outputs the stable pose information and motion information of the current UAV.

In this embodiment of the present invention, the SLAM data processing module also includes:

The map update module 160 is used to update the map information of the three-dimensional laser SLAM system 100 in real time according to the local map information of the surrounding environment of the current frame; and

The global map fusion module 170 is used to fuse all local map information to generate the global three-dimensional map model information, so that the UAV can plan a navigation path in the global map information.

In the embodiment of the present invention, the UAV system based on lidar SLAM positioning and navigation also includes a calibration module 144, which is used to correct the error of the IMU unit 111 in real time according to the comparison result of the trajectory comparison module.

In the embodiment of the present invention, when there are GPS and/or RTK signals, the multi-sensor fusion module 180 will also combine the pose information and motion information output by the lidar fusion module, the GPS and/or RTK position information, and the currently updated map. The information is fused to obtain fused pose and motion information with earth coordinates.

Preferably, the provision of predicted pose and motion information for the three-dimensional laser SLAM system 100 also includes the IMU unit 111 first collecting mileage information, and converting the mileage information into the UAV pose through the UAV inertial odometry kinematics model. The change information is sent to the Bayesian filter to initially calculate the predicted pose and motion information. The filtering process includes denoising the point cloud data, removing abnormal points, and reducing redundant point cloud data processing.

In this embodiment of the present invention, the point cloud processing module 130 also includes a filtering module 131 and a preprocessing module 132, where,

The filtering module 131 is used to filter the point cloud data collected by the lidar 112;

The preprocessing module 132 is used to perform preliminary processing on the filtered point cloud data to obtain temporary local map information.

In the embodiment of the present invention, the flight control unit 200 also includes a global planning module 210, a local planning module 220 and a bottom-level control module 230, wherein,

The global planning module 210 is used to plan the optimal navigation path of the global map;

The local planning module 220 is used to plan the global optimal path for the real-time local map information obtained after preprocessing;

The underlying control module 230 is used to control and allocate drones.

On the other hand, please refer to Figure 3. An embodiment of the present invention provides a method for positioning and navigating a drone based on lidar SLAM, which includes the following steps:

(1) Obtain the IMU pose and motion information of the current frame collected by the IMU unit, and obtain the point cloud data information of the current frame collected by the three-dimensional lidar;

(2) Filter and preprocess the collected point cloud data information;

(3) Solve the filtered point cloud data to obtain the lidar pose and motion information of the current frame and the local map information of the surrounding environment of the current frame in the laser SLAM system;

(4) Update the map in the laser SLAM system based on local map information, and fuse all local maps to generate a three-dimensional global map;

(5) Combine the lidar pose and motion information of the previous frame with the lidar pose and motion information of the current frame to generate the first motion trajectory;

(6) Generate a second motion trajectory by combining the IMU pose and motion information of the previous frame with the IMU pose and motion information of the current frame;

(7) Compare the first movement trajectory and the second movement trajectory to obtain a comparison error, and compare the comparison error with the system's preset laser SLAM calibration error;

(8) Based on the comparison results of the comparison error and the laser SLAM calibration error, the pose and motion information of the IMU and lidar are fused, and combined with the currently updated global map model, the fused pose and motion information of the current frame is obtained;

(9) Obtain the location information specified by the user, and generate the best route based on the location information specified by the user, map update information, fused pose and motion information, and display and control the positioning and navigation of the drone.

In the embodiment of the present invention, filtering and preprocessing the collected point cloud data in step 2 also includes the following steps:

(2-1) Filter the collected point cloud data;

(2-2), preprocess the filtered point cloud data;

(2-3). Update the temporary local map based on the preprocessed point cloud data.

In the embodiment of the present invention, comparing the comparison error with the laser SLAM calibration error in step 7 also includes the following steps:

(7-1). If the comparison error is greater than the laser SLAM calibration error, replace the IMU pose and motion information of the current frame with the lidar pose and motion information of the current frame;

(7-2). If the comparison error is smaller than the laser SLAM calibration error, the IMU pose and motion information of the current frame are stored.

In the embodiment of the present invention, the step 8 also includes: when there is a GPS signal and/or RTK signal, combining the fused pose and motion information of the current frame with the GPS signal and/or RTK signal to obtain an image with the earth coordinate system. Fusion of pose and motion information.

The overall operation of a UAV system based on lidar SLAM positioning and navigation in the embodiment of the present invention is as follows:

The IMU unit 111 acquires the mileage information of the three-dimensional laser SLAM system 100, converts the mileage information into UAV pose change information through the UAV inertial odometry kinematics model, and sends it to the Bayesian filter to initially calculate the current frame. The IMU pose and motion information is output to the trajectory generation module 142. The multi-line three-dimensional laser scanning radar collects the point cloud data information of the current frame and outputs it to the filtering module 131;

The filtering module 131 performs filtering processes such as denoising, removing abnormal points, and reducing the amount of redundant point cloud data on the collected point cloud data, and outputs the filtered point cloud data to the radar calculation module 141 and the preprocessing module 132 respectively. ;

The preprocessing module 132 performs preliminary processing on the filtered point cloud data to obtain temporary local map information and outputs it to the local planning module 220;

The radar calculation module 141 calculates the filtered point cloud data to obtain the lidar pose and motion information of the current frame and the local map information of the surrounding environment of the current frame in the laser system, and converts the lidar pose and motion information of the current frame into and the motion information is output to the trajectory generation module 142, and the local map information of the surrounding environment of the current frame in the laser system is output to the map update module 160;

The map update module 160 updates the local map information of two consecutive frames in real time and outputs it to the global map fusion module 170;

The global map fusion module 170 fuses all local map information to generate global map information. When the drone takes off at the same location next time, the global map information can be directly used to output the global map information to the lidar fusion module 145 and the global map information. planning module 210;

The trajectory generation module 142 combines the lidar pose and motion information of the previous frame with the lidar pose and motion information of the current frame to generate a first motion trajectory, and combines the IMU pose and motion information of the previous frame with the IMU position of the current frame. Generate a second motion trajectory from the posture and motion information, and output the first motion trajectory and the second motion trajectory to the trajectory comparison module 143;

The trajectory comparison module 143 compares the first movement trajectory and the second movement trajectory to obtain a first comparison error, compares the first comparison error with the preset laser SLAM calibration error, and outputs the comparison result to the calibration module 144;

If the first comparison error is greater than the laser SLAM calibration error, then the calibration module 144 replaces the IMU pose and motion information of the current frame with the lidar pose and motion information of the current frame. If the first comparison error is less than the laser SLAM calibration error, then Store the IMU pose and motion information of the current frame;

The pose and motion information of the IMU and lidar are output to the lidar fusion module 145. The lidar fusion module 145 fuses the pose and motion information of the IMU and lidar according to the fusion algorithm to obtain the fused pose and motion information of the current frame, The fused pose and motion information of the current frame are output to the multi-sensor fusion module 180. The fused pose and motion information received by the multi-sensor fusion module 180 is fused with the updated global three-dimensional map information. When there is an external GPS and/or RTK When receiving the signal, the GPS and/or RTK information are simultaneously fused to obtain the fused pose and motion information with the earth coordinate system, and output to the global planning module 210;

The global planning module 210 plans the best navigation path for the UAV to reach the target point based on the user-specified location, map update information, and fused pose and motion information, that is, the navigation path with the shortest route and no obstacles during travel, and all data Output to local planning module 220;

The local planning module 220 plans the local best navigation path based on the real-time local map information sent by the preprocessing module in the laser information processing unit and the data output by the global planning module 210, and compares it with the best navigation path to obtain the optimal path. The optimal path is output to the bottom control module 230;

The underlying control module 230 controls and allocates the drone according to the optimal path, and controls the flight speed, angle, orientation and other information of the drone;

The drone receives the command and starts sailing.

The method and system provided by this application integrate the inertial navigation data of the IMU, and use the direction information measured by the inertial navigation as the input information of the SLAM system to avoid the problem of being unable to obtain the current direction of movement when there is no GPS signal, and improve the reliability of the system's autonomous positioning. sex. At the same time, the invention is based on multi-line three-dimensional laser scanning data and uses the SLAM data system to construct and update a three-dimensional map in real time for use in UAV navigation.

At the same time, the present invention can realize the autonomous obstacle avoidance flight of the drone based on the map model scanned by the three-dimensional lidar. Compared with the general planar single-line lidar, the advantage of using the three-dimensional lidar is that it can obtain the three-dimensional terrain information of the environment. The three-dimensional information ratio is The amount of information in two-dimensional information is several orders of magnitude greater, which means that the SLAM positioning algorithm based on three-dimensional data can obtain more accurate and stable position information. In addition, it can also obtain the coordinates of any point in the three-dimensional space, which makes unmanned The drone can have the ability to autonomously generate flight tracks and fly autonomously. As long as the user points out the target location that the drone needs to reach, the drone can automatically and quickly generate the drone's flight route. The route is scanned based on the surrounding environment. Accurate map construction, the route generated on this basis is very safe, and the algorithm can ensure the most efficient flight route.

In addition, through the multi-sensor fusion module of the present invention, when there are GPS and/or RTK signals, the coordinates generated by the method and system provided by the present invention fuse the absolute position coordinates of GPS or RTK. Therefore, when the GPS or RTK signals are valid, the system By converting relative coordinates into global standard earth coordinates, the generated coordinate data can be directly displayed in the earth coordinate system. The generated routes can also be used by other application systems to achieve the goal of sharing data. The system routes will not be as similar as relative coordinates. Like the coordinate system, once the origin is lost, all data is lost, but it can be permanently valid like GPS coordinates. In addition, this system simultaneously corrects a set of states from GPS and/or RTK, 3D lidar and IMU data, allowing multi-sensor fusion to achieve seamless switching, make up for each other's shortcomings, and maximize sensor performance.

The above has introduced in detail the embodiments of a UAV based on LiDAR SLAM positioning and navigation provided by the present invention, the LiDAR SLAM positioning and navigation UAV method and the system thereof. For those of ordinary skill in the art, According to the ideas of the embodiments of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this description should not be understood as a limitation of the present invention.

Claims (6)

1. A drone based on the positioning and navigation of the lidar SLAM system, which is characterized by including: a drone body, a multi-line three-dimensional lidar unit installed directly in front of the drone body, an IMU inertial sensor, and high-performance data processing Unit and flight control system; the high-performance data processing unit performs calculations based on the point cloud data detected by lidar, generates the current relative position of the UAV, and transmits the current relative position information of the UAV to the UAV flight control System, the flight control system generates flight routes in real time based on the position information of the UAV, and performs positioning and navigation of the UAV's flight; among them, The high-performance data processing unit also handles the following functions: Solve the point cloud data to obtain the lidar pose and motion information of the current frame and the local map information of the surrounding environment of the current frame; update the map in the lidar SLAM system based on the local map information, and fuse all local maps to generate 3D global map; Generate a first motion trajectory by combining the lidar pose and motion information of the previous frame with the lidar pose and motion information of the current frame; Combine the IMU pose and motion information of the previous frame with the IMU pose and motion information of the current frame to generate a second motion trajectory; compare the first motion trajectory and the second motion trajectory to obtain a comparison error, and compare the comparison error with the system's preset Compare laser SLAM calibration errors; Based on the comparison results of the comparison error and the laser SLAM calibration error, the pose and motion information of the IMU and lidar are fused, and combined with the currently updated global map model, the fused pose and motion information of the current frame is obtained. 2. The UAV based on lidar SLAM system positioning and navigation according to claim 1, characterized in that: the high-performance data processing unit also filters and preprocesses the collected point cloud data; according to the preprocessing The subsequent point cloud data updates the temporary local map information. 3. The UAV based on laser radar SLAM system positioning and navigation according to claim 2, characterized in that: the high-performance data processing unit compares the comparison error with the laser SLAM calibration error and also includes the following processing: When the comparison error is greater than the laser SLAM calibration error, the IMU pose and motion information of the current frame are modified to the lidar pose and motion information of the current frame; When the comparison error is less than the laser SLAM calibration error, the IMU pose and motion information of the current frame are stored. 4. The UAV based on lidar SLAM system positioning and navigation according to claim 3, characterized in that: it also includes a GPS device and/or an RTK device. When the presence of a GPS signal and/or an RTK signal is detected, the high-performance The data processing unit combines the fused pose and motion information of the current frame with the GPS signal and/or RTK signal to obtain the fused pose and motion information with the earth coordinate system. 5. The UAV based on the positioning and navigation of the lidar SLAM system as claimed in claim 4, further comprising a route generation unit that obtains the location information specified by the user and updates the map information according to the location information and map specified by the user. , integrate pose and motion information to generate the best route for the flight control unit to control the positioning and navigation of the drone. 6. The UAV based on lidar SLAM system positioning and navigation according to claim 5, characterized in that: the three-dimensional lidar unit, IMU inertial sensor, and high-performance data processing unit are integrated in one frame.