Lidar SLAM positioning and navigation method and unmanned aerial vehicle system

Abstract

The present invention discloses a method for positioning and navigation based on laser radar SLAM and its unmanned aerial vehicle system, including a laser information collection unit for collecting information for a three-dimensional laser SLAM system, a laser information processing unit for filtering and preprocessing the collected information, solving and processing the filtered and preprocessed data, fusing the motion posture information with the three-dimensional map model information to generate the current posture and motion information, and a flight control unit for obtaining the position information specified by the user, and generating the best route based on the position information specified by the user, map update information, fused posture and motion information, and displaying and controlling the positioning and navigation of the unmanned aerial vehicle. The method and system provided by the present invention can be flown indoors or under any condition without satellite GPS signal reception, and is safe and reliable, is not easily interfered by wireless signals, and can fly autonomously without any communication connection.

Description

Technical Field

[0001] The present invention belongs to the technical field of unmanned aerial vehicles, and in particular relates to a method for positioning and navigation based on laser radar SLAM and an unmanned aerial vehicle system thereof. Background Art

[0002] Unmanned aerial vehicles, or UAVs for short, 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 post-disaster search and rescue, aerial photography, crop monitoring, military operations and other fields because of their flexibility, small constraints on ground terrain, low cost and no casualties. Different fields have different requirements for UAVs, such as requiring UAVs to achieve complex tasks such as positioning, obstacle avoidance, tracking and trajectory planning.

[0003] In recent years, quadcopter drones have become an important research direction in the drone industry due to their low cost and easy operation. As a flying robot, quadcopter drones are increasingly required to have high-precision, intelligent, and autonomous flight capabilities, and the basis for these capabilities is that drones have accurate state estimation and environmental perception capabilities. At present, various aircraft generally use GPS / RTK for positioning. However, in some cases, such as indoor environments or crowded areas, the accuracy of GPS / RTK deteriorates or even fails, which is not enough to support autonomous flight of drones. The fixed-point hovering of drones using satellite navigation technology as a navigation method has errors that cannot be ignored, and it is ultimately difficult to achieve stable hovering in a fixed area. However, if a binocular vision system is installed on the drone, the drone will no longer need to be autonomously controlled through external signal transmission.

[0004] Because most drones currently rely on GPS / RTK signals to fly, and in indoor environments, GPS / RTK signals cannot be obtained, so studying how drones can still perform obstacle avoidance flight when GPS / RTK signals are lost is the main difficulty and a major challenge to drone flight safety. Therefore, the research and development of autonomous flight for multi-rotor drone flight systems is of great significance. Summary of the invention

[0005] The embodiment of the present invention provides a method for positioning and navigation based on laser radar SLAM and a drone system thereof, which can achieve the effect that the drone can still perform obstacle avoidance flight without relying on GPS / RTK signals.

[0006] On the one hand, an embodiment of the present invention provides a method for positioning and navigating a drone based on a laser radar SLAM, comprising the following steps:

[0007] (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 3D laser radar;

[0008] (2) Filter and preprocess the collected point cloud data information;

[0009] (3) Solve the filtered point cloud data to obtain the laser radar posture 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;

[0010] (4) Update the map in the laser SLAM system based on the local map information, and fuse all local maps to generate a three-dimensional global map;

[0011] (5) combining the laser radar posture and motion information of the previous frame with the laser radar posture and motion information of the current frame to generate a first motion trajectory;

[0012] (6) Combining the IMU posture and motion information of the previous frame with the IMU posture and motion information of the current frame to generate a second motion trajectory;

[0013] (7) Compare the first motion trajectory and the second motion trajectory to obtain a comparison error, and compare the comparison error with a laser SLAM calibration error preset by the system;

[0014] (8) According to the comparison result of the comparison error and the laser SLAM calibration error, the position and motion information of the IMU and the laser radar are fused, and combined with the currently updated global map model to obtain the fused position and motion information of the current frame;

[0015] (9) Obtain the location information specified by the user, and generate the best route based on the user-specified location information, map update information, fused posture and motion information, and display and control the positioning and navigation of the drone.

[0016] Preferably, the filtering and preprocessing of the collected point cloud data in step 2 further includes the following steps:

[0017] (2-1) Filter the collected point cloud data;

[0018] (2-2), preprocessing the filtered point cloud data;

[0019] (2-3) Update the temporary local map based on the preprocessed point cloud data.

[0020] Preferably, the step of comparing the comparison error with the laser SLAM calibration error in step 7 further comprises the following steps:

[0021] (7-1) If the comparison error is greater than the laser SLAM calibration error, the IMU pose and motion information of the current frame is replaced with the lidar pose and motion information of the current frame;

[0022] (7-2) If the comparison error is smaller than the laser SLAM calibration error, the IMU pose and motion information of the current frame is stored.

[0023] Preferably, 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 the fused pose and motion information with the earth coordinate system.

[0024] On the other hand, an embodiment of the present invention provides a UAV system based on laser radar SLAM positioning and navigation, including a three-dimensional laser SLAM system and a flight control unit, wherein the three-dimensional laser SLAM system provides UAV positioning and navigation information, and the flight control unit controls the UAV positioning and navigation flight according to the positioning and navigation information of the three-dimensional laser SLAM system;

[0025] The three-dimensional laser SLAM system also includes a laser information acquisition unit and a laser information processing unit, wherein:

[0026] The laser information acquisition unit is used to collect information for the three-dimensional laser SLAM system, including an IMU unit and at least one multi-line three-dimensional laser radar module, wherein:

[0027] The IMU unit is used to provide predicted position and motion information for the three-dimensional laser SLAM system;

[0028] The laser radar is used to collect point cloud data of the surrounding environment;

[0029] The laser information processing unit is used to process the information collected by the laser information collection unit, including:

[0030] Point cloud processing module, used for filtering and preprocessing point cloud data;

[0031] SLAM data processing module, used to solve the filtered and pre-processed data and output motion posture information and three-dimensional map model; and

[0032] The multi-sensor fusion module is used to generate the current posture and motion information based on the motion posture information obtained by the SLAM data processing module and the three-dimensional map model information.

[0033] Preferably, the SLAM data processing module also includes a radar solution module, a trajectory generation module, a trajectory comparison module, a calibration module, and a laser radar fusion module, wherein:

[0034] The radar solution module is used to solve the point cloud data after filtering to obtain the corresponding laser radar posture and motion information and the local map information of the surrounding environment of the current frame;

[0035] The trajectory generation module is used to generate a first motion trajectory of the UAV by combining the laser radar posture and motion information of two consecutive frames, and to generate a second motion trajectory of the UAV by combining the IMU posture and motion information of two consecutive frames;

[0036] The trajectory comparison module is used to compare the first motion trajectory and the second motion trajectory to obtain a comparison error, and compare the obtained comparison error with a preset laser SLAM calibration error;

[0037] The laser radar fusion module fuses the position and motion information output by the IMU unit and the laser radar according to the comparison result of the trajectory comparison module; and outputs the stable position and motion information of the current UAV.

[0038] Preferably, the SLAM data processing module also includes:

[0039] A map update module, used to update the map information of the 3D laser SLAM system in real time according to the local map information of the surrounding environment of the current frame; and

[0040] The global map fusion module is used to fuse all the local map information to generate the global three-dimensional map model information, so that the UAV can plan the navigation path in the global map information.

[0041] Preferably, the UAV system based on laser radar SLAM positioning and navigation also includes a calibration module for correcting the IMU unit error in real time according to the comparison result of the trajectory comparison module.

[0042] Preferably, when there are GPS and / or RTK signals, the multi-sensor fusion module also fuses the posture information and motion information output by the lidar fusion module, the GPS and / or RTK position information and the currently updated map information to obtain fused posture and motion information with earth coordinates.

[0043] Preferably, providing predicted posture and motion information for the three-dimensional laser SLAM system also includes the IMU unit first collecting mileage information, converting the mileage information into UAV posture change information through the UAV inertial odometer kinematic model, and sending it to the Bayesian filter to preliminarily calculate the predicted posture and motion information, and the filtering processing includes denoising the point cloud data, removing abnormal points and reducing redundant point cloud data processing.

[0044] The method and system provided by the present invention integrate the inertial navigation data of IMU, and use the direction information measured by 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. At the same time, the present invention is based on multi-line three-dimensional laser scanning data and uses the SLAM data system to build and update the three-dimensional map in real time for drone navigation. The beneficial effects of the present invention are as follows:

[0045] 1. It can be flown indoors or in any condition where there is no satellite GPS signal reception. It is safe and reliable, not easily interfered by wireless signals, and can fly autonomously without any communication connection.

[0046] 2. Realize high-reliability hovering and autonomous flight of drones, and independently plan routes and reach destinations.

[0047] 3. The system's lidar is an autonomous luminous sensor that can fly in complete darkness. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 The present invention provides a general flow chart of the laser radar SLAM positioning and navigation method in an embodiment of the present invention.

[0050] Figure 2 This is a module diagram of an unmanned aerial vehicle system based on laser radar SLAM positioning and navigation in an embodiment of the present invention. DETAILED DESCRIPTION

[0051] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application generally described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0052] On the one hand, see Figure 1 The embodiment of the present invention provides a method for positioning and navigating a drone based on a laser radar SLAM, comprising the following steps:

[0053] (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 3D laser radar;

[0054] (2) Filter and preprocess the collected point cloud data information;

[0055] (3) Solve the filtered point cloud data to obtain the laser radar posture 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;

[0056] (4) Update the map in the laser SLAM system based on the local map information, and fuse all local maps to generate a three-dimensional global map;

[0057] (5) combining the laser radar posture and motion information of the previous frame with the laser radar posture and motion information of the current frame to generate a first motion trajectory;

[0058] (6) Combining the IMU posture and motion information of the previous frame with the IMU posture and motion information of the current frame to generate a second motion trajectory;

[0059] (7) Compare the first motion trajectory and the second motion trajectory to obtain a comparison error, and compare the comparison error with a laser SLAM calibration error preset by the system;

[0060] (8) According to the comparison result of the comparison error and the laser SLAM calibration error, the position and motion information of the IMU and the laser radar are fused, and combined with the currently updated global map model to obtain the fused position and motion information of the current frame;

[0061] (9) Obtain the location information specified by the user, and generate the best route based on the user-specified location information, map update information, fused posture and motion information, and display and control the positioning and navigation of the drone.

[0062] In an embodiment of the present invention, the filtering and preprocessing of the collected point cloud data in step 2 further includes the following steps:

[0063] (2-1) Filter the collected point cloud data;

[0064] (2-2), preprocessing the filtered point cloud data;

[0065] (2-3) Update the temporary local map based on the preprocessed point cloud data.

[0066] In an embodiment of the present invention, comparing the comparison error with the laser SLAM calibration error in step 7 further includes the following steps:

[0067] (7-1) If the comparison error is greater than the laser SLAM calibration error, the IMU pose and motion information of the current frame is replaced with the lidar pose and motion information of the current frame;

[0068] (7-2) If the comparison error is smaller than the laser SLAM calibration error, the IMU pose and motion information of the current frame is stored.

[0069] In an embodiment of the present invention, step 8 also includes: when there is a GPS signal and / or an RTK signal, combining the fused pose and motion information of the current frame with the GPS signal and / or the RTK signal to obtain the fused pose and motion information with the earth coordinate system.

[0070] On the other hand, an embodiment of the present invention provides a UAV system based on laser radar SLAM positioning and navigation, Figure 2 The module diagram of the UAV system based on laser radar SLAM positioning and navigation in the embodiment of the present invention includes a three-dimensional laser SLAM system 100 and a flight control unit 200, wherein the three-dimensional laser SLAM system 100 provides the 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;

[0071] The three-dimensional laser SLAM system 100 also includes a laser information acquisition unit 110 and a laser information processing unit 120, wherein:

[0072] The laser information acquisition unit 110 is used to collect information for the three-dimensional laser SLAM system 100, and includes an IMU unit 111 and at least one multi-line three-dimensional laser radar module 112, wherein:

[0073] The IMU unit 111 is used to provide predicted position and motion information for the three-dimensional laser SLAM system 100;

[0074] The laser radar 112 is used to collect point cloud data of the surrounding environment;

[0075] The laser information processing unit 120 is used to process the information collected by the laser information collection unit, including:

[0076] A point cloud processing module 130, for filtering and preprocessing point cloud data;

[0077] The SLAM data processing module 140 is used to process the filtered and pre-processed data and output motion posture information and a three-dimensional map model;

[0078] and a multi-sensor fusion module 180, which is used to generate current posture and motion information based on the motion posture information obtained by the SLAM data processing module and the three-dimensional map model information.

[0079] In the embodiment of the present invention, the SLAM data processing module 140 further includes a radar solution module 141, a trajectory generation module 142, a trajectory comparison module 143, a calibration module 144, and a laser radar fusion module 145, wherein:

[0080] The radar solving module 141 is used to solve the point cloud data after filtering to obtain the corresponding laser radar posture and motion information and the local map information of the surrounding environment of the current frame;

[0081] The trajectory generation module 142 is used to generate a first motion trajectory of the drone by combining the laser radar posture and motion information of two consecutive frames, and to generate a second motion trajectory of the drone by combining the IMU posture and motion information of two consecutive frames;

[0082] The trajectory comparison module 143 is used to compare the first motion trajectory and the second motion trajectory to obtain a comparison error, and compare the obtained comparison error with a preset laser SLAM calibration error;

[0083] The laser radar fusion module 145 fuses the position and motion information output by the IMU unit 111 and the laser radar according to the comparison result of the trajectory comparison module, and outputs the stable position and motion information of the current UAV.

[0084] In an embodiment of the present invention, the SLAM data processing module further includes:

[0085] A map updating module 160, for updating the map information of the 3D laser SLAM system 100 in real time according to the local map information of the surrounding environment of the current frame; and

[0086] The global map fusion module 170 is used to fuse all the local map information to generate the global three-dimensional map model information, so that the drone can plan a navigation path in the global map information.

[0087] In the embodiment of the present invention, the UAV system based on laser radar SLAM positioning and navigation further includes a calibration module 144 for correcting the error of the IMU unit 111 in real time according to the comparison result of the trajectory comparison module.

[0088] In an embodiment of the present invention, when there are GPS and / or RTK signals, the multi-sensor fusion module 180 also fuses the posture information and motion information output by the lidar fusion module, the GPS and / or RTK position information, and the currently updated map information to obtain fused posture and motion information with earth coordinates.

[0089] Preferably, providing predicted posture and motion information for the three-dimensional laser SLAM system 100 also includes the IMU unit 111 first collecting mileage information, converting the mileage information into UAV posture change information through the UAV inertial odometer kinematic model, and sending it to the Bayesian filter to preliminarily calculate the predicted posture and motion information. The filtering processing includes denoising the point cloud data, removing abnormal points and reducing redundant point cloud data processing.

[0090] In the embodiment of the present invention, the point cloud processing module 130 further includes a filtering module 131 and a pre-processing module 132, wherein:

[0091] The filtering module 131 is used for filtering the point cloud data collected by the laser radar 112;

[0092] The pre-processing module 132 is used to perform preliminary processing on the filtered point cloud data to obtain temporary local map information.

[0093] In the embodiment of the present invention, the flight control unit 200 further includes a global planning module 210, a local planning module 220 and a bottom control module 230, wherein:

[0094] The global planning module 210 is used to plan the best navigation path of the global map;

[0095] The local planning module 220 is used to plan the global optimal path for the real-time local map information obtained after preprocessing;

[0096] The bottom control module 230 is used to control and distribute the UAV.

[0097] The overall operation of a UAV system based on laser radar SLAM positioning and navigation in an embodiment of the present invention is as follows:

[0098] The IMU unit 111 obtains the mileage information of the 3D laser SLAM system 100, and converts the mileage information into the UAV posture change information through the UAV inertial odometer kinematic model, and sends it to the Bayesian filter to preliminarily calculate the IMU posture and motion information of the current frame and output it to the trajectory generation module 142. The multi-line 3D laser scanning radar collects the point cloud data information of the current frame and outputs it to the filtering module 131;

[0099] The filtering module 131 performs filtering processing such as noise reduction, removal of abnormal points and reduction of redundant point cloud data on the collected point cloud data, and outputs the filtered point cloud data to the radar solution module 141 and the pre-processing module 132 respectively;

[0100] 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;

[0101] The radar solving module 141 solves the filtered point cloud data to obtain the laser radar posture 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 outputs the laser radar posture and motion information of the current frame to the trajectory generation module 142, and outputs the local map information of the surrounding environment of the current frame in the laser system to the map update module 160;

[0102] The map updating module 160 updates the local map information of two consecutive frames in real time and outputs it to the global map fusion module 170;

[0103] The global map fusion module 170 fuses all the local map information to generate global map information. When the UAV takes off from the same location next time, the global map information can be directly used and output to the laser radar fusion module 145 and the global planning module 210;

[0104] The trajectory generation module 142 generates a first motion trajectory by combining the laser radar posture and motion information of the previous frame with the laser radar posture and motion information of the current frame, generates a second motion trajectory by combining the IMU posture and motion information of the previous frame with the IMU posture and motion information of the current frame, and outputs the first motion trajectory and the second motion trajectory to the trajectory comparison module 143;

[0105] The trajectory comparison module 143 compares the first motion trajectory with the second motion trajectory to obtain a first comparison error, compares the first comparison error with a preset laser SLAM calibration error, and outputs the comparison result to the calibration module 144;

[0106] If the first comparison error is greater than the laser SLAM calibration error, the calibration module 144 replaces the IMU pose and motion information of the current frame with the laser radar pose and motion information of the current frame. If the first comparison error is less than the laser SLAM calibration error, the IMU pose and motion information of the current frame is stored.

[0107] The position and motion information of the IMU and the laser radar is output to the laser radar fusion module 145. The laser radar fusion module 145 fuses the position and motion information of the IMU and the laser radar according to the fusion algorithm to obtain the fused position and motion information of the current frame, and outputs the fused position and motion information of the current frame to the multi-sensor fusion module 180. The multi-sensor fusion module 180 fuses the fused position and motion information received by the fused module 180 and the updated global three-dimensional map information. When there is an external GPS and / or RTK signal, the GPS and / or RTK information is simultaneously fused to obtain the fused position and motion information with the earth coordinate system, and outputs it to the global planning module 210.

[0108] The global planning module 210 plans the best navigation path for the UAV to reach the target point according to the user-specified position, map update information, and fused posture and motion information, that is, the navigation path with the shortest route and no obstacles during the journey, and outputs all data to the local planning module 220;

[0109] The local planning module 220 plans the local optimal navigation path according to 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 optimal navigation path to obtain the optimal path, and outputs the optimal path to the bottom control module 230;

[0110] The bottom control module 230 controls the drone according to the optimal path and controls the flight speed, angle, and orientation of the drone;

[0111] The drone received the command and started flying.

[0112] The method and system provided by the present application integrate the inertial navigation data of IMU, and use the direction information measured by inertial navigation as the input information of 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. At the same time, the present invention is based on multi-line three-dimensional laser scanning data and uses the SLAM data system to build and update three-dimensional maps in real time for drone navigation.

[0113] At the same time, the present invention can realize autonomous obstacle avoidance flight of the UAV based on the map model scanned by the three-dimensional laser radar. Compared with the general planar single-line laser radar, the advantage of using the three-dimensional laser radar is that the three-dimensional terrain information of the environment can be obtained. The amount of information of three-dimensional information is several orders of magnitude more than that of two-dimensional information. This 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. This enables the UAV to have the ability to autonomously generate flight tracks and perform autonomous flight. As long as the user points out the target position that the UAV needs to reach, the UAV can automatically and quickly generate the flight route of the UAV. The route is constructed based on the accurate map scanned from the surrounding environment. The route generated on this basis is very safe, and the algorithm can ensure that the flight route is the most efficient.

[0114] In addition, the present invention uses a multi-sensor fusion module. 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 signal is valid, the system transforms the relative coordinates into global standard earth coordinates, and the generated coordinate data can be directly displayed in the earth coordinate system. The generated route can also be used by other application systems to achieve the goal of sharing data. The system's route will not be like the relative coordinate system. Once the origin is lost, it means that all data is lost, but it can be permanently valid like GPS coordinates. In addition, the system simultaneously corrects a group of states of GPS and / or RTK, three-dimensional laser radar and IMU data, so that multi-sensor fusion can achieve seamless switching, make up for each other's shortcomings, and maximize the performance of sensors.

[0115] The above is a detailed introduction to a method and system for laser radar SLAM positioning and navigation of drones provided by the present invention. For a person skilled in the art, according to the ideas of the embodiments of the present invention, there may be changes in the specific implementation methods and application scopes. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for positioning and navigating drones based on LiDAR SLAM. It is characterized in that The following steps are involved: (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 3D laser radar; (2) Filter and preprocess the collected point cloud data information; (3) Solve the filtered point cloud data to obtain the laser radar posture 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 the local map information, and fuse all local maps to generate a three-dimensional global map; (5) combining the laser radar posture and motion information of the previous frame with the laser radar posture and motion information of the current frame to generate a first motion trajectory; (6) Combining the IMU posture and motion information of the previous frame with the IMU posture and motion information of the current frame to generate a second motion trajectory; (7) Compare the first motion trajectory and the second motion trajectory to obtain a comparison error, and compare the comparison error with a laser SLAM calibration error preset by the system; (8) According to the comparison result of the comparison error and the laser SLAM calibration error, the position and motion information of the IMU and the laser radar are fused, and combined with the currently updated global map model to obtain the fused position and motion information of the current frame; (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, fusion posture and motion information, and display and control the positioning and navigation of the drone; The step (7) of comparing the comparison error with the laser SLAM calibration error also includes the following steps: (7-1) If the comparison error is greater than the laser SLAM calibration error, the IMU pose and motion information of the current frame is modified to 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 is stored.

2. The method for positioning and navigating a UAV based on laser radar SLAM according to claim 1, It is characterized in that The filtering and preprocessing of the collected point cloud data in step (2) further includes the following steps: (2-1) Filter the collected point cloud data; (2-2), preprocessing the filtered point cloud data; (2-3) Update the temporary local map based on the preprocessed point cloud data.

3. The method for positioning and navigating a UAV based on laser radar SLAM according to claim 2, It is characterized in that The step (8) further includes: when there is a GPS signal and / or an RTK signal, combining the fused posture and motion information of the current frame with the GPS signal and / or the RTK signal to obtain the fused posture and motion information with the earth coordinate system.

4. A UAV system based on laser radar SLAM positioning and navigation, It is characterized in that The invention comprises a three-dimensional laser SLAM system (100) and a flight control unit (200), wherein the three-dimensional laser SLAM system (100) provides positioning and navigation information of a drone, and the flight control unit (200) controls the positioning and navigation flight of the drone according to the positioning and navigation information of the three-dimensional laser SLAM system, wherein: The three-dimensional laser SLAM system (100) further comprises a laser information acquisition unit (110) and a laser information processing unit (120), wherein: The laser information acquisition unit (110) is used to collect information for the three-dimensional laser SLAM system (100), and comprises an IMU unit (111) and at least one multi-line three-dimensional laser radar module (112), wherein: The IMU unit (111) is used to provide predicted position and motion information for the three-dimensional laser SLAM system (100); The laser radar (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, and comprises: A point cloud processing module (130), used for filtering and preprocessing point cloud data; A SLAM data processing module (140) is used to perform calculation processing on the filtered and pre-processed data and output motion posture information and a three-dimensional map model; and A multi-sensor fusion module (180) is used to generate current posture information based on the motion posture information obtained by the SLAM data processing module and the three-dimensional map model information; The SLAM data processing module (140) further comprises a radar solution module (141), a trajectory generation module (142), a trajectory comparison module (143), a calibration module (144), a laser radar fusion module (145), a map update module (160) and a global map fusion module (170), wherein: The radar solving module (141) is used to solve the point cloud data after filtering to obtain the corresponding laser radar posture 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 a first motion trajectory of the drone by combining two consecutive frames of laser radar posture and motion information, and to generate a second motion trajectory of the drone by combining two consecutive frames of IMU posture and motion information; The trajectory comparison module (143) is used to compare the first motion trajectory and the second motion trajectory to obtain a comparison error, and compare the obtained comparison error with a preset laser SLAM calibration error; The laser radar fusion module (145) fuses the position and motion information output by the IMU unit (111) and the laser radar according to the comparison result of the trajectory comparison module, and outputs the stable position and motion information of the current unmanned aerial vehicle; The map updating 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; 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 drone can plan a navigation path in the global map information; The calibration module (144) is used to correct the error of the IMU unit (111) in real time according to the comparison result of the trajectory comparison module; when the comparison error is greater than the preset laser SLAM calibration error, the IMU posture and motion information of the current frame is modified to the laser radar posture and motion information of the current frame; otherwise, the IMU posture and motion information of the current frame is stored.

5. The unmanned aerial vehicle system based on laser radar SLAM positioning and navigation as claimed in claim 4, It is characterized in that When there are GPS and / or RTK signals, the multi-sensor fusion module (180) also fuses the posture information and motion information output by the laser radar fusion module, the GPS and / or RTK position information and the currently updated map information to obtain fused posture information with earth coordinates.

6. The unmanned aerial vehicle system based on laser radar SLAM positioning and navigation as claimed in claim 5, It is characterized in that The method of providing predicted posture and motion information for the three-dimensional laser SLAM system (100) also includes the IMU unit (111) first collecting mileage information, converting the mileage information into UAV posture change information through the UAV inertial odometer kinematic model, and sending it to the Bayesian filter to preliminarily calculate the predicted posture and motion information. The filtering process includes denoising the point cloud data, removing abnormal points and reducing redundant point cloud data processing.

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