External parameter calibration method for laser radar and combined inertial navigation and related device

Abstract

The invention discloses a laser radar and combined inertial navigation external parameter calibration method and a related device, and relates to the technical field of robots. Position sensors are arranged on the laser radar and the combined inertial navigation to detect the position offset of the laser radar and the combined inertial navigation in real time; if the position offset detected by at least one position sensor is greater than the first threshold value, external parameter calibration of the laser radar and the combined inertial navigation is automatically started, so that the problem of inaccurate external parameters caused by position offset of the laser radar or the combined inertial navigation is avoided; the accuracy of data fusion and target detection based on the laser radar and the combined inertial navigation is ensured, so that the running safety of the robot is ensured.

Description

Technical Field

[0001] The present application relates to the field of robotics technology, and in particular to an external parameter calibration method and related devices for a laser radar and a combined inertial navigation system. Background Art

[0002] LiDAR and combined inertial navigation system are commonly used sensors in the field of robot navigation. The data of LiDAR and combined inertial navigation system are integrated and processed to obtain more accurate and reliable information.

[0003] At present, the laser radar and the combined inertial navigation system are generally fixedly installed on the robot, and the laser radar point cloud is converted to the robot coordinate system through pre-calibrated fixed external parameters. However, as the robot moves, the laser radar and the combined inertial navigation system may be offset due to vibration. If fixed external parameters are still used, the accuracy of the laser radar point cloud conversion will be affected, thereby affecting the safety of the robot operation. Summary of the invention

[0004] In view of the above problems, the present application provides a method and related device for calibrating the external parameters of laser radar and combined inertial navigation, which detects the position offset of laser radar and combined inertial navigation in real time through a position sensor, and calibrates the external parameters of laser radar and combined inertial navigation in real time when the position offset is greater than a threshold. The specific scheme is as follows: In a first aspect, the present application provides an external parameter calibration method for a laser radar and a combined inertial navigation system, which is applied to a controller in a robot, wherein the robot further comprises a laser radar and a combined inertial navigation system, wherein at least one position sensor is arranged on the laser radar, and at least one position sensor is arranged on the combined inertial navigation system, and wherein the external parameter calibration method for the laser radar and the combined inertial navigation system comprises: Acquire the position offset detected by each of the position sensors; If the position offset detected by at least one of the position sensors is greater than a first threshold, obtaining measurement data of the laser radar and the combined inertial navigation; The target extrinsic parameters of the laser radar and the combined inertial navigation are calculated according to the measurement data of the laser radar and the combined inertial navigation.

[0005] In a possible implementation, the external parameter calibration method of the laser radar and combined inertial navigation further includes: Recording the accumulated mileage of the robot corresponding to the current external parameters of the laser radar and the combined inertial navigation; If the accumulated mileage is greater than a second threshold, the steps of acquiring measurement data of the laser radar and the combined inertial navigation system and calculating target external parameters of the laser radar and the combined inertial navigation system according to the measurement data of the laser radar and the combined inertial navigation system are executed.

[0006] In a possible implementation, after calculating the target extrinsic parameters of the laser radar and the combined inertial navigation according to the measurement data of the laser radar and the combined inertial navigation, the extrinsic parameter calibration method of the laser radar and the combined inertial navigation further includes: Determining whether the robot meets the preset external parameter update conditions; If the robot meets the preset external parameter update condition, the current external parameters of the laser radar and the combined inertial navigation are updated to the target external parameters.

[0007] In a possible implementation, the determining whether the robot satisfies a preset external parameter update condition includes: Determining whether the robot is in a working state; If the robot is in a working state, it is determined that the robot does not meet the preset external parameter update condition.

[0008] In a possible implementation, the determining whether the robot satisfies a preset external parameter update condition further includes: If the robot is in a non-working state, determining whether there is an obstacle within a first preset range of the robot's forward direction according to the measurement data of the laser radar; If there is no obstacle within the first preset range of the robot's forward direction, it is determined that the robot meets the preset external parameter update condition.

[0009] In a possible implementation, the determining whether the robot satisfies a preset external parameter update condition further includes: If there is an obstacle within the first preset range in the forward direction of the robot, a Kalman filter algorithm is used to predict the state data of the robot at the next moment based on the state data of the robot at the current moment and the measurement data of the laser radar and the combined inertial navigation; Determine whether there is an obstacle within a first preset range in the robot's forward direction according to the state data of the robot at the next moment; If there is an obstacle within the first preset range in the forward direction of the robot, return to the step of using a Kalman filter algorithm to predict the state data of the robot at the next moment based on the state data of the robot at the current moment and the measurement data of the laser radar and the combined inertial navigation; If there is no obstacle within the first preset range of the robot's forward direction, it is determined that the robot meets the preset external parameter update condition.

[0010] In a possible implementation, the determining whether the robot satisfies a preset external parameter update condition further includes: If there is an obstacle within a first preset range in the robot's forward direction, determining whether the duration for which the robot does not meet the preset external parameter update condition is greater than a preset time; If it is greater than the preset time, determining whether there is an obstacle within a second preset range in the robot's forward direction according to the state data of the robot at the next moment, wherein the second preset range is smaller than the first preset range; If there is an obstacle within the second preset range in the forward direction of the robot, return to the step of using a Kalman filter algorithm to predict the state data of the robot at the next moment based on the state data of the robot at the current moment and the measurement data of the laser radar and the combined inertial navigation; If there is no obstacle within the second preset range of the robot's forward direction, it is determined that the robot meets the preset external parameter update condition.

[0011] A second aspect of the present application provides a controller, comprising at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the controller can implement the external parameter calibration method of the laser radar and combined inertial navigation of the above-mentioned first aspect or any implementation method of the first aspect.

[0012] A third aspect of the present application provides a robot, comprising: a robot body, a controller, and a laser radar and a combined inertial navigation system arranged on the robot body; At least one position sensor is provided on the laser radar, for detecting the position offset of the laser radar; At least one position sensor is provided on the combined inertial navigation system for detecting a position offset of the combined inertial navigation system; The controller is used to implement the external parameter calibration method of the laser radar and combined inertial navigation of the above-mentioned first aspect or any implementation method of the first aspect.

[0013] The fourth aspect of the present application provides a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on a controller, the controller implements the external parameter calibration method of the laser radar and combined inertial navigation of the above-mentioned first aspect or any implementation method of the first aspect.

[0014] By means of the above technical scheme, the external parameter calibration method and related devices of the laser radar and combined inertial navigation provided in the present application, by setting position sensors on the laser radar and the combined inertial navigation, detect the position offset of the laser radar and the combined inertial navigation in real time. If the position offset detected by at least one position sensor is greater than the first threshold, the external parameter calibration of the laser radar and the combined inertial navigation is automatically started to avoid the problem of inaccurate external parameters due to the position offset of the laser radar or the combined inertial navigation, thereby ensuring the accuracy of data fusion and target detection based on the laser radar and the combined inertial navigation, thereby ensuring the safety of the robot operation. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and the originals and elements are not necessarily drawn to scale.

[0016] Figure 1 A flowchart of an external parameter calibration method for a laser radar and a combined inertial navigation system provided in an embodiment of the present application; Figure 2 A flowchart of another external parameter calibration method for a laser radar and a combined inertial navigation system provided in an embodiment of the present application; Figure 3 A schematic diagram of the structure of a controller provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of a robot provided in an embodiment of the present application. DETAILED DESCRIPTION

[0017] The following describes the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. The terms used in the implementation method section of the present application are only used to explain the specific embodiments of the present application, and are not intended to limit the present application.

[0018] The embodiments of the present application are described below in conjunction with the accompanying drawings. Those skilled in the art will appreciate that, with the development of technology and the emergence of new scenarios, the technical solutions provided in the embodiments of the present application are also applicable to similar technical problems.

[0019] The terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and need not be used to describe a specific order or sequential order. It should be understood that the terms used in this way can be interchangeable under appropriate circumstances, which is only to describe the distinction mode adopted by the objects of the same attributes when describing in the embodiments of the present application. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, so that the process, method, system, product or equipment comprising a series of units need not be limited to those units, but may include other units that are not clearly listed or inherent to these processes, methods, products or equipment.

[0020] The embodiment of the present application provides an external parameter calibration method for a laser radar and a combined inertial navigation system, which is applied to a controller in a robot. The robot also includes a laser radar and a combined inertial navigation system. At least one position sensor is provided on the laser radar to detect the position offset of the laser radar. At least one position sensor is also provided on the combined inertial navigation system to detect the position offset of the combined inertial navigation system. The external parameter calibration method for the laser radar and the combined inertial navigation system in the embodiment of the present application is described in detail below in conjunction with the accompanying drawings.

[0021] Reference Figure 1 , Figure 1 The process diagram of a laser radar and combined inertial navigation external parameter calibration method provided in an embodiment of the present application is as follows: Figure 1 As shown, an external parameter calibration method for a laser radar and a combined inertial navigation system provided in an embodiment of the present application may include steps 101 to 103, and these steps are described in detail below.

[0022] 101: Obtain the position offset detected by each position sensor; The position sensor may be a laser displacement sensor, a capacitive sensor, or the like.

[0023] The position offset detected by the position sensor set on the laser radar represents the position offset of the laser radar, and the position offset detected by the position sensor set on the combined inertial navigation represents the position offset of the combined inertial navigation.

[0024] 102: If the position offset detected by at least one position sensor is greater than a first threshold, obtaining measurement data of the laser radar and the combined inertial navigation; The first threshold is set according to the actual application scenario. Exemplarily, the first threshold is 0.5 mm.

[0025] If the position offset detected by at least one position sensor is greater than the first threshold, it means that the position offset of the laser radar or the combined inertial navigation is too large, and the external parameters of the laser radar and the combined inertial navigation need to be recalibrated.

[0026] The laser radar measurement data includes the laser radar pose sequence: , , … ,in, is the time serial number of the laser radar, express The pose vector of the lidar at time .

[0027] The measurement data of the combined inertial navigation includes the combined inertial navigation attitude sequence: , , … ,in, is the time serial number of the combined inertial navigation, express The combined inertial navigation posture vector at time.

[0028] 103: Calculate the target extrinsic parameters of the laser radar and the combined inertial navigation according to the measurement data of the laser radar and the combined inertial navigation.

[0029] Exemplarily, the hand-eye calibration algorithm is used to align the laser radar and the combined inertial navigation posture sequence through the relative displacement of the laser radar and the combined inertial navigation to calculate the external parameter matrix of the laser radar and the combined inertial navigation. , which is the target external parameter.

[0030] For the i-th moment, the position vector of the lidar and the position vector of the combined inertial navigation have the following corresponding relationship: .

[0031] After the above formula is expressed in Lie algebra form, it can be solved iteratively using nonlinear optimization: , Where I is the identity matrix, for The transposed matrix of .

[0032] Then, the Gauss-Newton method (GN) or Levenberg-Marquardt algorithm (LM) is used to iteratively optimize the error J to the minimum, that is, to solve the final external parameter matrix .

[0033] The present embodiment discloses a method for calibrating external parameters of a laser radar and a combined inertial navigation system. Position sensors are arranged on the laser radar and the combined inertial navigation system to detect the position offset of the laser radar and the combined inertial navigation system in real time. If the position offset detected by at least one position sensor is greater than a first threshold, the external parameter calibration of the laser radar and the combined inertial navigation system is automatically started to avoid the problem of inaccurate external parameters due to the position offset of the laser radar or the combined inertial navigation system, thereby ensuring the accuracy of data fusion and target detection based on the laser radar and the combined inertial navigation system, thereby ensuring the safety of the robot operation.

[0034] In a possible implementation, considering that the robot runs for a long time, especially running on a rough road for a long time, it may also cause the position of the laser radar or the combined inertial navigation to shift. This embodiment records the cumulative mileage of the robot corresponding to the current external parameters of the laser radar and the combined inertial navigation, and each time the external parameters are updated, the cumulative mileage of the robot corresponding to the updated external parameters will also be recorded. If the cumulative mileage of the robot corresponding to the recorded current external parameters of the laser radar and the combined inertial navigation is greater than the second threshold, even if the position offset of the laser radar and the combined inertial navigation does not reach the first threshold, the external parameter calibration of the laser radar and the combined inertial navigation will be automatically started to further determine the accuracy and applicability of the external parameters of the laser radar and the combined inertial navigation. Among them, the second threshold is calibrated according to the application scenario of the robot, for example, the second threshold can be 800 kilometers.

[0035] In one possible implementation, the target external parameters are stored in a database. After the external parameter calibration is completed, the target external parameters can be updated to the database in a timely manner to ensure that the external parameters of the lidar and combined inertial navigation stored in the database are always up-to-date and accurate, providing an accurate basis for subsequent data processing.

[0036] It should be noted that after the external parameters of the lidar and combined inertial navigation are recalibrated, if the robot is in working state, such as a photovoltaic cleaning robot performing cleaning work, or the robot is close to an obstacle, there may be safety hazards after the external parameters of the lidar and combined inertial navigation are updated.

[0037] To solve the above technical problems, please refer to Figure 2 This embodiment provides an external parameter calibration method for a laser radar and a combined inertial navigation system, which specifically includes the following steps 201-205: 201: Obtain the position offset detected by each position sensor; 202: If the position offset detected by at least one position sensor is greater than a first threshold, obtaining measurement data of the laser radar and the combined inertial navigation; 203: Calculate target external parameters of the laser radar and the combined inertial navigation according to the measurement data of the laser radar and the combined inertial navigation; 204: Determine whether the robot meets the preset external parameter update conditions; 205: If the robot meets the preset external parameter update conditions, the current external parameters of the laser radar and the combined inertial navigation are updated to the target external parameters.

[0038] There are multiple ways to determine whether the robot meets the preset external parameter update conditions, which are described below with examples.

[0039] In a possible implementation, determining whether the robot satisfies a preset external parameter update condition includes: determining whether the robot is in a working state; if the robot is in a working state, determining that the robot does not satisfy the preset external parameter update condition.

[0040] Exemplarily, the robot is a photovoltaic cleaning robot. If the photovoltaic cleaning robot is in a working state when performing cleaning work, in order to avoid the risk of damaging the photovoltaic panel components after updating the external parameters of the laser radar and the combined inertial navigation, the external parameters of the laser radar and the combined inertial navigation are not updated.

[0041] If the robot is not in operation, in order to ensure the safety of the robot, in a possible implementation, it is necessary to determine whether there are obstacles within the first preset range of the robot's forward direction based on the laser radar's measurement data. If there are no obstacles within the first preset range of the robot's forward direction, it is determined that the robot meets the preset external parameter update conditions, and the current external parameters of the laser radar and combined inertial navigation are updated to the target external parameters. Among them, the absence of obstacles within the first preset range of the robot's forward direction indicates that there is no danger in updating the external parameters of the laser radar and combined inertial navigation. The first preset range is calibrated according to the robot's application scenario and running speed, for example, it can be 8 meters.

[0042] In a possible implementation, if there is an obstacle within the first preset range of the robot's forward direction, in order to ensure the safety of the robot's operation, it is determined whether the robot meets the preset external parameter update condition, including the following steps 301-303: 301: Using the Kalman filter algorithm, based on the robot's state data at the current moment and the measurement data of the laser radar and the combined inertial navigation, predict the robot's state data at the next moment; 302: Determine whether there is an obstacle within a first preset range in the robot's forward direction according to the state data of the robot at the next moment; If there is an obstacle within the first preset range in the robot's forward direction, return to execution 301; If there is no obstacle within the first preset range of the robot's forward direction, execute 303: determine whether the robot meets the preset external parameter update condition.

[0043] The Kalman filter algorithm is used to predict the robot's state data at the next moment based on the robot's state data at the current moment and the measurement data of the laser radar and the combined inertial navigation, which specifically includes the following steps A1-A4: A1: Define the state vector and measurement vector State Vector , represents the vector matrix of the robot at time t, the state vector At least include: the robot's position P, velocity v, acceleration a, which can be expressed as a vector .

[0044] Measuring vector , represents the vector matrix of the measurement data of the lidar and combined inertial navigation at time t.

[0045] It should be noted that the state vector is the vector representation of the state data of the above robot.

[0046] A2: Prediction state vector

[0047] According to the state vector at time t-1 To predict the state vector at time t , that is, the state vector , the calculation formula is: =A +B , Among them, A represents the coefficient of the state vector changing with time, and B represents the influence coefficient of the control input (such as velocity v, acceleration a, etc.) on the state vector. A and B are set according to the operating state of the robot.

[0048] A3: Forecast Error Covariance

[0049] According to the error covariance matrix at time t-1 To predict the error covariance matrix at the current time t , error covariance Used to represent the uncertainty of the state vector, the calculation formula is: = A +Q, Where Q represents the process noise covariance matrix, which is used to describe the statistical characteristics of the measurement noise. Represented as the transposed matrix of A.

[0050] A4: Update the state vector Calculate Kalman gain : Kalman gain Used to transform the state vector With the measurement vector Fusion to obtain a more accurate state vector estimation result, the calculation formula is: , Among them, H is used to describe the relationship between the measurement vector and the state vector, and R is used to describe the statistical characteristics of the measurement vector noise. It is represented as the transposed matrix of H.

[0051] Then according to the Kalman gain Update the state vector: Updated state vector at time t , the calculation formula is: = + ; Calculate the updated error covariance , the calculation formula is: = (I- ) , According to the state vector of the robot at the current moment and the state vector of the robot at the next moment, the moving direction and moving distance of the robot at the next moment relative to the current moment can be determined, so as to determine whether there is an obstacle within the first preset range of the robot's forward direction at the next moment. If there is an obstacle, the Kalman filter algorithm is continued to be used for cyclic judgment. If there is no obstacle, the robot meets the preset external parameter update conditions, and the current external parameters of the laser radar and the combined inertial navigation are updated to the target external parameters, and the calculation of the Kalman filter algorithm is ended.

[0052] In a possible implementation, in order to avoid the accumulation of external parameter errors caused by the robot not meeting the preset external parameter update conditions for a long time, the external parameters of the laser radar and the combined inertial navigation are updated in time while ensuring the safety of the robot operation. In this embodiment, if there is an obstacle within the first preset range of the robot's forward direction, it is determined whether the duration of the robot not meeting the preset external parameter update conditions is greater than the preset time. If it is greater than the preset time, it is determined whether there is an obstacle within the second preset range of the robot's forward direction according to the state data of the robot at the next moment. If there is an obstacle within the second preset range of the robot's forward direction, it returns to execute the above step 301. If there is no obstacle within the second preset range of the robot's forward direction, it is determined that the robot meets the preset external parameter update conditions, and the current external parameters of the laser radar and the combined inertial navigation are updated to the target external parameters. Among them, the preset time is calibrated according to the actual application scenario, and the second preset range is smaller than the first preset range. For example, the first preset range is 8 meters and the second preset range is 6 meters.

[0053] The present application also provides a controller, including at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute a computer program so that the controller can implement any one of the external parameter calibration methods of the laser radar and combined inertial navigation provided in the embodiments of the present application.

[0054] For example, see Figure 3 The controller shown in FIG. 3 includes a bus 301 , a processor 302 , a communication interface 303 and a memory 304 . The processor 302 , the memory 304 and the communication interface 303 communicate with each other via the bus 302 .

[0055] The bus 301 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0056] The processor 302 may be any one or more of a central processing unit (CPU), a graphics processing unit (GPU), a microprocessor (MP), or a digital signal processor (DSP).

[0057] The memory 304 may include a volatile memory, such as a random access memory (RAM). The memory 304 may also include a non-volatile memory, such as a read-only memory (ROM), a flash memory, a hard disk drive (HDD), or a solid state drive (SSD).

[0058] Among them, the memory 304 can be used to store software codes related to the external parameter calibration method of the laser radar and combined inertial navigation, the processor 302 can execute the steps of the external parameter calibration method of the laser radar and combined inertial navigation, and can also schedule other units to realize corresponding functions.

[0059] The present application also provides a robot, see Figure 4 The robot structure schematic diagram shown in the figure, the robot comprises: a robot body 100, a controller 200, and a laser radar 300 and a combined inertial navigation 400 arranged on the robot body 100; At least one position sensor 500 is provided on the laser radar to detect the position offset of the laser radar; At least one position sensor 500 is provided on the combined inertial navigation system for detecting the position offset of the combined inertial navigation system.

[0060] In a possible implementation, five position sensors are arranged on the laser radar, which are respectively installed in the front, rear, left, right and top of the laser radar housing, and are used to detect the position offset of the laser radar in the front, rear, left, right and top respectively; five position sensors are arranged on the combined inertial navigation, which are respectively installed in the front, rear, left, right and top of the combined inertial navigation housing, and are used to detect the position offset of the combined inertial navigation in the front, rear, left, right and top respectively.

[0061] In a possible implementation, the robot is a photovoltaic cleaning robot.

[0062] An embodiment of the present application also provides a computer program product, including computer-readable instructions. When the computer-readable instructions are executed on a controller, the controller implements any one of the external parameter calibration methods for the laser radar and combined inertial navigation provided in the embodiment of the present application.

[0063] A computer-readable storage medium is also provided in an embodiment of the present application. The storage medium carries one or more computer programs. When the one or more computer programs are executed by a controller, the controller can implement any one of the external parameter calibration methods of the laser radar and combined inertial navigation provided in the embodiment of the present application.

[0064] It should also be noted that the device embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed over multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the scheme of this embodiment. In addition, in the drawings of the device embodiments provided by the present application, the connection relationship between the modules indicates that there is a communication connection between them, which may be specifically implemented as one or more communication buses or signal lines.

[0065] Through the description of the above implementation mode, the technicians in the field can clearly understand that the present application can be implemented by means of software plus necessary general hardware, and of course, it can also be implemented by special hardware including special integrated circuits, special CPUs, special memories, special components, etc. In general, all functions completed by computer programs can be easily implemented by corresponding hardware, and the specific hardware structure used to implement the same function can also be various, such as analog circuits, digital circuits or special circuits. However, for the present application, software program implementation is a better implementation mode in more cases. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a readable storage medium, such as a computer floppy disk, a U disk, a mobile hard disk, a ROM, a RAM, a disk or an optical disk, etc., including a number of instructions to enable a computer device (which can be a personal computer, a training device, or a network device, etc.) to execute the methods described in each embodiment of the present application.

[0066] In the above embodiments, all or part of the embodiments may be implemented by software, hardware, firmware or any combination thereof. When implemented by software, all or part of the embodiments may be implemented in the form of a computer program product.

[0067] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website site, a computer, a training device, or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) mode to another website site, computer, training device, or data center. The computer-readable storage medium may be any available medium that a computer can store or a data storage device such as a training device, a data center, etc. that includes one or more available media integrations. The available medium may be a magnetic medium, (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid-state drive (SSD)), etc.

Claims

1. A method for calibrating external parameters of laser radar and combined inertial navigation, characterized in that: A controller applied to a robot, wherein the robot further comprises a laser radar and a combined inertial navigation system, wherein at least one position sensor is arranged on the laser radar, and at least one position sensor is arranged on the combined inertial navigation system, and an external parameter calibration method of the laser radar and the combined inertial navigation system comprises: Acquire the position offset detected by each of the position sensors; If the position offset detected by at least one of the position sensors is greater than a first threshold, obtaining measurement data of the laser radar and the combined inertial navigation; The target extrinsic parameters of the laser radar and the combined inertial navigation are calculated according to the measurement data of the laser radar and the combined inertial navigation.

2. The external parameter calibration method of laser radar and combined inertial navigation according to claim 1 is characterized in that: The external parameter calibration method of the laser radar and the combined inertial navigation also includes: Recording the accumulated mileage of the robot corresponding to the current external parameters of the laser radar and the combined inertial navigation; If the accumulated mileage is greater than a second threshold, the steps of acquiring measurement data of the laser radar and the combined inertial navigation system and calculating target external parameters of the laser radar and the combined inertial navigation system according to the measurement data of the laser radar and the combined inertial navigation system are executed.

3. The external parameter calibration method of laser radar and combined inertial navigation according to claim 1 is characterized in that: After calculating the target extrinsic parameters of the laser radar and the combined inertial navigation according to the measurement data of the laser radar and the combined inertial navigation, the extrinsic parameter calibration method of the laser radar and the combined inertial navigation further includes: Determining whether the robot meets the preset external parameter update conditions; If the robot meets the preset external parameter update condition, the current external parameters of the laser radar and the combined inertial navigation are updated to the target external parameters.

4. The external parameter calibration method of laser radar and combined inertial navigation according to claim 3 is characterized in that: The determining whether the robot meets a preset external parameter update condition includes: Determining whether the robot is in a working state; If the robot is in a working state, it is determined that the robot does not meet the preset external parameter update condition.

5. The external parameter calibration method of laser radar and combined inertial navigation according to claim 4 is characterized in that: The determining whether the robot meets the preset external parameter update condition also includes: If the robot is in a non-working state, determining whether there is an obstacle within a first preset range of the robot's forward direction according to the measurement data of the laser radar; If there is no obstacle within the first preset range of the robot's forward direction, it is determined that the robot meets the preset external parameter update condition.

6. The external parameter calibration method of laser radar and combined inertial navigation according to claim 5 is characterized in that: The determining whether the robot meets the preset external parameter update condition also includes: If there is an obstacle within the first preset range in the forward direction of the robot, a Kalman filter algorithm is used to predict the state data of the robot at the next moment based on the state data of the robot at the current moment and the measurement data of the laser radar and the combined inertial navigation; Determine whether there is an obstacle within a first preset range in the robot's forward direction according to the state data of the robot at the next moment; If there is an obstacle within the first preset range in the forward direction of the robot, return to the step of using a Kalman filter algorithm to predict the state data of the robot at the next moment based on the state data of the robot at the current moment and the measurement data of the laser radar and the combined inertial navigation; If there is no obstacle within the first preset range of the robot's forward direction, it is determined that the robot meets the preset external parameter update condition.

7. The external parameter calibration method of laser radar and combined inertial navigation according to claim 6 is characterized in that: The determining whether the robot meets the preset external parameter update condition also includes: If there is an obstacle within a first preset range in the robot's forward direction, determining whether the duration for which the robot does not meet the preset external parameter update condition is greater than a preset time; If it is greater than the preset time, determining whether there is an obstacle within a second preset range in the robot's forward direction according to the state data of the robot at the next moment, wherein the second preset range is smaller than the first preset range; If there is an obstacle within the second preset range in the forward direction of the robot, return to the step of using a Kalman filter algorithm to predict the state data of the robot at the next moment based on the state data of the robot at the current moment and the measurement data of the laser radar and the combined inertial navigation; If there is no obstacle within the second preset range of the robot's forward direction, it is determined that the robot meets the preset external parameter update condition.

8. A controller, characterized in that: The method comprises at least one processor and a memory connected to the processor, wherein: The memory is used to store computer programs; The processor is used to execute the computer program so that the controller can implement the external parameter calibration method of the laser radar and combined inertial navigation as described in any one of claims 1 to 7.

9. A robot, characterized in that: include: A robot body, a controller, and a laser radar and a combined inertial navigation system arranged on the robot body; At least one position sensor is provided on the laser radar, for detecting the position offset of the laser radar; At least one position sensor is provided on the combined inertial navigation system for detecting a position offset of the combined inertial navigation system; The controller is used to implement the external parameter calibration method of the laser radar and combined inertial navigation as described in any one of claims 1 to 7.

10. A computer program product, characterized in that It includes computer-readable instructions. When the computer-readable instructions are executed on a controller, the controller implements the external parameter calibration method of the laser radar and combined inertial navigation as described in any one of claims 1 to 7.

Images