Measuring equipment and method for inertial navigation positioning

Abstract

The invention discloses inertial navigation positioning measurement equipment and method, and the method specifically comprises the following steps: S1, a processor of a measurement device obtains acceleration data and angular velocity data from an inertial sensor, and transmits the acceleration data and angular velocity data to a terminal; s2, the terminal analyzes the angular velocity data to obtain attitude information of the measurement device; and S3, obtaining position information under the approximate geodetic coordinate system according to the attitude information and the acceleration data. According to the invention, the acceleration data and the angular velocity data acquired by the measuring device are transmitted to the terminal for calculation of the position information, so that data acquisition and calculation are carried out separately, high calculation power requirements and high power consumption caused by data calculation are avoided, and the power consumption and the size of the measuring device are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the field of measurement technology, and in particular to a measurement device and method for inertial navigation positioning. Background Art

[0002] Traditional measurement methods such as total station measurement, steel ruler measurement, and GPS measurement have many limitations. Total station measurement requires line of sight, that is, the measurement points must be able to see each other, which is often difficult to achieve in complex terrain or environments with obstacles; steel ruler measurement has limited accuracy and a short measurement distance, which is not suitable for large-scale measurement tasks; GPS signals are easily blocked and interfered with, and in environments such as urban canyons and forests, positioning accuracy decreases or even becomes impossible.

[0003] The existing RTK system has discontinuous or delayed reception of RTK satellite signals in scenes with obstructions, such as the foot of a house, under the shade of trees, and on eaves. As a result, the equipment cannot perform normal calculations, and accurate positioning cannot be obtained or the positioning accuracy is very poor. The location of the measured point is difficult to reach or the surveyor cannot directly conduct station measurement. In areas with no signal, a total station is generally used for station measurement. Station measurement requires more than two professional surveyors to operate, and the actual working environment varies greatly. In general, the operation is difficult and takes a long time. The measurement results need to be brought back to the computer terminal for professional processing before they can be mapped, and the measurement results cannot be displayed and confirmed in real time. Summary of the invention

[0004] In view of the problem of low measurement accuracy when there is no RTK signal in the prior art, the present invention proposes a measurement device and method for inertial navigation positioning.

[0005] In order to achieve the above object, the present invention provides the following technical solutions:

[0006] A measuring device for inertial navigation positioning, comprising a measuring device and a terminal; the measuring device comprises a processor, an inertial sensor, a power module, and a communication module; the output end of the inertial sensor is bidirectionally connected to a signal receiving end of the processor, the communication end of the processor is connected to the terminal through the communication module, and the output end of the power module is respectively connected to a power end of the processor and a power end of the inertial sensor;

[0007] The processor is used to process various electrical signals; the inertial sensor is used to sample the actual acceleration and angular velocity; the power module is used to supply power to other modules; the communication module is used for communication between the processor and the terminal.

[0008] Preferably, the model of the processor is ATSAMS70N21; the model of the inertial sensor is BMI270; the model of the power module is LTC3455; and the model of the communication module is nRF52832.

[0009] Preferably, the source module is also provided with a USB-typec interface for charging a lithium battery.

[0010] Preferably, the terminal includes a mobile phone, a tablet, or a computer.

[0011] Preferably, the processor is connected to the inertial sensor via SPI combined with DMA communication.

[0012] The present invention also provides a method for measuring inertial navigation positioning, which specifically comprises the following steps:

[0013] S1: The processor of the measuring device obtains acceleration data and angular velocity data from the inertial sensor and transmits it to the terminal;

[0014] S2: The terminal obtains the attitude information of the measuring device according to the angular velocity data analysis;

[0015] S3: Obtaining position information in an approximate earth coordinate system according to the attitude information and acceleration data.

[0016] Preferably, in S1, the processor asynchronously reads the acceleration data and angular velocity data of the inertial sensor by adopting hardware SPI combined with DMA communication, and the communication baud rate is 10 MHz.

[0017] Preferably, in S2, the time interval for the inertial sensor to collect data is Δt, and the output three-axis angular velocity is ω x ,ω y ,ω z , the quaternion is calculated by the first-order Runge-Kutta formula:

[0018]

[0019] In formula (1), Q(t+Δt) represents the quaternion at time t+Δt; Q(t) represents the quaternion at time t; Δt represents the acquisition time interval; Ω(t) represents the angular velocity matrix at time t; Represents quaternion multiplication;

[0020] in,

[0021]

[0022] Expanding formula (2) yields:

[0023]

[0024] In formula (3), Represents the quaternion at time t+Δt;

[0025] Based on the quaternion at time t, the quaternion at each moment can be obtained according to the time interval Δt between the forward and backward motion states and the angular velocity matrix to represent the posture information.

[0026] Preferably, S3 includes:

[0027] S3-1: First, deduct the effect of gravity on acceleration in the b frame to obtain the acceleration deducted by gravity in the n frame:

[0028]

[0029] In formula (4), Acc n It indicates the acceleration information after deducting gravity in the n system, that is, the three-axis acceleration; represents the rotation matrix, Acc b It represents the acceleration data in the b system; g is the local gravitational acceleration;

[0030] S3-2: Then deduct the acceleration Acc of gravity in the n system n Integrate to get the velocity:

[0031]

[0032] In formula (5), represents the velocity of the n system at time t+Δt; represents the velocity of the n system at time t; Represents the acceleration data of the n system at time t+Δt; represents the acceleration data of the n system at time t; Δt represents the acquisition time interval of the inertial sensor;

[0033] S3-3: Finally, integrate the velocity to obtain the position information:

[0034]

[0035] In formula (6), It represents the position information in the n system at time t+Δt, that is, the coordinates of the measuring device in the n system after moving for Δt time; Represents the position information in the n system at time t, that is, coordinates.

[0036] In summary, due to the adoption of the above technical solution, compared with the prior art, the present invention has at least the following beneficial effects:

[0037] The present invention transmits the acceleration data and angular velocity data obtained by the measuring device to the terminal for calculating the position information, thereby separating the data collection and solving, avoiding the high computing power requirements and high power consumption caused by solving the data, and effectively reducing the power consumption and size of the measuring device. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 It is a schematic diagram of a measuring device for inertial navigation positioning according to an exemplary embodiment of the present invention.

[0039] Figure 2 It is a schematic diagram of a measurement method for inertial navigation positioning according to an exemplary embodiment of the present invention. DETAILED DESCRIPTION

[0040] The present invention is further described in detail below in conjunction with the examples and specific implementation methods. However, this should not be understood as the scope of the above subject matter of the present invention being limited to the following examples, and all technologies realized based on the content of the present invention belong to the scope of the present invention.

[0041] In the description of the present invention, it is necessary to understand that the terms "longitudinal", "lateral", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating the orientation or position relationship, are based on the orientation or position relationship shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation on the present invention.

[0042] like Figure 1 As shown, the present invention provides an inertial navigation and positioning measuring device, including a measuring device and a terminal (mobile phone, tablet, computer, etc.); the measuring device includes a processor, an inertial sensor, a power module, and a communication module.

[0043] In this embodiment, the output end of the inertial sensor is bidirectionally connected to the signal receiving end of the processor, the communication end of the processor is connected to the terminal through the communication module (bidirectional connection), and the output end of the power module is respectively connected to the power end of the processor and the power end of the inertial sensor.

[0044] In this embodiment, the processor (model ATSAMS70N21) is used to process various electrical signals; the inertial sensor can adopt a 6-axis IMU, model BMI270, for sampling the actual acceleration and angular velocity; the power module model can adopt LTC3455, for converting the lithium battery power into the 3.3v voltage required for actual work; the communication module can adopt a Bluetooth module, model nRF52832, for transmitting the acceleration and angular velocity data to the terminal via Bluetooth for solving and obtaining positioning information; the power module is also provided with a Usb-typec interface for charging the lithium battery.

[0045] In this embodiment, when the inertial sensor senses the periodic change of longitudinal acceleration, the power module is started. The purpose of this design is to start the device in a contactless manner, so that it can be easily started and measured in the complex environment of the actual measurement process. After the power module is started, the timed shutdown function is started. When the communication module is not working (that is, not connected to Bluetooth) for more than a certain period of time, it will automatically shut down. The purpose of this design is to realize the shutdown function without operation when the device is accidentally started or the measurement is ended, which reduces the difficulty of operation.

[0046] In this embodiment, the acceleration data and angular velocity data of the inertial sensor collector are transmitted to a tablet, mobile phone or other terminal via Bluetooth for analysis, that is, data collection and analysis are performed separately, avoiding high computing power requirements and high power consumption caused by analyzing the data, and effectively reducing the power consumption and size of the measuring device.

[0047] The high-precision inertial measurement equipment designed by the present invention solves the problem of difficulty in high-precision measurement operation in the absence of satellite signals. In the case of only Bluetooth communication between the measuring device and the flat terminal, an accuracy index of 0.5% is achieved in the cases of 200-meter straight line measurement, 500-meter straight line measurement, 1000-meter straight line measurement and 200-meter closed shape measurement, 500-meter closed shape measurement, and 1000-meter closed shape measurement, thereby achieving easy-to-operate high-precision measurement in the absence of satellite signals.

[0048] like Figure 2 As shown, the present invention provides a measurement method for inertial navigation positioning, which specifically includes the following steps:

[0049] S1: The processor communicates asynchronously and at high speed with the 6-axis inertial sensor to obtain high-precision acceleration data and angular velocity data, and transmits the acceleration data and angular velocity data to the terminal through the communication module for positioning information analysis.

[0050] S2: The terminal obtains the posture information of the measuring device based on the angular velocity data analysis.

[0051] In this embodiment, the initial heading angle is 0, and the attitude information of the device can be obtained by multiplying the angular velocity information at the same time interval.

[0052] Assuming that quaternion Q(q0,q1,q2,q3) is used to represent the posture, the following definition is given:

[0053] Q=q0+q1i+q2j+q3k (1)

[0054] In formula (1), Q represents a quaternion; i, j, and k represent unit vectors that are mutually orthogonal and imaginary units. The specific provisions are reflected in the following quaternion multiplication relationship:

[0055]

[0056] in, Represents quaternion multiplication.

[0057] In this embodiment, it is assumed that the time interval for the inertial sensor to collect data is Δt, and the output three-axis angular velocity is ω x ,ω y ,ω z , then the quaternion can be calculated by the first-order Runge-Kutta formula:

[0058]

[0059] In formula (2), Q(t+Δt) represents the quaternion at time t+Δt; Q(t) represents the quaternion at time tt; Δt represents the acquisition time interval; Ω(t) represents the angular velocity matrix at time t; Represents quaternion multiplication.

[0060] in,

[0061]

[0062] Expanding formula (3) yields:

[0063]

[0064] In formula (4), Represented as the quaternion at time t+Δt.

[0065] From formula (4), we can know that based on the quaternion at time t, the quaternion at each time can be obtained according to the time interval Δt between the previous and next motion states and the angular velocity matrix to represent the attitude information. In three-dimensional space, quaternions can be used to represent rotation or attitude; assuming that an object rotates from an initial attitude to a new attitude, the quaternion Q can describe this rotation.

[0066] S3: Obtain position information in an approximate geodetic coordinate system (only the heading angle and initial coordinates are different, n system) according to the attitude information and acceleration data.

[0067] In this embodiment, the acceleration data is obtained by the measuring device in the carrier coordinate system (b system). In order to calculate the position information in the n system, the acceleration data in the b system needs to be converted to the n system before the position information can be further calculated. Therefore, it needs to be converted into acceleration information in an approximate geodetic coordinate system.

[0068] In this embodiment, the acceleration data obtained in the carrier coordinate system is Acc b =[α x , α y , αz ] T , then the acceleration in the approximate geodetic coordinate system (n system) is:

[0069]

[0070] In formula (5), Acc n Represents the acceleration information in the approximate geodetic coordinate system (n system), that is, the three-axis acceleration; represents the rotation matrix, Acc b Represents the acceleration data in the carrier coordinate system (b system); α x represents the x-axis acceleration in the carrier coordinate system (b system); α y represents the y-axis acceleration in the carrier coordinate system (b system); α z represents the z-axis acceleration in the carrier coordinate system (b system); T represents the transposed matrix.

[0071] in,

[0072]

[0073] Among them, q0, q1, q2, and q3 all represent real numbers.

[0074] In this embodiment, the acceleration Acc in the approximate earth coordinate system (n system) is calculated at the same acquisition time interval Δt. n By integrating, we can get the coordinates of each sampling point in the near-sighted geodetic coordinate system, and then we can get various information.

[0075] First, deduct the effect of gravity on acceleration in the b system to obtain the acceleration after deducting gravity in the n system.

[0076]

[0077] In formula (6), Acc n It represents the acceleration information after deducting gravity in the approximate geodetic coordinate system (n system), that is, the three-axis acceleration; represents the rotation matrix, Acc b It represents the acceleration data in the carrier coordinate system (b system); g is the local gravity acceleration;

[0078] Then, the acceleration Acc after deducting gravity in the n system is n Integrating, we get the velocity:

[0079]

[0080] In formula (7), represents the velocity of the n system at time t+Δt; represents the velocity of the n system at time t; Represents the acceleration data of the n system at time t+Δt; It represents the acceleration data of the n system at time t; Δt represents the collection time interval of the inertial sensor.

[0081] Finally, integrating the velocity gives us the position information:

[0082]

[0083] In formula (8), It represents the position information in the n system at time t+Δt, that is, the coordinates of the measuring device in the n system after moving for Δt time; Represents the position information in the n system at time t, that is, coordinates.

[0084] In the present embodiment, according to the optimal estimation theory, when the measurement result of the same target data of the measuring device satisfies the same normal distribution, the more the number of measurements, the higher the accuracy after data fusion. And multiple measuring devices simultaneously measure the same target data according to the same theory and can also improve the accuracy. In the same batch, different inertial sensors (IMU) have different accuracy, and the high-precision IMU measurement result is more accurate. Therefore, the present invention combines software to design a high-precision inertial measurement algorithm. In the device testing stage, the closed index of the measurement result of the fixed closed path is analyzed by a fixed index (final position information). The high-precision IMU often measures more accurately, and this data is significantly better than other devices, so that the 16 IMUs of the device are screened, and only the devices with accuracy better than the index are selected, and then the data of these devices are optimally estimated and fused, so as to obtain high-precision measurement results.

[0085] In general, the results of inertial sensor measurements follow a Gaussian distribution. Assuming that the true value to be measured is μ, since zero bias is inevitable and will change over time, it can be effectively removed by subsequent algorithms. Therefore, the following discussion does not consider the device zero bias, and the measurement value follows a Gaussian distribution X~N(μ,σ). The fluctuation σ of the same measurement value to be measured reflects the level of measurement accuracy. When two IMUs are used to measure the same device, there are:

[0086] X i ~N(μ,σ i ), X j ~N(μ,σ j ) (9)

[0087] make:

[0088] X e =K i X i +K j X j , K i +K j=1

[0089] at this time

[0090] X e ~N(μ,σ e ),

[0091] When fusing the measurement results of two targets, we hope to obtain the result with the minimum variance. i , σ j Known and K i +K j =1, then the variance value of the fusion result becomes about K i The function of K i Taking the derivative and setting it to 0 gives:

[0092]

[0093] Take the fusion front difference and Compare and make a difference

[0094]

[0095] If the difference is greater than 0, it means that the variance after fusion is smaller than the variance of any single distribution before fusion, that is, the variance becomes smaller after data fusion and the measurement accuracy is higher.

[0096] Using the same method, X1, X2, X3...X n Where X i ~N(μ,σ i ), i=1,2,3...n Gaussian distributions are fused, X e =K1X1+K2X2+...+K n X n ,in

[0097]

[0098] The variance after fusion is

[0099]

[0100] From the above derivation, it can be seen that the variance after fusion is smaller than the variance of any n-1 optimal estimates. In other words, under the fusion mode of optimal estimation, the measurement accuracy gradually increases with the increase of the number of sensors, so the accuracy of the sensor can be effectively improved by increasing the number of sensors.

[0101] In actual measurement, the target data measured by IMU: angular velocity and acceleration are continuous time signals. In actual measurement application scenarios, the amplitude of this signal has a maximum value (IMU range, and data exceeding the range can be regarded as invalid data). According to the Nyquist sampling theorem:

[0102] When discrete sampling is performed on a band-limited signal, only when the sampling frequency is higher than twice its highest frequency (i.e., at least two points are sampled in one cycle) can the original band-limited signal be correctly restored from the sampled signal. Here, twice the highest frequency is called the Nyquist frequency. If the sampling frequency does not meet this condition, the original signal spectrum will produce spectrum aliasing, and the original signal cannot be correctly restored.

[0103] Therefore, the IMU requires a sufficiently high sampling frequency. This design adopts a higher IMU sampling frequency and shortens the interval for MCU to read the IMU, effectively increasing the sampling frequency of the real inertial data, thereby capturing the high-frequency components of the data to be measured, increasing the IMU sampling frequency and improving the reading speed of the IMU data.

[0104] In this embodiment, by adopting hardware spi combined with DMA communication, the processor reads the inertial navigation data asynchronously and at high speed, effectively eliminating the situation where the MCU cannot read the IMU data in time when it is occupied by a task. At the same time, the communication baud rate is 10MHz, which ensures high-speed data reading, and can effectively avoid the misalignment of the IMU sampling data and the IMU data read by the MCU, thereby improving the measurement accuracy.

[0105] The specific principle of the processor asynchronously reading the inertial navigation data in the inertial sensor at high speed is as follows:

[0106] 1. Sensor data acquisition: Inertial sensors (such as accelerometers, gyroscopes, etc.) are usually connected to the processor through SPI, and the sensors periodically send the collected sensor data.

[0107] 2. SPI data transmission: The processor exchanges data with the sensor through the SPI protocol. The processor sends a request through MOSI, and the sensor transmits the collected sensor data through MISO.

[0108] 3. DMA data transfer: In traditional SPI transmission, the CPU needs to read each received data byte, while using DMA can directly transfer data from the SPI peripheral buffer to the specified location in the memory, completely eliminating the need for CPU intervention. In this way, the CPU can perform other computing tasks while transmitting data, improving the overall performance of the system.

[0109] In this embodiment, high-precision inertial measurement is achieved through the above two methods (calculation and communication of position information), that is, high-precision measurement can be easily achieved in areas without RTK signals.

[0110] Those skilled in the art will appreciate that the above-mentioned embodiments are specific examples for implementing the present invention, and in actual applications, various changes may be made thereto in form and detail without departing from the spirit and scope of the present invention.

Claims

1. A measuring device for inertial navigation positioning, characterized in that: The device comprises a measuring device and a terminal; the measuring device comprises a processor, an inertial sensor, a power module, and a communication module; the output end of the inertial sensor is bidirectionally connected to the signal receiving end of the processor, the communication end of the processor is connected to the terminal through the communication module, and the output end of the power module is respectively connected to the power end of the processor and the power end of the inertial sensor; The processor is used to process various electrical signals; the inertial sensor is used to sample the actual acceleration and angular velocity; The power module is used to supply power to other modules; the communication module is used for communication between the processor and the terminal.

2. The inertial navigation positioning measuring device according to claim 1, characterized in that: The model of the processor is ATSAMS70N21; the model of the inertial sensor is BMI270; the model of the power module is LTC3455; and the model of the communication module is nRF52832.

3. The inertial navigation positioning measuring device according to claim 1, characterized in that: The source module is also provided with a USB-typec interface for charging the lithium battery.

4. The inertial navigation positioning measuring device according to claim 1, characterized in that: The terminals include mobile phones, tablets, and computers.

5. The inertial navigation positioning measuring device according to claim 1, characterized in that: The processor is connected to the inertial sensor through SPI combined with DMA communication.

6. A method for measuring inertial navigation positioning based on the measuring device according to any one of claims 1 to 5, characterized in that: The specific steps include: S1: The processor of the measuring device obtains acceleration data and angular velocity data from the inertial sensor and transmits it to the terminal; S2: The terminal obtains the attitude information of the measuring device according to the angular velocity data analysis; S3: Obtaining position information in an approximate earth coordinate system according to the attitude information and acceleration data.

7. The inertial navigation positioning measurement method according to claim 6, characterized in that: In S1, the processor asynchronously reads the acceleration data and angular velocity data of the inertial sensor by using hardware SPI combined with DMA communication, and the communication baud rate is 10 MHz.

8. The inertial navigation positioning measurement method according to claim 6, characterized in that: In S2, the time interval for the inertial sensor to collect data is Δt, and the output three-axis angular velocity is ω x ,ω y ,ω z , the quaternion is calculated by the first-order Runge-Kutta formula: In formula (1), Q(t+Δt) represents the quaternion at time t+Δt; Q(t) represents the quaternion at time t; Δt represents the acquisition time interval; Ω(t) represents the angular velocity matrix at time t; Represents quaternion multiplication; in, Expanding formula (2) yields: In formula (3), Represents the quaternion at time t+Δt; Based on the quaternion at time t, the quaternion at each moment can be obtained according to the time interval Δt between the forward and backward motion states and the angular velocity matrix to represent the posture information.

9. The inertial navigation positioning measurement method according to claim 6, characterized in that: The S3 includes: S3-1: First, deduct the effect of gravity on acceleration in the b frame to obtain the acceleration deducted by gravity in the n frame: In formula (4), Acc n It indicates the acceleration information after deducting gravity in the n system, that is, the three-axis acceleration; represents the rotation matrix, Acc b It represents the acceleration data in the b system; g is the local gravitational acceleration; S3-2: Then deduct the acceleration Acc of gravity in the n system n Integrate to get the velocity: In formula (5), represents the velocity of the n system at time t+Δt; represents the velocity of the n system at time t; Represents the acceleration data of the n system at time t+Δt; represents the acceleration data of the n system at time t; Δt represents the acquisition time interval of the inertial sensor; S3-3: Finally, integrate the velocity to obtain the position information: In formula (6), It represents the position information in the n system at time t+Δt, that is, the coordinates of the measuring device in the n system after moving for Δt time; Represents the position information in the n system at time t, that is, coordinates.

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