An infrared target ranging and velocity measuring method, device, equipment and storage medium
By using infrared thermal horizon and Kalman filter fusion technology, the problem of poor environmental adaptability of infrared target ranging and velocity measurement at night or in severe weather conditions has been solved, and high-precision target distance and velocity measurement has been achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHIZI AUTOMOTIVE TECHNOLOGY CO LTD
- Filing Date
- 2026-04-29
- Publication Date
- 2026-05-29
AI Technical Summary
Existing infrared target ranging and velocity measurement technologies have poor environmental adaptability at night or in adverse weather conditions, rely on visible light ranging which results in low accuracy, and IMU data drift affects ranging accuracy.
By acquiring infrared images and inertial measurement unit (IMU) data, the camera pitch angle is calculated using infrared thermo-horizontal difference, and data fusion is performed using Kalman filtering and complementary filtering to correct the IMU pitch angle error, thereby achieving target distance and velocity measurement.
It improves the stability and reliability of measurement results at night or in adverse weather conditions, and enhances the accuracy of distance and speed measurement.
Smart Images
Figure CN122110074A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of target tracking and ranging technology, specifically to an infrared target ranging and velocity measurement method, device, equipment, and storage medium. Background Technology
[0002] Infrared target ranging and velocity measurement technology is a key technology that utilizes infrared detectors to acquire and analyze infrared radiation signals emitted or reflected by targets, thereby measuring target distance and speed. Compared to visible light, the infrared band has significant advantages in penetrating smoke, rain, fog, and low-light environments, enabling it to operate stably at night, in adverse weather conditions, and in complex battlefield environments. In recent years, with the continuous improvement of the sensitivity of infrared imaging devices and the ongoing development of signal processing algorithms, infrared ranging and velocity measurement technology has been widely applied in fields such as autonomous driving, border surveillance, drone obstacle avoidance, and precision guidance.
[0003] In existing technologies, such as CN119779279A, a long-range target ranging method based on a visible light camera combined with an IMU is proposed. However, this method has the following limitations: First, its application scenarios are limited. Since it relies on visible light images for ranging, it is only suitable for environments with good lighting conditions and is difficult to work effectively at night or in adverse weather conditions. Second, this method mainly relies on the IMU to directly obtain image pose information, failing to fully utilize the geometric and texture features inherent in the image itself to assist in pose estimation. This results in a significant limitation on ranging accuracy when there is drift or noise in the IMU data. Summary of the Invention
[0004] To address the problems of severe visible light dependence, poor environmental adaptability, and low anti-interference and ranging accuracy due to insufficient utilization of image features in existing infrared detection methods, this invention provides an infrared target ranging and velocity measurement method, apparatus, device, and storage medium. The technical solution is as follows:
[0005] According to a first aspect of the present invention, an infrared target ranging and velocity measurement method is provided, the method comprising the following steps:
[0006] The system acquires infrared images and data from an inertial measurement unit (IMU), which includes an accelerometer and a gyroscope.
[0007] The infrared temperature difference horizon is extracted based on the infrared image, and the pitch angle of the first camera is calculated based on the infrared temperature difference horizon.
[0008] Based on the data from the IMU, the pitch angle of the second camera is estimated using Kalman filtering;
[0009] The first camera pitch angle and the second camera pitch angle are fused by complementary filtering to obtain the fused camera pitch angle;
[0010] Based on the fused camera pitch angle, the target's distance and velocity are estimated using Kalman filtering.
[0011] The infrared target ranging and velocity measurement method provided by this invention first acquires infrared images and data from an inertial measurement unit (IMU), which includes an accelerometer and a gyroscope. Then, it extracts an infrared thermo-horizontal horizon from the infrared image and calculates the first camera pitch angle based on this horizon. Next, based on the IMU data, it estimates the second camera pitch angle using Kalman filtering. The first and second camera pitch angles are then fused using complementary filtering to obtain a fused camera pitch angle. Finally, based on the fused camera pitch angle, the target's distance and velocity are estimated using Kalman filtering. This method, through joint processing of IMU data and infrared image data, utilizes the infrared thermo-horizontal horizon to correct IMU pitch angle errors. It enables target distance and velocity measurement under conditions independent of visible light, such as at night, in situations with missing lane lines, or in adverse weather conditions, thus improving the stability and reliability of the measurement results.
[0012] As a further aspect of the present invention: the extraction of the infrared temperature difference horizon based on infrared images specifically includes:
[0013] Scan each column of the infrared image in the vertical direction, use the Sobel algorithm to find the location of the maximum temperature gradient, and record it row by row as candidate heights;
[0014] The candidate heights are then subjected to median filtering for noise reduction.
[0015] The infrared thermoelectric horizon is obtained by fitting the denoised candidate height using the RANSAC algorithm.
[0016] The method of this invention uses the Sobel algorithm to scan and extract the maximum temperature gradient in the vertical direction, combined with median filtering to remove noise interference, and uses the RANSAC algorithm for fitting. It can stably extract the temperature difference horizon between the sky and the ground from the infrared image, and reduce the influence of non-horizon objects such as tree branches and street lights.
[0017] As a further aspect of the present invention: the calculation of the first camera pitch angle based on the infrared thermal horizon specifically includes:
[0018] Obtain the infrared temperature difference horizon pixel height in the infrared image;
[0019] Based on the pixel height, the first camera pitch angle is calculated using a first formula, which includes:
[0020] (1)
[0021] Where, θ camLet c be the pitch angle of the first camera. y Let y be the coordinate of the principal point in the image coordinate system. img y is the y-axis coordinate of the infrared temperature difference horizon pixel in the infrared image, and f is the camera focal length.
[0022] The method of this invention calculates the camera pitch angle using the arctangent function based on the pixel height of the infrared thermal horizon in the image, converting the horizon position information in the image into a specific angle value, thus providing a basis for subsequent angle fusion.
[0023] As a further aspect of the present invention: the estimation of the second camera pitch angle based on the IMU data using Kalman filtering specifically includes:
[0024] Read the current angular velocity data of the gyroscope and the data of the accelerometer;
[0025] Obtain the zero bias estimate of the gyroscope after the Kalman filter update at the previous time step;
[0026] Initialize the Kalman filter's attitude, covariance matrix, and noise parameters;
[0027] The zero-bias estimate from the previous moment is used to compensate the current angular velocity data of the gyroscope, and the compensated angular velocity data is used as the prediction step for Kalman filtering prediction.
[0028] Determine whether the magnitude of the accelerometer reading is within a reasonable range;
[0029] If yes, then Kalman filtering is performed using the accelerometer data as the update step; otherwise, no update is performed.
[0030] Extract the second camera pitch angle and the updated zero-bias estimate of the gyroscope from the updated state vector.
[0031] The method of this invention performs zero-bias estimation and compensation of the gyroscope when estimating the pitch angle of the IMU, and judges in real time whether the accelerometer data is within a reasonable range. Kalman filtering is only performed when the data is valid to avoid dynamic acceleration interference and improve the accuracy of pitch angle estimation.
[0032] As a further aspect of the present invention: the first camera pitch angle and the second camera pitch angle are fused by complementary filtering to obtain the fused camera pitch angle, which is then calculated using a second formula, the second formula including:
[0033]
[0034] Where θ is the fused camera pitch angle, θ imuLet θ be the pitch angle of the second camera. cam Let α be the pitch angle of the first camera, and α be the weighting parameter.
[0035] The method of this invention fuses the pitch angle calculated by infrared thermal horizon and the pitch angle estimated by IMU through complementary filtering, which can comprehensively utilize the long-term stability of infrared features and the short-term accuracy of IMU to obtain a more accurate camera pitch angle than a single sensor.
[0036] As a further aspect of the present invention: the dynamic estimation of the target's distance and velocity based on the fused camera pitch angle using Kalman filtering specifically includes:
[0037] Set the target's state vector ,in Let k be the target distance. Let k be the target velocity at time k;
[0038] Construct an observation model to obtain distance observations calculated based on the fused camera pitch angle;
[0039] Based on the state estimate of the previous time step, obtain the predicted value of the state vector at the current time step;
[0040] The prediction results are corrected using the distance observations, the Kalman gain is calculated, the state estimate is updated, and the weights of the prediction values and the distance observations are assigned.
[0041] Output the target's distance and speed as updated at the current moment;
[0042] The distance observation is calculated using a third formula, which includes:
[0043] z k = H / tan(θ) (3)
[0044] Among them, z k For distance observations, H is the camera height, and θ is the fused camera pitch angle.
[0045] The method of this invention sets up a state vector containing target distance and velocity, calculates the target distance using the fused camera pitch angle as the observation, and performs prediction and update through Kalman filtering, which can output smooth distance estimates and real-time velocity estimates.
[0046] As a further aspect of the present invention: in the step of obtaining the predicted value of the state vector at the current moment based on the state estimation at the previous moment, the state transition matrix of the Kalman filter is: Where Δt is the time interval, w k This is process noise.
[0047] The method of this invention uses a state transition matrix in Kalman filtering to describe the evolution of the target's motion state over time, which makes the distance and velocity prediction process have a clear model and helps to improve the performance of distance and velocity estimation under brief occlusion or measurement noise.
[0048] According to a second aspect of the present invention, an infrared target ranging and velocity measuring device is provided, comprising: an acquisition module, a calculation module, an attitude estimation module, a fusion module, and a state estimation module;
[0049] The acquisition module is used to acquire infrared images and data from the inertial measurement unit (IMU), which includes an accelerometer and a gyroscope.
[0050] The calculation module is used to extract the infrared temperature difference horizon based on the infrared image and calculate the first camera pitch angle based on the infrared temperature difference horizon.
[0051] The attitude estimation module is used to estimate the pitch angle of the second camera based on the data from the IMU using Kalman filtering;
[0052] The fusion module is used to fuse the first camera pitch angle and the second camera pitch angle through complementary filtering to obtain the fused camera pitch angle;
[0053] The state estimation module is used to estimate the target's distance and velocity using Kalman filtering based on the fused camera pitch angle.
[0054] The infrared target ranging and velocity measuring device provided by this invention includes: an acquisition module, a calculation module, an attitude estimation module, a fusion module, and a state estimation module. The acquisition module acquires infrared images and data from an inertial measurement unit (IMU), which includes an accelerometer and a gyroscope. The calculation module extracts an infrared temperature difference horizon from the infrared image and calculates the first camera pitch angle based on the infrared temperature difference horizon. The attitude estimation module estimates the second camera pitch angle based on the IMU data using Kalman filtering. The fusion module fuses the first and second camera pitch angles using complementary filtering to obtain a fused camera pitch angle. The state estimation module estimates the target's distance and velocity based on the fused camera pitch angle using Kalman filtering. This device acquires infrared images and IMU data through the acquisition module, which are then processed sequentially by the calculation, attitude estimation, fusion, and state estimation modules. It uses the temperature difference information at the horizon in the infrared image to correct the IMU pitch angle error, enabling distance and velocity measurement of targets under conditions independent of visible light, such as at night, in situations with missing lane lines, or in adverse weather conditions. This improves the stability, reliability, and accuracy of the measurement results.
[0055] According to a third aspect of the present invention, an infrared target ranging and velocity measuring device is provided, the infrared target ranging and velocity measuring device comprising a processor and a memory, the memory storing at least one computer instruction, the instruction being loaded and executed by the processor to implement the steps performed in the infrared target ranging and velocity measuring method described above.
[0056] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the storage medium storing at least one computer instruction, the instruction being loaded and executed by a processor to perform the steps performed in the infrared target ranging and velocimetry method described in any of the preceding claims.
[0057] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit the invention. Attached Figure Description
[0058] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with the invention and, together with the description, serve to explain the principles of the invention.
[0059] Figure 1 This is a flowchart of the infrared target ranging and velocity measurement method provided in the embodiments of the present invention;
[0060] Figure 2 This is a flowchart of the infrared target ranging and velocity measurement method provided in the embodiments of the present invention;
[0061] Figure 3 This is a flowchart of IMU estimation of the second camera pitch angle provided in an embodiment of the present invention;
[0062] Figure 4 This is a schematic diagram of the visual imaging geometry principle for constructing an observation model provided in an embodiment of the present invention;
[0063] Figure 5 This is a structural diagram of the infrared target ranging and velocity measuring device provided in an embodiment of the present invention. Detailed Implementation
[0064] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present invention.
[0065] This invention provides an infrared target ranging and velocity measurement method, such as... Figure 1 , Figure 2 As shown, it includes the following steps:
[0066] Step 101: Acquire infrared images and data from the inertial measurement unit (IMU).
[0067] Specifically, the inertial measurement unit includes accelerometers and gyroscopes.
[0068] Step 102: Extract the infrared temperature difference horizon based on the infrared image, and calculate the pitch angle of the first camera based on the infrared temperature difference horizon.
[0069] It should be noted that this embodiment creatively utilizes the significant temperature difference between the sky and the ground (or buildings) in infrared images to construct a stable sky-ground boundary curve. This curve can still be stably extracted under conditions such as day and night, no lane markings, and severe weather. By extracting the vertical position change of the horizon due to temperature difference, the camera's pitch angle can be derived in real time.
[0070] In one embodiment, extracting the infrared temperature difference horizon based on an infrared image specifically includes:
[0071] In each column of the vertically scanned infrared image, the Sobel algorithm was used to find the location of the maximum temperature gradient, and the position was recorded row by row as a candidate height.
[0072] Median filtering is applied to the candidate heights to reduce noise.
[0073] The RANSAC algorithm was used to fit the denoised candidate heights to obtain the infrared thermoelectric horizon.
[0074] In this embodiment, median filtering is performed on the candidate heights to remove some interfering information such as tree branches and streetlights.
[0075] The method of this invention uses the Sobel algorithm to scan and extract the maximum temperature gradient in the vertical direction, combined with median filtering to remove noise interference, and uses the RANSAC algorithm for fitting. It can stably extract the temperature difference horizon between the sky and the ground from the infrared image, and reduce the influence of non-horizon objects such as tree branches and street lights.
[0076] In one embodiment, calculating the first camera pitch angle based on the infrared thermal horizon specifically includes:
[0077] Obtain the pixel height of the infrared temperature difference horizon in the infrared image;
[0078] Based on pixel height, the first camera pitch angle is calculated using a first formula, which includes:
[0079] (1)
[0080] Where, θ cam Let c be the pitch angle of the first camera. y Let y be the coordinate of the principal point in the image coordinate system.img y is the y-axis coordinate of the infrared temperature difference horizon pixel in the infrared image, and f is the camera focal length.
[0081] The method of this invention calculates the camera pitch angle using the arctangent function based on the pixel height of the infrared thermal horizon in the image, converting the horizon position information in the image into a specific angle value, thus providing a basis for subsequent angle fusion.
[0082] Step 103: Based on the IMU data, estimate the pitch angle of the second camera using Kalman filtering.
[0083] It should be noted that accelerometers exhibit high stability over long-term measurements but are sensitive to vehicle dynamic acceleration; gyroscopes are accurate in the short term but suffer from long-term drift. To overcome the limitations of single sensors, this invention creatively employs a Kalman filter fusion algorithm to combine the long-term stability of the accelerometer with the short-term accuracy of the gyroscope, outputting the optimal pitch angle estimate.
[0084] In one embodiment, the pitch angle of the second camera is estimated using Kalman filtering based on IMU data, specifically including:
[0085] Read the current angular velocity data from the gyroscope and the data from the accelerometer;
[0086] Obtain the zero bias estimate of the gyroscope after the Kalman filter update at the previous time step;
[0087] Initialize the Kalman filter's attitude, covariance matrix, and noise parameters;
[0088] The zero-bias estimate from the previous moment is used to compensate the current angular velocity data of the gyroscope, and the compensated angular velocity data is used as the prediction step for Kalman filtering prediction.
[0089] Determine whether the modulus of the accelerometer reading is within a reasonable range;
[0090] If yes, then Kalman filtering is performed using the accelerometer data as the update step; otherwise, no update is performed.
[0091] Extract the second camera pitch angle and the updated gyroscope zero-bias estimate from the updated state vector.
[0092] In practical use, the reasonable range for the modulus of accelerometer readings is 0.5g to 1.5g. Readings exceeding this range are considered unreasonable. The specific procedure is as follows: Figure 3 As shown.
[0093] The method of this invention performs zero-bias estimation and compensation of the gyroscope when estimating the pitch angle of the IMU, and judges in real time whether the accelerometer data is within a reasonable range. Kalman filtering is only performed when the data is valid to avoid dynamic acceleration interference and improve the accuracy of pitch angle estimation.
[0094] Step 104: Combine the pitch angles of the first camera and the second camera using complementary filtering to obtain the combined camera pitch angle.
[0095] In this embodiment, the pitch angle estimated by the IMU and the pitch angle calculated by the infrared thermo-horizontal difference are fused by complementary filtering and used as the observation input of the secondary Kalman filter to perform range and velocity measurement on the infrared target.
[0096] In one embodiment, the first camera pitch angle and the second camera pitch angle are fused using complementary filtering to obtain the fused camera pitch angle, which is then calculated using a second formula, the second formula including:
[0097]
[0098] Where θ is the fused camera pitch angle, θ imu Let θ be the pitch angle of the second camera. cam Let α be the pitch angle of the first camera, and α be the weighting parameter.
[0099] In practical use, the value of α ranges from 0 to 1. Furthermore, the larger the value of α, the more certain the second camera's pitch angle is; the smaller the value of α, the more certain the first camera's pitch angle is.
[0100] The method of this invention fuses the pitch angle calculated by infrared thermal horizon and the pitch angle estimated by IMU through complementary filtering, which can comprehensively utilize the long-term stability of infrared features and the short-term accuracy of IMU to obtain a more accurate camera pitch angle than a single sensor.
[0101] Step 105: Based on the fused camera pitch angle, estimate the target's distance and velocity using Kalman filtering.
[0102] In one embodiment, based on the fused camera pitch angle, the target's distance and velocity are dynamically estimated using Kalman filtering, specifically including:
[0103] Set the target's state vector ,in Let k be the target distance. Let k be the target velocity at time k;
[0104] Construct an observation model to obtain distance observations calculated based on the fused camera pitch angle;
[0105] Based on the state estimate of the previous time step, obtain the predicted value of the state vector at the current time step;
[0106] The prediction results are corrected using distance observations, the Kalman gain is calculated, the state estimate is updated, and the weights of the predictions and distance observations are assigned.
[0107] Output the target's distance and speed as updated at the current moment;
[0108] The distance observation is calculated using a third formula, which includes:
[0109] z k = H / tan(θ) (3)
[0110] Among them, z k For distance observations, H is the camera height, and θ is the fused camera pitch angle.
[0111] In practical use, such as Figure 4 The diagram shown illustrates the visual imaging geometry used to construct the observation model. Based on the geometric principles of imaging, assuming the ground is flat, the longitudinal distance z of the observed target is... k This can be calculated using the formula above. Furthermore, the specific update process includes: predicting the current distance and velocity based on the state of the previous moment; the update phase uses... The prediction is corrected, and the Kalman gain automatically assigns weights to the predicted and measured values; the output is a smooth distance estimate and a real-time velocity estimate.
[0112] The method of this invention sets up a state vector containing target distance and velocity, calculates the target distance using the fused camera pitch angle as the observation, and performs prediction and update through Kalman filtering, which can output smooth distance estimates and real-time velocity estimates.
[0113] In one embodiment, in the step of obtaining the predicted state vector value at the current time based on the state estimate of the previous time step, the state transition matrix of the Kalman filter is: Where Δt is the time interval, w k This is process noise.
[0114] The method of this invention uses a state transition matrix in Kalman filtering to describe the evolution of the target's motion state over time, which makes the distance and velocity prediction process have a clear model and helps to improve the performance of distance and velocity estimation under brief occlusion or measurement noise.
[0115] The infrared target ranging and velocity measurement method provided in this invention first acquires infrared images and data from an inertial measurement unit (IMU), which includes an accelerometer and a gyroscope. Then, it extracts an infrared thermo-horizontal horizon from the infrared image and calculates the first camera pitch angle based on the infrared thermo-horizontal horizon. Next, based on the IMU data, it estimates the second camera pitch angle using Kalman filtering. The first and second camera pitch angles are then fused using complementary filtering to obtain a fused camera pitch angle. Finally, based on the fused camera pitch angle, the target distance and velocity are estimated using Kalman filtering. This invention, through joint processing of IMU data and infrared image data, utilizes the infrared thermo-horizontal horizon to correct IMU pitch angle errors. This enables target distance and velocity measurement under conditions independent of visible light, such as at night, in situations with missing lane lines, or in adverse weather conditions, improving the stability and reliability of the measurement results.
[0116] In summary, this invention is the first to propose the concept of "infrared temperature difference horizon," which utilizes the temperature distribution difference between the sky and the ground (or buildings) in infrared images to provide a stable reference for camera attitude estimation and target ranging in infrared scenes. This method does not rely on lane lines or explicit landmarks; by detecting the vertical position change of the horizon region in the infrared image, it directly derives the camera pitch angle, thus providing a reliable and independent source of information for vehicle attitude perception even in conditions where lane lines are missing or the environment is complex.
[0117] Based on this, this invention designs a two-stage Kalman filter structure, which jointly models the target position information extracted from infrared images with IMU inertial data to achieve high-precision dynamic estimation of target distance and velocity. By using the pitch angle calculated from the temperature difference horizon as an independent observation and fusing it with the pitch angle output by the IMU, the cumulative error of the IMU and the drift or noise interference of a single sensor are effectively suppressed. This multi-sensor fusion strategy overcomes the limitations of relying solely on IMU or infrared data, significantly improving the stability, accuracy, and overall system reliability of target ranging and velocity measurement at night, in adverse weather conditions, and under complex road conditions.
[0118] Based on the above Figure 1 The infrared target ranging and velocity measurement method described in the corresponding embodiments is described below as an embodiment of the device of the present invention, which can be used to execute the embodiment of the method of the present invention.
[0119] The infrared target ranging and velocity measuring device provided in this embodiment of the invention, such as Figure 5 As shown, the device includes: an acquisition module 201, a calculation module 202, an attitude estimation module 203, a fusion module 204, and a state estimation module 205;
[0120] The acquisition module 201 is used to acquire infrared images and data from the inertial measurement unit (IMU), which includes an accelerometer and a gyroscope.
[0121] The calculation module 202 is used to extract the infrared temperature difference horizon based on the infrared image and calculate the first camera pitch angle based on the infrared temperature difference horizon;
[0122] Attitude estimation module 203 is used to estimate the pitch angle of the second camera based on IMU data through Kalman filtering;
[0123] The fusion module 204 is used to fuse the first camera pitch angle and the second camera pitch angle through complementary filtering to obtain the fused camera pitch angle;
[0124] The state estimation module 205 is used to estimate the target's range and velocity using Kalman filtering based on the fused camera pitch angle.
[0125] The infrared target ranging and velocity measuring device provided in this embodiment of the invention includes: an acquisition module 201, a calculation module 202, an attitude estimation module 203, a fusion module 204, and a state estimation module 205; the acquisition module 201 acquires infrared images and data from an inertial measurement unit (IMU), the IMU including an accelerometer and a gyroscope; the calculation module 202 extracts an infrared temperature difference horizon based on the infrared image and calculates a first camera pitch angle based on the infrared temperature difference horizon; the attitude estimation module 203 estimates a second camera pitch angle based on the IMU data using Kalman filtering; the fusion module 204 fuses the first camera pitch angle and the second camera pitch angle using complementary filtering to obtain a fused camera pitch angle; the state estimation module 205 estimates the target's distance and velocity based on the fused camera pitch angle using Kalman filtering. The device of this invention acquires infrared images and IMU data, and uses the temperature difference information at the horizon in the infrared image to correct the pitch angle error of the IMU. It can measure the distance and speed of the target under conditions that do not rely on visible light, such as at night, when lane lines are missing or in bad weather, thereby improving the stability, reliability and accuracy of the measurement results.
[0126] Based on the above Figure 1 In addition to the infrared target ranging and velocity measurement method described in the corresponding embodiments, another embodiment of the present invention also provides an infrared target ranging and velocity measurement device. This device includes a processor and a memory, wherein the memory stores at least one computer instruction, which is loaded and executed by the processor to implement the above-described method. Figure 1 The infrared target ranging and velocity measurement method described in the corresponding embodiments.
[0127] Based on the above Figure 1In addition to the infrared target ranging and velocity measurement method described in the corresponding embodiments, this invention also provides a computer-readable storage medium. For example, a non-transitory computer-readable storage medium may be a read-only memory (ROM), a random access memory (RAM), a CD-ROM, magnetic tape, a floppy disk, or an optical data storage system. This storage medium stores at least one computer instruction for executing the above-described... Figure 1 The infrared target ranging and velocity measurement methods described in the corresponding embodiments will not be repeated here.
[0128] Other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the disclosure herein. This application is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of the invention are indicated by the claims.
[0129] It should be understood that the present invention is not limited to the precise structure described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.
Claims
1. An infrared target ranging and velocity measurement method, characterized in that, The method includes the following steps: The system acquires infrared images and data from an inertial measurement unit (IMU), which includes an accelerometer and a gyroscope. The infrared temperature difference horizon is extracted based on the infrared image, and the pitch angle of the first camera is calculated based on the infrared temperature difference horizon. Based on the data from the IMU, the pitch angle of the second camera is estimated using Kalman filtering; The first camera pitch angle and the second camera pitch angle are fused by complementary filtering to obtain the fused camera pitch angle; Based on the fused camera pitch angle, the target's distance and velocity are estimated using Kalman filtering.
2. The infrared target ranging and velocity measurement method according to claim 1, characterized in that, The extraction of the infrared temperature difference horizon based on the infrared image specifically includes: Scan each column of the infrared image in the vertical direction, use the Sobel algorithm to find the location of the maximum temperature gradient, and record it row by row as candidate heights; The candidate heights are then subjected to median filtering for noise reduction. The infrared thermoelectric horizon is obtained by fitting the denoised candidate height using the RANSAC algorithm.
3. The infrared target ranging and velocity measurement method according to claim 1, characterized in that, The calculation of the first camera pitch angle based on the infrared thermal horizon specifically includes: Obtain the infrared temperature difference horizon pixel height in the infrared image; Based on the pixel height, the first camera pitch angle is calculated using a first formula, which includes: （1） Where, θ cam Let c be the pitch angle of the first camera. y The y-coordinate of the principal point in the image coordinate system. y is the y-axis coordinate of the infrared temperature difference horizon pixel in the infrared image, and f is the camera focal length.
4. The infrared target ranging and velocity measurement method according to claim 1, characterized in that, The estimation of the second camera's pitch angle using Kalman filtering based on the IMU data specifically includes: Read the current angular velocity data of the gyroscope and the data of the accelerometer; Obtain the zero bias estimate of the gyroscope after the Kalman filter update at the previous time step; Initialize the Kalman filter's attitude, covariance matrix, and noise parameters; The zero-bias estimate from the previous moment is used to compensate the current angular velocity data of the gyroscope, and the compensated angular velocity data is used as the prediction step for Kalman filtering prediction. Determine whether the magnitude of the accelerometer reading is within a reasonable range; If yes, then Kalman filtering is performed using the accelerometer data as the update step; otherwise, no update is performed. Extract the second camera pitch angle and the updated zero-bias estimate of the gyroscope from the updated state vector.
5. The infrared target ranging and velocity measurement method according to claim 1, characterized in that, The first camera pitch angle and the second camera pitch angle are fused using complementary filtering to obtain the fused camera pitch angle, which is then calculated using a second formula, the second formula including: （2） Where θ is the fused camera pitch angle, θ imu Let θ be the pitch angle of the second camera. cam Let α be the pitch angle of the first camera, and α be the weighting parameter.
6. The infrared target ranging and velocity measurement method according to claim 1, characterized in that, The estimation of the target's distance and velocity using Kalman filtering based on the fused camera pitch angle specifically includes: Set the target's state vector ,in Let k be the target distance at time k. Let k be the target velocity at time k; Construct an observation model to obtain distance observations calculated based on the fused camera pitch angle; Based on the state estimate of the previous time step, obtain the predicted value of the state vector at the current time step; The prediction results are corrected using the distance observations, the Kalman gain is calculated, the state estimate is updated, and the weights of the prediction values and the distance observations are assigned. Output the target's distance and speed as updated at the current moment; The distance observation is calculated using a third formula, which includes: z k = H / tan(θ) (3) Among them, z k For distance observations, H is the camera height, and θ is the fused camera pitch angle.
7. The infrared target ranging and velocity measurement method according to claim 6, characterized in that, In the step of obtaining the predicted state vector value at the current moment based on the state estimation of the previous moment, the state transition matrix of the Kalman filter is: Where Δt is the time interval, w k This is process noise.
8. An infrared target ranging and velocity measuring device, characterized in that, include: The system includes a data acquisition module, a calculation module, an attitude estimation module, a fusion module, and a state estimation module. The acquisition module is used to acquire infrared images and data from the inertial measurement unit (IMU), which includes an accelerometer and a gyroscope. The calculation module is used to extract the infrared temperature difference horizon based on the infrared image and calculate the first camera pitch angle based on the infrared temperature difference horizon. The attitude estimation module is used to estimate the pitch angle of the second camera based on the data from the IMU using Kalman filtering; The fusion module is used to fuse the first camera pitch angle and the second camera pitch angle through complementary filtering to obtain the fused camera pitch angle; The state estimation module is used to estimate the target's distance and velocity using Kalman filtering based on the fused camera pitch angle.
9. An infrared target ranging and velocity measuring device, characterized in that, The infrared target ranging and velocity measuring device includes a processor and a memory. The memory stores at least one computer instruction, which is loaded and executed by the processor to perform the steps in the infrared target ranging and velocity measuring method according to any one of claims 1 to 7.
10. A computer-readable storage medium, characterized in that, The storage medium stores at least one computer instruction, which is loaded and executed by a processor to perform the steps in the infrared target ranging and velocity measurement method according to any one of claims 1 to 7.