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Simultaneous localization and mapping (SLAM) is a critical technology for unmanned driving. Existing SLAM methods have the drawbacks of significant cumulative errors and drift in coal mine roadway environment. In this study, a roadway environment feature-assisted SLAM algorithm integrating inertial measurement unit (IMU) and LiDAR was proposed. IMU observation data was used to predict the motion state of point cloud and motion compensation was applied to reduce point cloud distortion caused by equipment movement. Pose transformation information from LiDAR odometry was obtained through point cloud registration, forming a LiDAR odometry constraint. Point clouds from roadway sidewalls and floor were extracted and fitted to planes, establishing environmental constraints. Using IMU pre-integration constraints, LiDAR odometry constraints, and environmental constraints, the algorithm applied factor graph optimization to achieve tight coupling between LiDAR and IMU, enabling high-precision 3D reconstruction of roadway scenes and accurate localization of autonomous vehicles. Simulation experiments showed that the absolute trajectory root mean square error (RMSE) of the roadway environment feature-assisted IMU-LiDAR integrated SLAM algorithm was 0.1162 m, and the relative trajectory RMSE was 0.0409 m, improving positioning accuracy compared to commonly used algorithms such as LeGO-LOAM and LIO-SAM. Based on the test results in a real environment, the algorithm provides excellent mapping performance with no drift or trailing, demonstrating strong environmental adaptability and robustness.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Train positioning method based on the global navigation satellite system (GNSS) is a key development direction for the next generation train operation control systems. To enhance the safety and autonomy of train positioning, while fully considering the cost of integrated positioning systems and the complementary performance of heterogeneous sensors, this study employed the BeiDou navigation satellite system (BDS) and inertial measurement unit (IMU) to build an onboard integrated positioning scheme. Addressing the issue of cumulative error divergence in IMU standalone navigation with BDS under conditions where satellite signals were limited, resulting in positioning failure, such as in tunnels, a method aided by grey combination prediction model was proposed to maintain positioning accuracy. An algorithm model was established based on location information from integrated BDS/ IMU system prior to BDS signal failure. When the train entered an environment with limited satellite signals, the grey combination model continuously corrected the IMU's cumulative errors using the predicted train positioning data sequence, ensuring the availability of positioning output throughout the entire train operation. The proposed method was validated on a simulation platform in a laboratory environment. The results showed that the BDS/ IMU combination maintained high positioning accuracy under normal working conditions, with the eastward/ northward position errors less than 2 m and the eastward/ northward velocity errors under 0. 2 m/ s. In scenarios where BDS positioning failed, the grey combination model corrected the IMU's cumulative errors using the predicted data, reducing eastward/ northward position errors by more than 85% and eastward/ northward velocity errors by over 80% compared to IMU standalone navigation. The positioning accuracy meets the train operation control system's requirements for train positioning. [ABSTRACT FROM AUTHOR]

In response to the problems of cumulative errors in step size estimation and the large sample size required by traditional deep learning methods in the pedestrian dead reckoning (PDR) based underground personnel positioning system in coal mines, a step size estimation method for underground personnel based on generative adversarial network (GAN) is proposed. The GAN model mainly includes two parts: generative model and discriminative model, both of which are implemented using deep neural networks (DNNs). The generative model aims to generate continuous result distributions (i.e. labels) based on input data. Its output layer uses a linear activation function to preserve the linear features of the network, allowing the model to predict the step size of any personnel during walking. The discriminant model aims to distinguish whether the input data and labels are real labels or labels generated by the generator. Its output layer uses a Sigmoid activation function to achieve binary classification of results. After determining the generative model and discriminant model, the GAN model combines two models for training. By constructing and optimizing the dynamic competition between the generator and discriminator, the generator can learn to generate more realistic and indistinguishable data samples in continuous iterations. The experimental results show that under the same training and testing sets, the average error of the GAN model is 0.14 m, and the standard deviation and root mean square error are both smaller than those of the DNNs model, with the minimum values being 0.74 m. The outdoor test results show that the GAN based underground personnel step estimation method has a minimum error of 3.21% and a maximum error of 4.79% in uphill and downhill scenarios. Compared to uphill and downhill scenarios, the error in playground scenarios is smaller, with a maximum error of 1.91%.

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Ordnance Equipment Engineering is the property of Chongqing University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In response to the problem of poor robustness of traditional visual odometers that only rely on point features, a monocular visual inertial odometry that utilize point features, line features and structural features are deduced. The structural feature is an orthonormal basis that satisfy the Manhattan world hypothesis. This feature contains the parallel and orthogonal information of the structural object. The introduction of structural feature provide an overall constrain for visual inertial odometry and can reduce the accumulative error of rotation effectively. Meanwhile, to ensure the real-time performance of the system after the introduction of structural features, a robust and efficient algorithm is proposed to extract structural feature, which is based on the matching relationship of line features and the direction of gravity vector. The experimental results demonstrate that, compared with the existing open source systems, the proposed system can effectively reduce the accumulated rotation error while maintaining the real-time performance. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The failure of autonomous navigation, positioning and mapping of the mobile robot is caused by the shotcrete surface and symmetrical roadway in coal mine. In order to solve this problem, a real-time positioning and mapping method based on LiDAR and IMU fusion is proposed for the roadway environment in the coal mine. Firstly, the original point cloud is segmented. The IMU pre-integration pose is used to remove the nonlinear motion distortion of the original point cloud. The line and surface feature extraction is carried out on the obtained point cloud. Secondly, the line and surface features of adjacent frames are matched. The initial pose value obtained by IMU pre-integration is fused in the hierarchical pose estimation process. The calculation iteration times are reduced, the matching precision of feature points is improved, and the pose of the current frame is solved. Finally, the local map factor, IMU factor and key frame factor are inserted into the factor graph to optimize and constrain the pose. The key frame is matched with the local map, and the map construction is realized through an octree structure. In order to verify the positioning performance and mapping effect of the proposed method, the experimental platforms of Autolabor, VLP-16 LiDAR and Ellipse-N IMU are built. The qualitative and quantitative comparison between the proposed method and LeGO-LOAM and LIO-SAM methods is carried out. The results show the following points. ① In the coal mine roadway environment, the average and median of the absolute positioning error in the three axes direction of the real-time positioning and mapping method based on LiDAR and IMU fusion are less than 32 cm. The position and attitude estimation precision in the X-axis is the highest, with a cumulative error of 1.65 m and a position deviation of 2.97 m. The overall mapping effect is good, and the mapping track does not drift. The point cloud map constructed has excellent performance in integrity and geometric structure authenticity. The map can directly reflect the actual situation of the roadway environment, and has good robustness. This is because hierarchical pose estimation is performed after point cloud matching. The multi-factor optimization can effectively reduce the global cumulative error, which plays an important role in improving track precision and map consistency. ② In the corridor environment, the three-axis error of the real-time positioning and mapping method based on LiDAR and IMU fusion for the coal mine roadway environment is less than 1.01 m. The average error is 5~15 cm, with small error range and high precision. The accumulated position deviation is only 1.67 m. Integrity and environment matching have good performance. This is because by adding keyframe factors and inserting factor graphs to optimize the related variables of the newly added nodes, the drift of pose estimation is reduced. The positioning and mapping precision is relatively high.

The underground coal mine environment is complex. The existing coal mine robot positioning methods have low positioning precision and low real-time performance caused by the non-line-of-sight (NLOS) error and other factors. In order to solve the above problems, a tightly combined positioning method of coal mine robot based on UWB (Ultra Wide Band) and IMU (Inertial Measurement Unit) is proposed. Firstly, the UWB module is used to measure distance between the coal mine robot and UWB base station. The least square support vector machine (LSSVM) model is trained by using real value and measured value of the distance between the coal mine robot and the UWB base station, and the modified LSSVM model is obtained. Secondly, the measured value measured by the UWB module during the positioning process of the coal mine robot is used as the input of the modified LSSVM model. The modified LSSVM model is used to correct the measured value of UWB, reduce the influence of NLOS error on positioning precision, and obtain more accurate distance information. Finally, the range information modified by modified LSSVM model is used as the measurement input of error-state Kalman filter (ESKF). The measurement equation is formed with the position information solved by inertial navigation. The ESKF is used to tightly combine the UWB ranging correction value with the range information calculated by the inertial navigation to complete the state update. The more precise position information of the coal mine robot is obtained, and the precise positioning of the coal mine robot is achieved. The simulation results under different layont schemes of UWB base stations show that using modified LSSVM model can make the UWB range information more accurate, and improve the positioning precision. In the static positioning experiment, when the four UWB base stations are symmetrically distributed at the same height, the root mean square error of the positioning is reduced from 0.146 4 m to 0.139 8 m. When the four UWB base stations are distributed symmetrically with unequal heights, the root mean square error decreases from 0.300 8 m to 0.200 6 m. When the four base stations are distributed irregularly, the root mean square error decreases from 0.317 5 m to 0.314 2 m. Therefore, in actual scenarios, the UWB base stations should be arranged symmetrically at the same height as possible. In the dynamic positioning experiment, the fusion positioning trajectory corrected by the modified LSSVM model is closer to the real trajectory of the coal mine robot than the fusion positioning trajectory before correction. The result verifies that the tightly combined positioning method can reduce the NLOS error and improve positioning precision.

When simultaneous localization and mapping (SLAM) technology is applied to autonomous positioning of unmanned aerial vehicles in coal mines, the use of feature points to construct maps can easily lead to degradation issues, resulting in inaccurate positioning. Moreover, due to its use of the body as a reference coordinate system, global positioning cannot be achieved. In order to solve the problems, a positioning method for underground unmanned aerial vehicles (UAV) in coal mines based on global point cloud map is proposed. The method uses Fast-LIO2 algorithm as the lidar SLAM algorithm to obtain UAV position and attitude estimation. An iterative nearest-neighbor algorithm is used for two-step matching of the acquired real-time lidar point cloud and the global point cloud map to achieve UAV position and attitude correction. To address the issue of point cloud matching speed not ensuring real-time positioning due to the excessive number of point clouds, a time-based position and attitude output strategy is designed to increase the frequency of outputting UAV position and attitude data. The SLAM precision and position and attitude correction effect of the UAV positioning method are tested in a 1 000 m underground coal mine roadway. The results show that in long-distance roadway environments, the cumulative positioning error of the Fast-LIO2 algorithm is less than 1 m, and is less than 0.3 m in the range of 600 m or more, which is significantly smaller than the cumulative positioning errors of LOAM-Livox algorithm and LIO-Livox algorithm. The position and attitude estimation output by the Fast-LIO2 algorithm has been corrected by the correction algorithm, and all flight paths are located in the global point cloud map, verifying the effectiveness of the position and attitude correction algorithm. The time consumption of single SLAM algorithm operation is 14.83 ms, the one of single position and attitude correction is 883 ms, and the output frequency of position and attitude data is 10 Hz, meeting the real-time requirements of UAV positioning.

Scraper conveyor is the core transportation equipment of the fully mechanized working face. Accurately perceiving its form is an important prerequisite to enhance its carrying capacity, alleviate the transmission impact, and improve the straightness of fully mechanized working face. The commonly used indirect measurement methods for the shape of scraper conveyors are difficult to accurately characterize their shape, resulting in significant measurement model errors. To address this issue, an inertial measurement unit is used to directly measure the original pose information of the middle trough of scraper conveyor, achieving accurate acquisition of the shape data of scraper conveyor. A wavelet thresholding denoising method that combines Heursure threshold rules and a new threshold function is used to filter out noise interference in the acceleration signal of the middle trough. Based on this, the motion features of the middle trough are analyzed, and a middle trough motion state recognition model based on random forest algorithm is designed. Based on the motion state recognition results, different strategies are used to update the position of the middle trough. It reduces the accumulated IMU data error over time and improves the precision of IMU position calculation. The improved Harris hawk optimization (HHO) algorithm unscented Kalman filter (UKF) is designed for middle trough attitude calculation. It is verified through experiments that the attitude angle calculated by this method meets the requirements of middle trough attitude measurement. The experimental platform for shape monitoring of scraper conveyors is constructed. It conducts experimental verification on the shape calculation method of scraper conveyors based on motion state recognition and improved HHO optimized UKF. The results show that when the scraper conveyor performs a single sliding with a step distance of 250 mm, the maximum cumulative errors of displacement in the X and Y directions of the scraper conveyor composed of 10 middle troughs are 6.4 mm and 8.4 mm respectively under the horizontal working condition of bottom plate. It remains unchanged in the Z direction. The maximum cumulative errors of pitch angle, roll angle, and heading angle are −0.148°, −0.035°, and 0.457° respectively. Under the working condition of floor undulation, the maximum cumulative errors of displacement in the X, Y, and Z directions are 6.6 mm, 11.5 mm, and 6.9 mm respectively. The maximum cumulative errors of pitch angle, roll angle, and heading angle are −0.540°, −0.157°, and 0.817° respectively. This method can effectively suppress cumulative errors, reduce measurement errors, and achieve accurate perception of the shape of the scraper conveyor.

Copyright of Journal of Guangdong University of Technology is the property of Journal of Guangdong University of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Long-distance oil and gas pipelines passing through the areas with serious geological hazards are prone to bending deformation due to the external soil loads, which will threaten the safe operation of pipelines. The inline inspection technology based on the inertial measurement unit (IMU) is the main means to inspect the local deformation of pipelines at present. Herein, the characteristics of IMU data of the four typical locally-deformed pipeline sections, i.e. the buried pipeline elbows, the dented pipelines, the pipelines with bending deformation and the abnormal girth welds, were provided. Meanwhile, a IMU data pre-processing method based on wavelet denoising was put forward, a deep neural network model was established to identify the IMU data thermal map of the 4 types of typical locally-deformed pipeline sections, and a set of method was developed to identify the pipeline sections with bending deformation based on IMU data. By analyzing the 6-year IMU data of China-Russia Crude Oil Pipeline with the new method, totally 33 177 data of sample pipeline sections were formed, and an IMU bending strain database was set in China. The results of application example show that: the accuracy of the deep neural network model established based on the database to identify the pipeline sections with bending deformation is more than 90%, and the identification efficiency is up to 0.02 min/km. Hence, the method for identification of pipeline sections with bending deformation based on IMU data provides an effective technical means to identify the deformed pipeline sections with bending strain greater than 0.125% in the integrity assessment of pipelines.

In order to improve the calculation accuracy of the inertial measurement coordinate data of in-service pipelines and thereby promote the pipeline digitization process with high quality, a calculation model of dead reckoning error of a small-caliber inertial measurement unit was proposed on the basis of the information of the inertial measurement unit and the odometer with reference to the smooth filtering algorithm. Through the filter recursive calculation with the established state equation and the measurement equation, the optimal estimated value of the system error can be obtained, and the data accuracy can be guaranteed by solving the ambiguity of the whole cycle with the smoothing filter algorithm. According to the verification of the pipeline inertial measurement results, the pipeline centerline coordinates calculated by the dead reckoning error calculation model are consistent with those collected by GNSS-RTK, and the error can be controlled at the meter level. As the result shows, the calculation model, which is special for reducing dead reckoning error of the small-caliber inertial measurement unit based on the information of the inertial measurement unit and the odometer in combination with the smooth filtering algorithm, can effectively reduce the influence of the pure inertial navigation error accumulation over time on the accuracy of measurement results, and thus it has good application value.

Copyright of Journal of Data Acquisition & Processing / Shu Ju Cai Ji Yu Chu Li is the property of Editorial Department of Journal of Nanjing University of Aeronautics & Astronautics and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the case of nonlinear systems with random bias, the Optimal Two-Stage Unscented Kalman Filter (OTSUKF) can obtain the optimal estimation of system state and bias. But it requires random bias to be accurately modeled, while it is always very difficult in actual situation because the aircraft is a typical nonlinear system. In this paper, the faults of the Inertial Measurement Unit (IMU) are treated as a random bias, and the random walk model is used to describe the fault. The accuracy of the random walk model depends on the degree of matching between the covariance of the random walk model and the actual situation. For the IMU fault diagnosis method based on OTSUKF, the covariance of the random walk model is assigned with a constant matrix, and the value of the matrix is initialized empirically. It is very difficult to select a matching matrix in practical applications. For this problem, in this paper, the covariance matrix of the random walk model is adaptively adjusted online based on the innovation covariance matching technique, and an adaptive Two-Stage Unscented Kalman Filter (ATSUKF) is proposed to solve the fault diagnosis problem of the IMU. The simulation experiment compares the IMU fault diagnosis performance of OTSUKF and ATSUKF, and verifies the effectiveness of the proposed adaptive method.

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to establish the connection between handicap control and arm motion, combined with inertial measurement unit (IMU), an arm motion recognition method based on sub-motion feature matrix and the dynamic time warping (DTW) algorithm is proposed. Firstly, the arm linkage movement model is established, and the spatial position of the arm end is calculated using the forward kinematics theory. Then the angular velocity and spatial position changes of the arm end are analyzed. It is found that the continuous arm movement can be broken down into several sub-actions according to the activity level of the movement, and the sub-motion feature matrix is constructed. Finally, the DTW algorithm based on the sub-motion feature matrix is used to classify and recognize six common arm movements in life. Experimental results show that compared with the traditional time series-based DTW action recognition method, the recognition accuracy of the method based on sub-action feature matrix is increased from 96.9% to 99.4%, and the recognition time is reduced to less than 0.1 s, and thus the method is more practical. [ABSTRACT FROM AUTHOR]

Optimal two stage Kalman filter (OTSKF) is able to obtain optimal estimation of system states and bias for linear system which contains random bias. Unscented Kalman filter (UKF) is a conventional nonlinear filtering method which utilizes Sigmas point sampling and unscented transformation technology realizes propagation of state means and covariances through nonlinear system. Aircraft is a typical complicate nonlinear system, this paper treats the faults of Inertial Measurement Unit (IMU) as random bias, established a filtering model which contains faults of IMU. Hybird the two stage filtering technique and UKF, this paper proposed an optimal two stage unscented Kalman filter (OTSUKF) algorithm which is suitable for fault diagnosis of IMU, realized optimal estimation of system states and faults identification of IMU via proposed innovative designing method of filtering model and the algorithm was validated that it is robust to wind disterbance via real flight data and it is also validated that proposed OTSUKF is optimal in the existance of wind disturbance via comparing with the existance iterated optimal two stage extended kalman filter (IOTSEKF) method.

In order to meet the application requirements of high-precision pedestrian autonomous navigation and indoor positioning, a prototype of wearable human motion measurement system integrating Arduino and Xsens Awinda inertial measurement kit is designed. The device is convenient to wear and highly reliable which is capable of simultaneous measurement of feet inertial information and the feet range. The function of wireless data transmission, remote storage and analysis is also supported. The gait pattern is obtained by zero-velocity detection algorithm using the inertial data, the feet range information is continuously measured by modified SRF10 ultrasonic range finder, which is supposed to improve the accuracy of inertial navigation and positioning.

According to the characteristics of DMI with high-precision position information, a navigation method combines DMIs with GPS and IMU is proposed in this paper. Firstly, the prototype system architecture is presented and its operational principle is discussed. Secondly, some key technologies are studied, which include integrated organization method of DMIs and road net data, the position method based on DMI matching with real-time image and the federal Kalman filter model with DMI, GPS and IMU. Finally, experimental results show that the proposed method can counteract GPS signal's unlocking problem and the inertial component's divergence for its error accumulation with time in a vehicle navigation system, whose positioning accuracy and robustness are improved in complex environment.

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Copyright of Journal of Central China Normal University is the property of Huazhong Normal University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Copyright of Telecommunication Engineering is the property of Telecommunication Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Harbin Institute of Technology. Social Sciences Edition / Haerbin Gongye Daxue Xuebao. Shehui Kexue Ban is the property of Harbin Institute of Technology and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The error modulation principle and rotation scheme of single-axis rotary strapdown inertial navigation system (SINS) are studied, and the rotary modulation compensation of errors is analyzed. Single- axis unidirectional continuous rotation scheme, more than 360° two-position rotation-stop scheme, and less than 360° four-position rotation-stop scheme are designed. A long time navigation simulation of two- and four-position rotation-stop schemes is carried out under the swing state. The simulated results show that the positioning accuracies of the two- and four-position rotation-stop schemes are nearly the same. The four-position rotation-stop scheme is selected for engineering application. The rotary SINS using the four-position rotation-stop scheme is verified through the turntable and vehicle-mounted experiments, respectively. The results meet the initial design indexes of system and have reference value for engineering. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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Precise target characteristics detection could provide important parameters for smart variable-rate sprayers. In order to diminish the influence of complex terrain conditions, 3 kinds of dynamic attitude angle deviation correction methods were proposed to mitigate the errors of Lidar-based spray target detection caused by roll angle, pitch angle and yaw angle respectively. An experimental vehicle integrated with a laser scanning sensor detection unit and an inertial measurement unit (IMU) was used to detect spray targets under complex road conditions. The laser sensor detection unit was capable of detecting spray targets with the laser scanner. The inertial measurement unit was able to detect the real-time attitude angle deviations of the vehicle. The dynamic roll correction adopted real-time measured roll angles to correct detection targets and 3-D (three-dimensional) reconstruction images using re-matching trigonometric function and laser scanning polar coordinate value. For the dynamic pitch correction, re-combination of laser scanning target frames and detection points under the guidance of pitch angle values averaged segmentally was used to diminish dynamic pitch angle deviations. The coefficient value of laser scanning depth data combined with yaw angle average values was applied for the dynamic yaw correction. According to the measured vehicle dynamic attitude angle deviations, these 3 kinds of dynamic attitude angle deviation correction methods were put forward to obtain accurate characteristics of the targets and 3-D reconstruction images. The verification experiments for the proposed correction methods to overcome complex field road conditions were divided into 3 test steps. Firstly, an artificial tree and a cuboid chosen as targets were detected with specified detection distances and laser travel speeds to verify pitch correction algorithm when single dynamic pitch angle changed under downhill or uphill terrain. Secondly, the artificial tree was selected as the laser scanning target to test the correction algorithms when dynamic pitch angles combined with roll angles existed under long wooden unilateral bridge terrain. Finally, the artificial tree and the cuboid cabinet were selected as the laser scanning target to verify the correction methods under uneven complex terrain. The data correction process and 3-D image reconstruction were conducted using MATLAB software. The experiment results of 3 steps showed that the maximum relative errors of the height and width of cuboid cabinet were 8.89% and 6.00% respectively after the correction. The relative errors of the height, width and canopy height of the artificial tree were 5.63%, 10.00% and 5.00% respectively. The 3-D reconstruction images also had significant improvements after the correction. The test results verify the effectiveness of the proposed data correction methods for laser attitude angle deviations correction under complex road conditions. [ABSTRACT FROM AUTHOR]

The system error equations in the SINS transverse coordinate method are deduced for the transverse coordinate system. According to the solutions from the static base system error equations, the impact of system initial errors and inertial measuring unit errors on the transverse coordinate method is analyzed. System errors are simulated, and compared with system errors from the geographic coordinate computation method. Theoretical analysis and simulation results show transverse velocity errors and horizontal attitude errors are a type of periodic oscillation;position errors, azimuth error angle, wander azimuth error and heading errors increase with time. The transverse coordinate method can solve the problems arising when system errors increase and calculations output singular values in the geographic latitude and longitude coordinate calculating method. [ABSTRACT FROM AUTHOR]

A new calibration method that can increase the estimation accuracy of strapdown inertial measurement unit(IMU)'s model parameters is described when the IMU's axes were not oriented precisely with respect to the axes of a tri-axia[ turntable. An accurate mathematical model was derived firstly. Then a test scheme, which was a combination of the coarse and precise calibration attempts to find the optimized values of the model's parameters, was introduced to minimize the error by starting at an initial parameter estimation of the coarse calibration. Test results showed that this calibration method can estimate the installation error and parameters of iMU model precisely without orientation adjustment. The best calibration method is found to be the "30-position" calibration arrangement. [ABSTRACT FROM AUTHOR]