Attitude Estimation Research Articles

Autonomous Landing of a Fixed‐Wing UAV With RTK GNSS and MEMS IMU

Autonomous landing system is an important part of unmanned aerial vehicle (UAV) autonomous flight. Large fixed‐wing UAVs often consume more onboard space and computing resources. With the improvement of system integration and the reduction of high‐performance global navigation satellite system (GNSS) costs, real‐time kinematic (RTK) GNSS has been widely used in small‐scale and low‐cost fixed‐wing UAVs. This article uses less onboard hardware and ground facility requirements to design an autonomous taking off and landing system suitable for general fixed‐wing UAVs. A modified gradient descent attitude estimator, based on microelectromechanical system (MEMS) inertial measurement unit (IMU) and RTK GNSS, and a guidance method based on a tracking differentiator for using in a high dynamic environment are proposed, and circular path following and vertical transient simulations are performed. The simulation results show that this method can improve attitude estimation accuracy, motion control accuracy, and altitudinal transient quality. Finally, the actual flight test verifies its feasibility in the actual environment. The flight test results prove that it is feasible to implement the autonomous landing process of fixed‐wing UAV with the proposed system and method.

Vision-based postural balance assessment of sit-to-stand transitions performed by younger and older adults.

The use of inertial measurement units (IMUs) in assessing fall risk is often limited by subject discomfort and challenges in data interpretation. Additionally, there is a scarcity of research on attitude estimation features. To address these issues, we explored novel features and representation methods in the context of sit-to-stand transitions. This study recorded sit-to-stand transition test data from three groups: community-dwelling elderly, elderly in day care centers (DCC), and college students, captured using mobile phone cameras. We employed pose estimation technology to extract key point kinematic features from the video data and used 10-fold cross-validation to train a random forest classifier, mitigating the impact of individual differences. We trained classifiers with the top 5, 10, and 15 features, calculating the average area under the receiver operating characteristic curve (AUC) for each model to compare feature importance. Our results indicated that elbow key point features, such as (KP08) mean Y, (KP08)RMS Y, (KP09) mean Y, and (KP09) RMS Y, are crucial for distinguishing between subject groups. Statistical tests further validated the significance of these features. The application of human pose estimation and key point signals shows promise for clinical postural balance screening. The identified features can be utilized to develop non-invasive tools for assessing postural instability risk, contributing to fall prevention efforts. This study lays the groundwork for integrating additional measurement modalities into sit-to-stand transition analysis to enhance clinical strategies.

MambaPose: A Human Pose Estimation Based on Gated Feedforward Network and Mamba.

Human pose estimation is an important research direction in the field of computer vision, which aims to accurately identify the position and posture of keypoints of the human body through images or videos. However, multi-person pose estimation yields false detection or missed detection in dense crowds, and it is still difficult to detect small targets. In this paper, we propose a Mamba-based human pose estimation. First, we design a GMamba structure to be used as a backbone network to extract human keypoints. A gating mechanism is introduced into the linear layer of Mamba, which allows the model to dynamically adjust the weights according to the different input images to locate the human keypoints more precisely. Secondly, GMamba as the backbone network can effectively solve the long-sequence problem. The direct use of convolutional downsampling reduces selectivity for different stages of information flow. We used slice downsampling (SD) to reduce the resolution of the feature map to half the original size, and then fused local features from four different locations. The fusion of multi-channel information helped the model obtain rich pose information. Finally, we introduced an adaptive threshold focus loss (ATFL) to dynamically adjust the weights of different keypoints. We assigned higher weights to error-prone keypoints to strengthen the model's attention to these points. Thus, we effectively improved the accuracy of keypoint identification in cases of occlusion, complex background, etc., and significantly improved the overall performance of attitude estimation and anti-interference ability. Experimental results showed that the AP and AP50 of the proposed algorithm on the COCO 2017 validation set were 72.2 and 92.6. Compared with the typical algorithm, it was improved by 1.1% on AP50. The proposed method can effectively detect the keypoints of the human body, and provides stronger robustness and accuracy for the estimation of human posture in complex scenes.

Risk Perception and Psychosocial Factors Influencing Exposure to Antimicrobial Resistance through Environmental Pathways in Malawi.

Antimicrobial-resistant (AMR) bacteria are prevalent in household and environmental settings in low-income locations. However, there are limited data on individuals' understanding of AMR bacteria exposure risks in these settings. A cross-sectional study was conducted to identify individual risk perception of AMR bacteria and its associated behavioral determinants at the household level in urban, peri-urban, and rural Malawi. We conducted interviews with 529 participants from 300 households (n = 100 households/site). The risk, attitude, norms, ability, and self-regulation model was used to assess psychosocial factors underlying AMR bacteria exposure through animal feces, river water, and drain water. Analysis of variance was used to assess the difference between doers and non-doers of the three targeted behaviors: use and contact with river water, contact with drain water, and contact with animal feces. There was limited understanding regarding human-environmental interactions facilitating AMR bacteria transmission across all sites, and as such, the perceived risk of contracting AMR infection was low (41%; P = 0.189). Human contact with animal feces was seen as risky (64%) compared with contact with river and drain water (17%). Urban participants perceived that they were at greater risk of AMR bacteria exposure than their rural counterparts (P = 0.001). The perception of social norms was favorable for the targeted behaviors (P = 0.001), as well as self-reported attitude and ability estimates (self-efficacy; P = 0.023), thus indicating the role of psychosocial factors influencing the human-environment interaction in AMR bacteria transmission. Our findings underscore the need for combined infrastructural improvements and behavior-centered AMR bacteria education to drive behavioral changes, benefiting both AMR infection mitigation and broader One Health initiatives.

Tillage Depth Detection and Control Based on Attitude Estimation and Online Calibration of Model Parameters

Aiming at solving the problems of inaccurate tillage depth detection and unstable tillage depth control, this study proposes a tillage depth detection and control method based on attitude estimation and online calibration of model parameters. First, a tillage depth detection model based on the attitude measurement of the plow is established. The attitude estimation method is designed to measure the horizontal attitude of the plow. An adaptive Kalman filter is utilized to perform an online calibration of the model parameters between the rotation angle of the lifting arm and the pitch angle of the plow. The dynamic and accurate detection of tillage depth is achieved using the tillage depth detection model. Second, a tillage depth control device using an STM32 microcontroller is developed in this study. The PID controller controls the tractor’s suspension system to adjust the plow through the solenoid valve, thus achieving stable control of the tillage depth. Finally, the experimental results obtained from simulation and field tests show that the tillage depth can be stably controlled within the set target interval during the tractor plowing operation, proving the effectiveness and feasibility of the proposed tillage depth detection and control method.

A Monocular Ranging Method for Ship Targets Based on Unmanned Surface Vessels in a Shaking Environment

Aiming to address errors in the estimation of the position and attitude of an unmanned vessel, especially during vibration, where the rapid loss of feature point information hinders continuous attitude estimation and global trajectory mapping, this paper improves the monocular ORB-SLAM framework based on the characteristics of the marine environment. In general, we extract the location area of the artificial sea target in the video, build a virtual feature set for it, and filter the background features. When shaking occurs, GNSS information is combined and the target feature set is used to complete the map reconstruction task. Specifically, firstly, the sea target area of interest is detected by YOLOv5, and the feature extraction and matching method is optimized in the front-end tracking stage to adapt to the sea environment. In the key frame selection and local map optimization stage, the characteristics of the feature set are improved to further improve the positioning accuracy, to provide more accurate position and attitude information about the unmanned platform. We use GNSS information to provide the scale and world coordinates for the map. Finally, the target distance is measured by the beam ranging method. In this paper, marine unmanned platform data, GNSS, and AIS position data are autonomously collected, and experiments are carried out using the proposed marine ranging system. Experimental results show that the maximum measurement error of this method is 9.2%, and the average error is 4.7%.

Evade Unknown Pursuer via Pursuit Strategy Identification and Model Reference Policy Adaptation (MRPA) Algorithm

The game of pursuit–evasion has always been a popular research subject in the field of Unmanned Aerial Vehicles (UAVs). Current evasion decision making based on reinforcement learning is generally trained only for specific pursuers, and it has limited performance for evading unknown pursuers and exhibits poor generalizability. To enhance the ability of an evasion policy learned by reinforcement learning (RL) to evade unknown pursuers, this paper proposes a pursuit UAV attitude estimation and pursuit strategy identification method and a Model Reference Policy Adaptation (MRPA) algorithm. Firstly, this paper constructs a Markov decision model for the pursuit–evasion game of UAVs that includes the pursuer’s attitude and trains an evasion policy for a specific pursuit strategy using the Soft Actor–Critic (SAC) algorithm. Secondly, this paper establishes a novel relative motion model of UAVs in pursuit–evasion games under the assumption that proportional guidance is used as the pursuit strategy, based on which the pursuit UAV attitude estimation and pursuit strategy identification algorithm is proposed to provide adequate information for decision making and policy adaptation. Furthermore, a Model Reference Policy Adaptation (MRPA) algorithm is presented to improve the generalizability of the evasion policy trained by RL in certain environments. Finally, various numerical simulations imply the precision of pursuit UAV attitude estimation and the accuracy of pursuit strategy identification. Also, the ablation experiment verifies that the MRPA algorithm can effectively enhance the performance of the evasion policy to deal with unknown pursuers.

Experimental assessment of IMU data processing techniques for a backpack mobile laser scanning system

Abstract. Positioning techniques are fundamental in many automation tasks with several applications. In GNSS-denied environments like in dense forests, other alternatives are required, such as inertial and visual navigation. However, Inertial Measurement Units (IMUs) data, mainly those from microelectromechanical-system (MEMS), are noisy, which affects the orientation estimation. MEMS IMUs have been employed in mobile laser scanning systems due to their compact design and low-cost solutions for short-term navigation. In this paper, we have compared three IMU processing techniques freely available: MAH (Mahony et al., 2009), MAD (Madgwick et al., 2011) and DCM (Hyyti and Visala, 2015). These techniques implemented different approaches to estimate the attitude. They were experimentally assessed with data from a backpack mobile laser scanning system, which is composed of an OS0-128 Ouster LiDAR equipped with an internal IMU. We have used data from a 5-second trajectory segment aiming to evaluate the attitude and position estimation for a local path. The results showed that the DCM algorithm maintained a consistent velocity for 5 seconds, achieving a positional error of 1.4 m, 0.06 m, and 1.05 m along the X-, Y- and Z-axis, respectively. In contrast, MAD and MAH showed a position error over 20 m, 7 m and 3 m along the X-, Y- and Z-axis, respectively, which was affected by the velocity drift.

A Spinning Magnetometer Based Approach to Solving the Roll Attitude of Aerial Vehicle

Abstract The accurate attitude estimation is the foundation of autonomous flight control for unmanned aerial vehicle (UAV). However, the existing inertia/magnetic field-based roll attitude estimation presents challenges, including the significant computational burden and high cumulative error. To address these issues, this paper proposes a novel roll attitude estimation approach for UAV based on spinning single-axis magnetometer. Firstly, a distinctive rotating measuring mechanism is structured by incorporating the encoder and geomagnetic sensor. Then, the rotating angle of the sensor in relation to the UAV is ascertained with the specific output signal of the geomagnetic sensor with the rotating mechanism. Based on the above, a roll attitude estimation model to be established with the relative angle relationship. Finally, comparative simulations are conducted to demonstrate the feasibility of the presented method. The suggested design method provides a rapid and cost-effective way for roll attitude estimation.

Optimal configuration design and attitude measurement method of redundant accelerometer for steering drilling tools

Abstract In order to improve the accuracy and reliability of attitude parameters measurement of the rotary steerable drilling tool, a slanting configuration method and measurement method of double accelerometer were proposed. The measurement equation of the redundant accelerometer in the drill coordinate system is established. According to the installation position of the accelerometer and sensitive direction, the trouble-free and uniaxial failure in both cases, in order to improve the component of gravity and angular velocity calculation precision as the objective, design measure performance evaluation indexes, the optimal configuration determines the redundant accelerometer. It uses the stability equation constraints gravity no trace adaptive Kalman filter algorithm, Accurate angular velocity measurement, the tool face Angle, and healthy inclination. Simulation and experimental results show that the optimal configuration of the redundant accelerometer is reasonable, and the attitude estimation algorithm is effective.

A robust adaptive error state Kalman filter for MEMS IMU attitude estimation under dynamic acceleration

Accurate estimation of attitude (pitch, roll) is a prerequisite for ensuring safe navigation during vehicle control execution. In dynamic environments, the presence of acceleration often degrades the accuracy and robustness of attitude estimation using traditional algorithms with Micro-Electro-Mechanical Systems (MEMS) Inertial Measurement Unit (IMU). This paper proposes a robust adaptive error state Kalman filter (RAESKF) algorithm for attitude estimation of MEMS IMU under dynamic acceleration conditions. The RAESKF algorithm decomposes the true attitude Direction Cosine Matrix (DCM) into nominal and error components. An error state Kalman filter is utilized to fuse real-time measurements from MEMS gyroscope and accelerometer, estimating the error component, which is then used to correct the nominal attitude DCM. Furthermore, within the error state Kalman filtering framework, a dynamic acceleration robust adaptive adjustment strategy is implemented. This strategy relies on real-time data monitoring, threshold-based decision-making, and switching mechanisms. It adaptively selects between dynamic acceleration model compensation and online adjustment of the measurement noise covariance matrix. The primary objective of this strategy is to mitigate the impact of disturbance induced by dynamic acceleration on attitude estimation, thereby enhancing the accuracy and robustness. Laboratory rotation experiments have demonstrated that the algorithm improved pitch and roll measurement accuracy by at least 8.81% and 11.69%, respectively, under rotational conditions. In dynamic acceleration conditions, pitch and roll measurement accuracy improved by at least 42.76% and 67.21%, respectively.

Extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments

Abstract Point cloud maps constructed using 3D LiDAR, are widely used for robot navigation and localization. Few studies have utilized point cloud maps to extract terrain elevationinformation in front of a vehicle, which can be used as active suspension inputs to reduce vehicle bumps. In addition, the trajectories of dynamic objects in point cloud maps and global navigation satellite system (GNSS) data loss can affect the extraction of elevation information. To solve these problems, this paper proposes a framework for extracting terrain elevation information in front of the vehicle based on vehicle-mounted LiDAR in dynamic environments. The framework consists of two modules: point cloud map construction and vehicle front terrain elevation information extraction. In the point cloud map construction module, a system for simultaneous localization and mapping (SLAM) is proposed, which is capable of building point cloud maps without GNSS. Furthermore, a dynamic descriptor-based dynamic object filtering algorithm is proposed which is applied to SLAM. Therefore, the SLAM system overcomes the influence of dynamic objects on vehicle position and attitude estimation, and there are no trajectories of dynamic objects in the point cloud maps built by the system. In the vehicle front terrain elevation information extraction module, the unscented Kalman filter is utilized to predict the vehicle position at the next moment. Based on the geometric features of the tire-ground contact area, the terrain elevation information of the tire contact area at the predicted position on the point cloud map is extracted. Experiments show that the algorithm in this paper overcomes the effect of dynamic objects and builds a vehicle point cloud map without dynamic objects under GNSS data loss, which improves the accuracy of the extraction of terrain elevation information in front of the vehicle.

Research on High-precision Algorithm for Flight Attitude Evaluation of Quadrotor Aircraft

With the rapid development of UAV technology, quadrotor aircraft has shown wide application potential in many fields. In this paper, a high-precision algorithm based on multi-sensor data fusion and optimized extended Kalman filter (EKF) technology is designed and implemented for the flight attitude evaluation of quadrotor aircraft. By fusing the data of IMU (Inertial Measurement Unit), GPS (Global Positioning System) and magnetometer, the algorithm can effectively resist noise interference and reduce sensor errors, and realize real-time and high-precision estimation of aircraft attitude angle and position. The experimental results show that the algorithm performs well in a variety of flight trajectories, which is basically consistent with the real trajectory, especially in complex movements such as hovering and diving, and can still maintain high accuracy and robustness. Compared with the commonly used Mahony and Madgwick algorithms, the algorithm proposed in this study has obvious advantages in attitude estimation error fluctuation range, error stability and error average. This not only improves the maneuverability and safety of the aircraft, but also provides strong support for the autonomous flight and precise control of the UAV. This study provides an important reference for the field of flight attitude evaluation of quadrotor aircraft.

Cross-Spectral Navigation with Sensor Handover for Enhanced Proximity Operations with Uncooperative Space Objects

Close-proximity operations play a crucial role in emerging mission concepts, such as Active Debris Removal or small celestial bodies exploration. When approaching a non-cooperative target, the increased risk of collisions and reduced reliance on ground intervention necessitate autonomous on-board relative pose (position and attitude) estimation. Although navigation strategies relying on monocular cameras which operate in the visible (VIS) spectrum have been extensively studied and tested in flight for navigation applications, their accuracy is heavily related to the target’s illumination conditions, thus limiting their applicability range. The novelty of the paper is the introduction of a thermal-infrared (TIR) camera to complement the VIS one to mitigate the aforementioned issues. The primary goal of this work is to evaluate the enhancement in navigation accuracy and robustness by performing VIS-TIR data fusion within an Extended Kalman Filter (EKF) and to assess the performance of such navigation strategy in challenging illumination scenarios. The proposed navigation architecture is tightly coupled, leveraging correspondences between a known uncooperative target and feature points extracted from multispectral images. Furthermore, handover from one camera to the other is introduced to enable seamlessly operations across both spectra while prioritizing the most significant measurement sources. The pipeline is tested on Tango spacecraft synthetically generated VIS and TIR images. A performance assessment is carried out through numerical simulations considering different illumination conditions. Our results demonstrate that a combined VIS-TIR navigation strategy effectively enhances operational robustness and flexibility compared to traditional VIS-only navigation chains.

Ground Constrained 3D Lidar SLAM Design and Implementation

Abstract. Synchronized positioning and mapping techniques based on multi-line LIDAR are mainly used for mobile platforms that require high-precision positioning, such as UAVs and self-driving cars. However, multi-line LiDAR is limited by vertical field of view and vertical angular resolution, which can affect the vertical accuracy of attitude estimation. In this paper, we aim to extract the ground information in the environment and fuse it into a factor map to constrain the vertical accuracy of the attitude. Different from the traditional geometric information-based ground plane segmentation, this paper utilizes point cloud distribution information to extract the ground cloud and uses high-confidence ground features to constrain the attitude drift by considering their distribution weights. Finally, the laser range factor, ground constraint factor and closed-loop factor are integrated under the factor graph optimization framework. The effectiveness of the method is validated on a self-constructed dataset.

Adaptive Fault‐Tolerant Multiplicative Attitude Filtering for Small Satellites

ABSTRACTThis study tackles the problem of fault‐tolerant attitude estimation for small satellites. A probabilistic adaptive technique is presented for the multiplicative extended Kalman filter (MEKF) algorithm that is used in attitude estimation. The presented method is based on tracking the normalized measurement innovations in the filter and calculating the probability of the normal operation of the estimation system. Using this probability, the filter gain is corrected to maintain the tracking performance of the filter despite faulty measurements. In order to evaluate the performance of this method, several simulations are performed where different types of faults are introduced to the synthetic attitude sensor measurements (magnetometer and sun sensor) at different times. Simulation results are compared not only with a conventional EKF but also with another popular adaptive Kalman filter, an adaptive Kalman filter with multiple scaling factors (MSFs).

Optimized Factor Graph for Tightly-Coupled LiDAR/IMU Localization in Underground Parking Garages

Abstract. To address the challenge of intelligent vehicle localization in underground parking structures due to the loss of GNSS signals, this paper introduces a method to address this issue by developing a novel localization framework known as GF-LIO, which denotes a tightly-coupled fusion of LiDAR and IMU data, innovatively combining the Interactive Extended State Kalman Filter (IESKF) with a factor graph to enhance the localization process and solve the problem of GNSS signal loss in underground parking lots. The GF-LIO model commences with a strategic feature selection process, facilitated by a greedy algorithm that prioritizes environmental cues within the point cloud data. This method effectively filters out redundant features, thereby enhancing the saliency of retained features and subsequently improving the robustness of the localization process. Following feature selection, the model integrates LiDAR and IMU measurements utilizing the IESKF algorithm, ensuring a cohesive fusion of sensor data and bolstering attitude estimation accuracy. The culmination of the GF-LIO framework involves factor graph optimization, a sophisticated technique that synthesizes LiDAR odometry, IMU pre-integration factors, and loop closure detection factors. This optimization step enhances the overall precision and consistency of the localization process, resulting in superior performance compared to existing methodologies. Experimental evaluations conducted within underground parking environments corroborate the efficacy of the GF-LIO model. Comparative analyses against established approaches such as A-LOAM, LeGO-LOAM, LIO-LOAM, and FAST-LIO demonstrate a notable performance improvement exceeding 12.53%. The proposed model adeptly integrates domain-specific environmental characteristics with multi-sensor data, thereby facilitating precise localization and map construction tasks for intelligent vehicle navigating within the intricate confines of subterranean parking structures.

GNSS/IMU integrated navigation heading angle enhancement algorithm based on dual-antenna TDCP

In the integrated navigation system of Global Navigation Satellite System (GNSS) and Inertial Measurement Unit (IMU), attitude estimation, especially accurate estimation of heading angle, is particularly important for real-time monitoring of vehicle driving status. However, since IMU is divergent in the altitude channel, its error will gradually accumulate if it is not accurately constrained. Therefore, the estimation accuracy of heading angle is insufficient in vehicle-mounted applications where the heading angle changes frequently. In order to solve the problem of poor navigation attitude estimation accuracy in the loose combination of traditional GNSS and IMU, this paper proposes a GNSS/IMU integrated navigation heading angle enhancement algorithm based on dual-antenna epoch carrier phase difference (TDCP). This method solves the vehicle heading angle through dual-antenna TDCP to increase the input dimension of the integrated navigation filter fusion observation value, and uses Hatch filtering and anti-error adaptive filtering to improve the observation domain pseudorange accuracy and GNSS/IMU integrated navigation positioning and attitude determination performance respectively. Experimental results show that compared with the traditional GNSS/IMU integrated navigation method, the proposed algorithm improves the positioning and speed measurement accuracy in the three-dimensional direction by and respectively 22.12%, 41.27%and the vehicle heading angle accuracy is improved 46.29%.

Parents' or Guardians' Knowledge, Attitudes and Practices in the Prevention and Management of Childhood Myopia.

The aim of this study was to assess the knowledge, attitude, and practices (KAP) of parents or guardians regarding the prevention and management of childhood myopia. This cross-sectional study was conducted at the Department of Ophthalmology in the First Affiliated Hospital of Soochow University (Suzhou, China) between August 2023 and November 2023. Parents or guardians who willingly volunteered to take part in the study were surveyed using a self-designed questionnaire. A total of 571 participants returned valid questionnaires, among whom 288 respondents (50.44%) fell within the 31- to 40-year age group and 474 respondents (83.01%) were identified as myopic. The mean KAP scores for the knowledge, attitude and practices dimensions were 23.34 ± 3.05 (possible range: 0-26), 46.47 ± 4.02 (possible range: 12-60), and 40.52 ± 7.07 (possible range: 11-54), respectively. Structural equation modeling analysis indicated that education had a direct effect on knowledge (estimate = 0.41, P = 0.038), while knowledge directly influenced both attitude (estimate = 0.40, P < 0.001) and practices (estimate = 0.36, P < 0.001). Also, attitude was found to have a direct impact on practices (estimate = 0.45, P < 0.001). Parents or guardians had adequate knowledge, a positive attitude, and proactive practices towards the prevention and management of childhood myopia, which might be affected by their educational level. This comprehensive understanding of parental perspectives highlights the potential for targeted interventions in clinical settings to further enhance pediatric eye care.

High precision DSRC and LiDAR data integration positioning method for autonomous vehicles based on CNN

In order to improve the safe driving and automatic positioning capability of autonomous vehicles, a high-precision DSRC and LiDAR data integration positioning technology for autonomous vehicles based on CNN is proposed. Import the data of Dedicated Short Range Communications and Light Detection and Ranging for automatic driving vehicle positioning, carry out kinematic analysis of autonomous driving vehicles under multi-sensor fusion, and transform the data of DSRC and LiDAR sensors into tightly coupled coordinate systems; The CNN depth learning method is used to compensate the position and attitude tracking estimation error under the overall time stamp synchronization of the sensor through adaptive information tracking; The first and second order feedforward compensation is made for the positioning parameters of the autonomous driving vehicle using the PID model, and the point cloud feature matching model is fused to complete the estimation of the positioning attitude parameters of the autonomous driving vehicle. In order to eliminate the noise interference under the DSRC communication mechanism, the Kalman filter function is used to automatically optimize the constraint parameters in the point cloud feature detection model, and the positioning error parameters are dynamically filtered and adjusted; Kinematics analysis is carried out for the driving state of the vehicle, and the positioning error in the vehicle movement is controlled through the difference technology to achieve high-precision DSRC and LiDAR data integration positioning. The simulation results show that this method can integrate and locate the high-precision DSRC and LiDAR data of the autonomous vehicle, and the attitude estimation and positioning accuracy of the vehicle is good, while the error of the attitude parameter estimation of the autonomous vehicle is low.