Visual Measurement Model Research Articles

Vision and Inertial Navigation Combined-Based Pose Measurement Method of Cantilever Roadheader

Pose measurement of coal mine excavation equipment is an important part of roadway excavation. However, in the underground mining roadway of coal mine, there are some influencing factors such as low illumination, high dust and interference from multiple equipment, which lead to the difficulty in the position and pose measurement of roadheader with low measurement accuracy and poor stability. A combination positioning method based on machine vision and optical fiber inertial navigation is proposed to realize the position and pose measurement of roadheader and improve the accuracy and stability of the position and pose measurement. The visual measurement model of arm roadheader is established, and the optical fiber inertial navigation technology and the spatial coordinate transformation method are used. Finally, the Kalman filter fusion algorithm is used to fuse the two kinds of data to get the accurate roadheader pose data, and the inertia is compensated and corrected. Underground coal mine experiments are designed to verify the performance of the proposed method. The results show that the positioning error of the roadheader body using this method is within 40 mm, which meets the positioning accuracy requirements of roadway construction. This method compensates for the shortcomings of low accuracy and poor reliability of single vision measurement, single inertial navigation measurement and single odometer measurement.

Ship Intention Prediction at Intersections Based on Vision and Bayesian Framework

Due to the high error frequency of the existing methods in identifying a ship’s navigational intention, accidents frequently occur at intersections. Therefore, it is urgent to improve the ability to perceive ship intention at intersections. In this paper, we propose an algorithm based on the fusion of image sequence and radar information to identify the navigation intention of ships at intersections. Some existing algorithms generally use the Automatic Identification System (AIS) to identify ship intentions but ignore the problems of AIS delay and data loss, resulting in unsatisfactory effectiveness and accuracy of intention recognition. Firstly, to obtain the relationship between radar and image, a cooperative target composed of a group of concentric circles and a central positioning radar angle reflector is designed. Secondly, the corresponding relationship of radar and image characteristic matrix is obtained after employing the RANSAC method to fit radar and image detection information; then, the homographic matrix is solved to realize radar and image data matching. Thirdly, the YOLOv5 detector is used to track the ship motion in the image sequence. The visual measurement model based on continuous object tracking is established to extract the ship motion parameters. Finally, the motion intention of the ship is predicted by integrating the extracted ship motion features with the position information of the shallow layer using a Bayesian framework. Many experiments on real data sets show that our proposed method is superior to the most advanced method for ship intention identification at intersections.

Retraction Note: Spatial distribution of heavy metals in groundwater based on structural equation and development of leisure agriculture tourism

This paper mainly from the structural equation through conventional measurement and visual measurement model, analyzes the relationship between groundwater heavy metals and it, to establish the measurement model on the spatial distribution of groundwater heavy metals influencing factor model, analyzes the influence effect of various indicators on the spatial distribution of groundwater heavy metals, and their mutual influence effect. However, due to many characteristics of heavy metal groundwater, long-term use and management, it has become the research focus in the field of potential environmental protection, and has become an indispensable part of this study. Heavy metal pollution in groundwater not only endangers the safety of ecosystem, but also seriously damages the health of residents. A typical integration area between urban and rural areas is selected to understand the current situation of heavy metal pollution in groundwater, analyze the spatial distribution characteristics of heavy metal ions, and conduct qualitative and quantitative research on heavy metal pollution sources in groundwater, which has important theoretical and practical value for promoting urban sprawl, It also provides a scientific basis for the local government to formulate the strategy of protecting water resources. Of course, this problem also has a certain impact on leisure agricultural tourism, and leisure agricultural tourism is one of the important ways to promote the structural adjustment of agricultural sector and increase farmers’ income, which is also an important practical process of developing multifunctional agriculture. In this paper, through structural equation analysis of underground heavy metal factor model, to understand its impact on leisure agricultural tourism, promote the development of leisure agricultural tourism.

RETRACTED ARTICLE: Spatial distribution of heavy metals in groundwater based on structural equation and development of leisure agriculture tourism

This paper mainly from the structural equation through conventional measurement and visual measurement model, analyzes the relationship between groundwater heavy metals and it, to establish the measurement model on the spatial distribution of groundwater heavy metals influencing factor model, analyzes the influence effect of various indicators on the spatial distribution of groundwater heavy metals, and their mutual influence effect. However, due to many characteristics of heavy metal groundwater, long-term use and management, it has become the research focus in the field of potential environmental protection, and has become an indispensable part of this study. Heavy metal pollution in groundwater not only endangers the safety of ecosystem, but also seriously damages the health of residents. A typical integration area between urban and rural areas is selected to understand the current situation of heavy metal pollution in groundwater, analyze the spatial distribution characteristics of heavy metal ions, and conduct qualitative and quantitative research on heavy metal pollution sources in groundwater, which has important theoretical and practical value for promoting urban sprawl, It also provides a scientific basis for the local government to formulate the strategy of protecting water resources. Of course, this problem also has a certain impact on leisure agricultural tourism, and leisure agricultural tourism is one of the important ways to promote the structural adjustment of agricultural sector and increase farmers’ income, which is also an important practical process of developing multifunctional agriculture. In this paper, through structural equation analysis of underground heavy metal factor model, to understand its impact on leisure agricultural tourism, promote the development of leisure agricultural tourism.

Research on lightweight web intrusion active defense key technology and visual measurement model based on dynamic ip black list

In view of the existing passive detection and prevention methods and lack of commercial products are expensive and complex deployment problem of a simple and fast active defense technology of WEB dynamic real-time active defense, in order to solve the problem of attack and defense for WEB server. Firstly, based on reverse proxy massive WAF logs, the mathematical model of user access behavior is established, and the behavior characteristics of malicious WEB attacks are studied through data mining analysis. Then, a threat factor algorithm and a dynamic IP blacklist generation algorithm are designed based on these features and a custom rule base. Finally, based on visual measurement model, D3 WEB visualization technology is used to monitor and measure WEB intrusion. The technology reduces the false alarm and false positive rate of active defense, improves the defense effect and execution efficiency, and ensures the quality of normal network communication.

Consistent Monocular Ackermann Visual-Inertial Odometry for Intelligent and Connected Vehicle Localization.

The observability of the scale direction in visual–inertial odometry (VIO) under degenerate motions of intelligent and connected vehicles can be improved by fusing Ackermann error state measurements. However, the relative kinematic error measurement model assumes that the vehicle velocity is constant between two consecutive camera states, which degrades the positioning accuracy. To address this problem, a consistent monocular Ackermann VIO, termed MAVIO, is proposed to combine the vehicle velocity and yaw angular rate error measurements, taking into account the lever arm effect between the vehicle and inertial measurement unit (IMU) coordinates with a tightly coupled filter-based mechanism. The lever arm effect is firstly introduced to improve the reliability for information exchange between the vehicle and IMU coordinates. Then, the process model and monocular visual measurement model are presented. Subsequently, the vehicle velocity and yaw angular rate error measurements are directly used to refine the estimator after visual observation. To obtain a global position for the vehicle, the raw Global Navigation Satellite System (GNSS) error measurement model, termed MAVIO-GNSS, is introduced to further improve the performance of MAVIO. The observability, consistency and positioning accuracy were comprehensively compared using real-world datasets. The experimental results demonstrated that MAVIO not only improved the observability of the VIO scale direction under the degenerate motions of ground vehicles, but also resolved the inconsistency problem of the relative kinematic error measurement model of the vehicle to further improve the positioning accuracy. Moreover, MAVIO-GNSS further improved the vehicle positioning accuracy under a long-distance driving state. The source code is publicly available for the benefit of the robotics community.

Robust Visual-Inertial Integrated Navigation System Aided by Online Sensor Model Adaption for Autonomous Ground Vehicles in Urban Areas

The visual-inertial integrated navigation system (VINS) has been extensively studied over the past decades to provide accurate and low-cost positioning solutions for autonomous systems. Satisfactory performance can be obtained in an ideal scenario with sufficient and static environment features. However, there are usually numerous dynamic objects in deep urban areas, and these moving objects can severely distort the feature-tracking process which is critical to the feature-based VINS. One well-known method that mitigates the effects of dynamic objects is to detect vehicles using deep neural networks and remove the features belonging to surrounding vehicles. However, excessive feature exclusion can severely distort the geometry of feature distribution, leading to limited visual measurements. Instead of directly eliminating the features from dynamic objects, this study proposes to adopt the visual measurement model based on the quality of feature tracking to improve the performance of the VINS. First, a self-tuning covariance estimation approach is proposed to model the uncertainty of each feature measurement by integrating two parts: (1) the geometry of feature distribution (GFD); (2) the quality of feature tracking. Second, an adaptive M-estimator is proposed to correct the measurement residual model to further mitigate the effects of outlier measurements, like the dynamic features. Different from the conventional M-estimator, the proposed method effectively alleviates the reliance on the excessive parameterization of the M-estimator. Experiments were conducted in typical urban areas of Hong Kong with numerous dynamic objects. The results show that the proposed method could effectively mitigate the effects of dynamic objects and improved accuracy of the VINS is obtained when compared with the conventional VINS method.

Developing Computationally Efficient Nonlinear Cubature Kalman Filtering for Visual Inertial Odometry

This paper presents a computationally efficient sensor-fusion algorithm for visual inertial odometry (VIO). The paper utilizes trifocal tensor geometry (TTG) for visual measurement model and a nonlinear deterministic-sampling-based filter known as cubature Kalman filter (CKF) to handle the system nonlinearity. The TTG-based approach is developed to replace the computationally expensive three-dimensional-feature-point reconstruction in the conventional VIO system. This replacement has simplified the system architecture and reduced the processing time significantly. The CKF is formulated for the VIO problem, which helps to achieve a better estimation accuracy and robust performance than the conventional extended Kalman filter (EKF). This paper also addresses the computationally efficient issue associated with Kalman filtering structure using cubature information filter (CIF), the CKF version on information domain. The CIF execution avoids the inverse computation of the high-dimensional innovation covariance matrix, which in turn further improves the computational efficiency of the VIO system. Several experiments use the publicly available datasets for validation and comparing against many other VIO algorithms available in the recent literature. Overall, this proposed algorithm can be implemented as a fast VIO solution for high-speed autonomous robotic systems.

Tight Fusion of a Monocular Camera, MEMS-IMU, and Single-Frequency Multi-GNSS RTK for Precise Navigation in GNSS-Challenged Environments

Precise position, velocity, and attitude is essential for self-driving cars and unmanned aerial vehicles (UAVs). The integration of global navigation satellite system (GNSS) real-time kinematics (RTK) and inertial measurement units (IMUs) is able to provide high-accuracy navigation solutions in open-sky conditions, but the accuracy will be degraded severely in GNSS-challenged environments, especially integrated with the low-cost microelectromechanical system (MEMS) IMUs. In order to navigate in GNSS-denied environments, the visual–inertial system has been widely adopted due to its complementary characteristics, but it suffers from error accumulation. In this contribution, we tightly integrate the raw measurements from the single-frequency multi-GNSS RTK, MEMS-IMU, and monocular camera through the extended Kalman filter (EKF) to enhance the navigation performance in terms of accuracy, continuity, and availability. The visual measurement model from the well-known multistate constraint Kalman filter (MSCKF) is combined with the double-differenced GNSS measurement model to update the integration filter. A field vehicular experiment was carried out in GNSS-challenged environments to evaluate the performance of the proposed algorithm. Results indicate that both multi-GNSS and vision contribute significantly to the centimeter-level positioning availability in GNSS-challenged environments. Meanwhile, the velocity and attitude accuracy can be greatly improved by using the tightly-coupled multi-GNSS RTK/INS/Vision integration, especially for the yaw angle.

Computer Visual Measurement Technology and Algorithm Simulation for the Assembly of Large Aircraft Parts

This research aims to solve the issues of limited measuring range and great accumulative error in the digital assembly of aircraft parts. In this paper, we propose the use of array visual measurement technology for the assembly of large aircraft parts. First, the visual measurement space for large aircraft parts assembly is determined. Second, the visual measurement model for large aircraft parts is constructed. Then, the differences that occur in real-time to the global coordinates can be calculated by using the pre-assembly feature points of large parts and the measurement tools of an array visual system. Finally, the real-time simulation of the aircraft assembly process is conducted in ADAMS by the secondary development of the software. In addition, errors between the real-time assembly and the design model are solved, and then transmitted to the mechanical actuators, which in turn adjust their attitude to complete the assembly of the large aircraft parts. The results show that array visual measurement technology for the assembly of large aircraft parts is feasible and efficient.

Research of Real-time Grabbing Yarn Tube System Based on Machine Vision

The current yarn tube manipulator just finishes yarn tube grabbing work according to the fixed coordinates. In the actual production process, equipment problems or human factors which make the spindles not on fixed coordinates cause the damage of the manipulator. Real-time grabbing yarn tube system with visual sensing has been designed and a extraction algorithm of spindles coordinates based on a mixed image morphology and Hough transform algorithm has been proposed. Through the combination of the yarn tube image characteristics which are extracted by the algorithm and the visual measurement model which is established by pinhole imaging principle, the mapping relation of yarn tube image coordinates and world coordinates has been gained to get the location information of yarn tube in real time. Results show that the proposed method could make the robot complete the grabbing job precisely and efficiently, under which the system meet the requirement of spinning and dyeing production line.

Autonomous target capturing of free-floating space robot: Theory and experiments

SUMMARYSpace robotic systems are expected to play an increasingly important role in the future. Unlike on the earth, space operations require the ability to work in the unstructured environment. Some autonomous behaviors are necessary to perform complex and difficult tasks in space. This level of autonomy relies not only on vision, force, torque, and tactile sensors, but also the advanced planning and decision capabilities. In this paper, the authors study the autonomous target capturing from the issues of theory and experiments. Firstly, we deduce the kinematic and dynamic equations of space robotic system. Secondly, the visual measurement model of hand–eye camera is created, and the image processing algorithms to extract the target features are introduced. Thirdly, we propose an autonomous trajectory planning method, directly using the 2D image features. The method predicts the target motion, plans the end-effector's velocities and solves the inverse kinematic equations using practical approach to avoid the dynamic singularities. At last, numeric simulation and experiment results are given. The ground experiment system is set up based on the concept of dynamic simulation and kinematic equivalence. With the system, the experiments of autonomous capturing a target by a free-floating space robot, composed of a 6-DOF manipulator and a satellite as its base, are conducted, and the results validate the proposed algorithm.