Analysis of a Distance Measurement System Based on Monocular Camera

Remote safety system for a robot tractor using a monocular camera and a YOLO-based method

To promote underwater construction, the tools such as underwater robotic manipulator system, underwater vehicles and so on are necessary. Furthermore, these system need an accurate 3D position parameters. If no position information for automatic control system that system is difficult to be operated. Hence, it is important to take position information. Although 3D position camera can offer 3D position parameters, it has still some deviation. In addition, the manipulator system need accurate distance information for fast and accurate operation. Hence, an accurate distance measurement system is necessary for a remotely teleoperated robotic manipulator system for underwater constructions. This paper focus on the distance measurement study to offer a accurate target distance information. The information can combine with 3D position camera to obtain more accurate 3D position parameters to offer a useful position information for underwater constructions, with a remotely teleoperated robotic manipulator system. In this paper, three distance measurement system, includes commercial ultrasonic distance measurement system, fabricated ultrasonic system and a laser distance measurement system is study. The commercial distance measurement system have a distance measurement system rang 8~35 cm, plusmn0.1 cm. However, a short distance measurement is difficult to be measured with the measurement system. On the other side, the fabricated measurement system can measure a short distance. Information from 0~5 cm, accuracy plusmn0.3 cm. The fabricated distance measurement system fabricated with PZT thin film. In this study, a horn is added to improve the accuracy of distance measurement. In addition, the laser distance measurement system, measurement system is used to measure the distance information also in this study. The combination of these distance measurement system to form a multiple range distance measurement can offer a long measurement range for accurate distance measurement underwater. A detailed experiment is to be studied in the future. Furthermore, the distance measurement system has been offered a distance measurement in a remotely teleoperated robotic manipulator system for underwater constructions

We propose simple three-dimensional measurement system in this paper. We think this system introduce a vehicle. This system is cheaper and easier than existing methods because, this system need only monocular camera and one light source. In addition we propose method re-create absorbed luminance by the texture to be able to measure any objects. Proposed method must keep pace surface that have high absorptive power because, proposed method use luminance distribution. This method use two light source. It can introduce vehicle very easy because, vehicle have back light and tail lamp.

The identification and classification of obstacles in navigable and non-navigable regions, as well as the measurement of distances, are crucial topics of investigation in the field of autonomous navigation for unmanned surface vehicles (USVs). Currently, USVs mostly rely on LiDAR and ultrasound technology for the purpose of detecting impediments that exist on water surfaces. However, it is worth noting that these approaches lack the capability to accurately discern the precise nature or classification of those obstacles. Nevertheless, the limited optical range of unmanned vessels hinders their ability to comprehensively perceive the entirety of the surrounding information. A cooperative USV-UAV system is proposed to ensure the visual perception ability of USVs. The multi-object recognition, semantic segmentation, and obstacle ranging through USV and unmanned aerial vehicle (UAV) perspectives are selected to validate the performance of a cooperative USV-UAV system. The you only look once-X (YOLOX) model, the proportional–integral–derivative-NET (PIDNet) model, and distance measurements based on a monocular camera are utilized to realize these problems. The results indicate that by integrating the viewpoints of USVs and UAVs, a collaborative USV-UAV system, employing the aforementioned methods, can successfully detect and classify different objects surrounding the USV. Additionally, it can differentiate between navigable and non-navigable regions for unmanned vessels through visual recognition, while accurately determining the distance between the USV and obstacles.

This paper describes an angle measurement system comprising a monocular camera. In robotics engineering, in order to avoid impairing the advantage of the pneumatic actuator, an angle measurement system that does not have an electrical element is required. In the field of human motion measurement, the use of quantitative evaluation is limited because sufficiently accurate systems are expensive and bulky. To overcome this problem, we introduce a Moire pattern that magnifies angular displacements to compensate for the low resolution of the camera. To facilitate the practical application of the angle measurement system, we use a novel Moire pattern for magnifying the rotation of the linear motion. We then experimentally verified the robustness of the measurement system results in case of a practical application.

This paper proposes a new localization method using monocular camera. A camera is hanging on the ceiling toward the ground. Its optical axis is perpendicular to the ground as possible. Its intrinsic parameters are calibrated in advance offline. Its extrinsic parameters refer to the chessboard reference are calibrated online. Then a method is proposed to estimate the 3D positions and orientations of the specified arrow targets in the reference frame with only one captured image. The experimental results in indoor environment verify the effectiveness of the proposed method.

The pose of space target is a key technical parameter in navigation guidance. In launching silo, the missile pose estimation is difficult because of specific mounting location and limited field of view of cameras. To solve these problems, we design a pose measurement system which has three measurement methods based on binocular intersection, central axis and monocular camera respectively. Synthetic experiments and real image experiments show that the system is feasible and the precision of the system can meet the accuracy demand of measurement in launching silo.

This article proposes a new method for kinematic identification of an industrial robot using a monocular camera mounted on its end-effector. Measurement of end-effector 3-D poses required as input for this process is bypassed by measurements solely in 2-D image space, thus permitting estimation of corrections in parameters in a single stage. This is in contrast with multiple stages used earlier. The use of camera enabled the combination of geometric and parametric techniques of identification. The advantages are well-defined experimental strategy and identification of externally defined coordinate frames. Kinematic identification using measurements in 2-D image space was not affected by issues associated with observability which is commonly encountered when measurements of 3-D pose are used. This is because of the fact that the algorithm used for this purpose utilized identification Jacobian which relates measurements in image space to corrections in parameters. It was computed using backward recursion. Hand-eye transformation was also obtained during the identification process, thus minimizing the number of steps or hardware that is involved. The results were verified through simulation and validated through multiple experiments including those based on ISO standards, on KUKA KR 5 arc industrial robot. Some of the results were comparable to that obtained using a laser tracker which is a popular but expensive and offline measurement system which is not commonly available. Robustness of the proposed method to measurement noise has also been demonstrated through simulation. The experiments were conducted and the estimated parameters could be used in an already existing system used for bin-picking.

MC-LRF based pose measurement system for shipborne aircraft automatic landing

Noncooperative target pose (position and attitude) measurement is very important for on-orbital servicing tasks, such as malfunctioned spacecraft capturing and repairing, space debris removal, and so on. Traditional stereovision-based methods require the two cameras to observe the same object and form a common observation area, limiting system flexibility, and measurement distance. In this paper, a virtual stereovision (VSV) modeling and pose measurement method are proposed for a dual-arm space robotic system. Each arm only carries one camera to observe different objects. The geometry features (i.e., the triangle, quadrilateral, or circular features) are independently extracted from the images of each camera. The two separate cameras are mapped to a stereo measurement range and the, respectively, identified features are converted into an equivalent “virtual common feature.” Then, a “VSV measurement system” is constructed. The rocket nozzle and the triangular support of the satellite are considered as different objects to be observed by the two cameras of a dual-arm space robot. The pose of the nozzles and the triangular brackets is further achieved based on the VSV information. Finally, we developed an experimental system, which is composed of a satellite mockup, two monocular cameras, and high precision laser tracker (used to evaluate the vision measure accuracy). The experimental results verified the proposed method. The average position error of the method is less than 6.1367 mm, and the average attitude error is less than 2.415°.

Navigation in mobile robotics involves two tasks, keeping track of the robot's position and moving according to a control strategy. In addition, when no prior knowledge of the environment is available, the problem is even more difficult, as the robot has to build a map of its surroundings as it moves. These three problems ought to be solved in conjunction since they depend on each other. <br/>This thesis is about simultaneously controlling an autonomous vehicle, estimating its location and building the map of the environment. The main objective is to analyse the problem from a control theoretical perspective based on the EKF-SLAM implementation. <br/>The contribution of this thesis is the analysis of system's properties such as observability, controllability and stability, which allow us to propose an appropriate navigation scheme that produces well-behaved estimators, controllers, and consequently, the system as a whole. <br/>We present a steady state analysis of the SLAM problem, identifying the conditions that lead to partial observability. It is shown that the effects of partial observability appear even in the ideal linear Gaussian case. This indicates that linearisation alone is not the only cause of SLAM inconsistency, and that observability must be achieved as a prerequisite to tackling the effects of linearisation. Additionally, full observability is also shown to be necessary during diagonalisation of the covariance matrix, an approach often used to reduce the computational complexity of the SLAM algorithm, and which leads to full controllability as we show in this work.<br/>Focusing specifically on the case of a system with a single monocular camera, we present an observability analysis using the nullspace basis of the stripped observability matrix. The aim is to get a better understanding of the well known intuitive behaviour of this type of systems, such as the need for triangulation to features from different positions in order to get accurate relative pose estimates between vehicle and camera. Through characterisation the unobservable directions in monocular SLAM, we are able to identify the vehicle motions required to maximise the number of observable states in the system. <br/>When closing the control loop of the SLAM system, both the feedback controller and the estimator are shown to be asymptotically stable. Furthermore, we show that the tracking error does not influence the estimation performance of a fully observable system and viceversa, that control is not affected by the estimation. Because of this, a higher level motion strategy is required in order to enhance estimation, specially needed while performing SLAM with a single camera. Considering a real-time application, we propose a control strategy to optimise both the localisation of the vehicle and the feature map by computing the most appropriate control actions or movements. The actions are chosen in order to maximise an information theoretic metric. Simulations and real-time experiments are performed to demonstrate the feasibility of the proposed control strategy.

This paper presents a novel person to camera distance measuring system based on eye-distance. The distance between centers of two eyes is used for measuring the person to camera distance. The variation in eye-distance (in pixels) with the changes in camera to person distance (in inches) is used to formulate the distance measuring system. The system starts with computing the distance between two eyes of a person and then person to camera distance is measured. The proposed distance measurement system is relatively simple and inexpensive to implement as it does not require any other external distance measuring tools. Experimental results show the effectiveness of the system with an average accuracy of 94.11%.

Author Summary: In modern process industry it is necessary to find “smart” ways to continuously satisfy customers’ demands, strive for more efficient production and prioritise the focus on safety, health and environment, allowing dedicated companies to obtain a competitive advantage. To achieve individual goals in these areas, companies increasingly implement a so-called business system approach, which ensures systematic, continuous improvement initiatives in all sectors. Most business systems address improvement in process control, since in many cases there is a significant positive economic potential which often only requires minor capital investments. This is also the case for Glencore Nikkelverk, Norway, where it has been decided to transform a former project-based improvement framework into a holistic business system, the Nikkelverk Business System (NBS). One pillar consists of different tools to improve process performance, with the objective to identify critical processes and process stages a.o. and also to ensure that the measurement systems in use can be geared, and validated, to mainly capture process variations, allowing to document valid process improvements. At Nikkelverk there are between 6000 and 7000 measurement points, of which one-third are used for process regulation purposes. It is therefore critically important to be able to monitor and verify that measurement system uncertainty is suitably low—ideally at a level of 10% relative or below compared to the variation of the process being monitored. This will become a key priority. An optimal measurement system should therefore not contribute significantly to the total apparent process variation as revealed by raw process data that are used to track when a process is deviating from steady state or dangerously close to control limits, or gets outside relevant safety limits. Critical processes must be kept under constant monitoring and control to ensure that all improvement attempts work from a solid database. As but two examples, energy and material consumption, can be fine-tuned to a lowest possible level only when both processes themselves and the measurement systems are monitored and controlled properly. This not only contributes to the cost efficiency of the company, but enforces process monitoring and control to take a leading role. We here focus specifically on the relative merits of variographic analysis (Theory of Sampling) and measurement system analysis (Six Sigma) and show how the former can function as a very effective screening for the much more costly latter.

The wide application of WAMS (Wide Area Measurement System) provided a new thinking for transient stability analysis of power system. Based on real-time measurement information from WAMS, a method to quickly analysis power system transient stability that does not depend on network structure, model and parameters of power system is proposed in this paper. Firstly, the parameters of system are identified online by improved least squares method on basis of that the multi-machine power system is equivalently reduction to one machine infinity bus system. The equilibrium point and stability margin of equivalent system are rapidly computed, and then the stability of system is quickly judged. The simulation results show that the algorithm can accurately judge transient stability of system and has better antidisturbance ability and robustness. The issue of dependence of transient stability analysis on system model and parameter is solved. The adverse influences of data lose and measurement errors in actual measurement of the WAMS on analysis results are avoided effectively. Keywords-power system; wide-area measurement system; parameter identification; energy function; transient stability analysis

RGB-D sensor frameworks such as the PrimeSense/Kinect have brought a massive change in the range of applications for the usage of depth data in not just core robotic and computer vision systems, but also in security, entertainment and medical faculties among others. Such projected texture range measurement systems have also effectively substituted traditional range sensor systems such as Laser and Lidar, which are not just bulky and expensive, but offer poor resolution/unit cost and low speed of usage. On the other hand, generic RGB-D sensor frameworks (as opposed to integrated RGB-D cameras) that provide flexibility in terms of usage of variegated monocular color and range image sensors form the future of computer vision applications. Unlike fixed RGB-D frameworks, these generic frameworks require explicit cross-calibration between the range and the monocular color image sensors. Traditional 2D checkerboard or similar alternate calibration patterns do not provide the necessary sensory response across the varied sensing modalities for accurate cross-calibration. To address this concern, we present a novel framework for extrinsic cross-calibration of variegated monocular and range sensors by extension of the traditional checkerboard pattern used for monocular or stereo calibration into a 3D checkerboard framework. A suite of computer vision techniques are also presented in order to obtain the necessary calibration parameters using the presented calibration pattern. Results presented show successful detection of correspondence points and estimation of extrinsic parameters for cross-calibration. It can also be seen that the error in the system increases with depth as the estimates from the Kinect sensor become unreliable.