Navigation Techniques Within Complex Underwater Structures

Underwater structures are crucial for national economic and social development. However, because of their complex environment, they are susceptible to damage during service. This damage should be prevented to minimize casualties and economic loss. Therefore, this study investigates the problems of disease identification and area statistics of underwater structures. To this end, the Dark‐Retinex (DR) algorithm that can enhance the image of underwater structure defects is proposed. The algorithm consists of a combination of a dark channel algorithm and the Retinex algorithm. This study analyzes the current research status of underwater image processing technology, designs the overall framework of the DR algorithm, and uses the underwater structure disease image to verify the algorithm. Comparing the effect of the image with only the dark channel defogging and DR algorithm processing, the DR algorithm is observed to achieve “defogging” processing of underwater structural disease images to achieve better enhancement effects. Moreover, for accurate disease area statistics, the binary morphology and optimal threshold segmentation theories are combined to perform disease edge screening and remove interference information. Finally, accurate statistics of the proportion of diseased pixels are achieved, as well as the quantitative detection of surface diseases of underwater structures. After actual operational verification, the improved image dehazing and parallel boundary screening algorithms can achieve better application results to detect underwater structure defects and disease statistics. The objective evaluation shows that the DR algorithm facilitates image processing, can obtain relatively high‐quality target images, and can solve the problems of time‐consuming and labor‐intensive detection of underwater structures, with significant risks and limitations. This helps pave the way for (1) the actual engineering of surface structure detection of underwater structures, (2) future storage in the database and assessment of hazard levels, and (3) a guide for engineering technicians to take corresponding maintenance measures.

Demonstration that updating an inertial navigation system (INS) with VOR/DME information (from one or two stations) by means of a maximum-likelihood filter results in substantial improvements in navigational accuracy over that obtained by the use of a single VOR/DME (current practice). When continuously updating, the use of a high-quality INS (0.01 deg/hr gyro drift) instead of a low-quality INS (1.0 deg/hr gyro drift) does not substantially improve position accuracy. In-flight alignment (or realignment) of an INS to an accuracy comparable to that of ground alignment can be accomplished by using two DMEs. Several reduced-order suboptimal filters were found to perform nearly optimally.

Celestial navigation system (CNS) is a completely autonomous navigation technology with strong anti-interference ability and high navigation accuracy, which has a great application prospect. Aim at defect of traditional integrated navigation system using attitude angle measurement celestial navigation system and strapdown inertial navigation system (SINS), which can only correct of strapdown inertial navigation system’s gyro drift. This paper proposes an improved integrated navigation system that add double-star positioning celestial navigation to traditional SINS/CNS integrated navigation system, meanwhile, introduces the fault detection technology based on statistical distance to solve the problem that inaccuracy of measurement information leads to the integrated navigation system’s performance degradation. By analyzing the simulation results, the improved navigation system can inhibit divergence of system and obtain high accurate and stable navigation information.

Due to melting ice at the North Pole, navigation at high latitudes is increasing regularly. Therefore, navigation and survey at these high latitudes becomes more and more a key issue, especially for underwater operations where GNSS signals are not available. Navigation at high latitudes is a challenge for inertial navigation systems. Alignment of these systems with respect to North becomes more difficult when closer to the pole because the physical effect that is used becomes smaller and smaller when going to high latitudes. In order to evaluate heading accuracy and navigation performance of its inertial systems under these extreme conditions, iXBlue had the opportunity to test several of its navigation systems on a vessel up to 81 degrees of latitude during summer 2014. Thanks to the collaboration of the Norwegian Polar Institute, iXBlue has tested QUADRANS gyrocompass and PHINS navigation systems on the research Vessel Lance. The paper emphasizes on the potential difficulties encountered by inertial navigation systems at high latitudes. In a second part the paper presents and analyzes performance of all navigation systems and gyrocompasses under test in terms of heading accuracy and long term stability and navigation accuracy without GNSS aiding. The third part explains why we obtained extremely good results during this test campaign.

Honeywell Laboratories recently funded the dev- elopment of a prototype personal navigation system based on MEMS technologies. The system components include an MEMS inertial measurement unit, a three-axis magnetometer, a baro- metric pressure sensor, and a SAASM GPS receiver. The system also uses Honeywell's human motion-based pedometry algo- rithm. The navigation process is based on a strap-down inertial navigator aided by feedback from a Kalman filter using typical measurements from the GPS, magnetometer and barometer when available. A key innovation is the addition of an inde- pendent measurement of distance traveled based on use of a human motion algorithm. The navigation system combines the best features of dead reckoning and inertial navigation, resulting in positioning performance exceeding that achieved with either method alone. Subsequent to the Honeywell effort, DARPA funded an individual Personal Inertial Navigation System (iPINS) seedling program. Honeywell worked to improve the baseline personal navigation system with the objective of demonstrating the feasibility of reliably achieving navigation accuracy < 1% of distance traveled in GPS-denied scenarios. In addition, an analysis was conducted to determine the benefit of incorporating terrain correlation into the personal navigation system. The results of this analysis indicate that overall navigation accuracy can be significantly improved through the application of terrain correlation. This paper presents an overview of Honeywell Laboratories' prototype Personal Navigation system, the software architecture, and the personal navigation algorithms. Demonstration results from the DARPA iPINS seedling program are presented. In addition, the paper includes the conclusions from the terrain correlation analysis conducted under the iPINS seedling program.

A pulsed-laser ablation method for non-contact experimental vibration analysis of completely submerged underwater structures is proposed. Although impact testing with an impulse hammer is commonly used for vibration analysis due to its simplicity, impact testing has limited use in underwater conditions. An input-detection-free frequency response function measurement in water will greatly contribute to the development of high-precision and high-speed positioning autonomous underwater vehicles, underwater vehicle-manipulators, underwater robots, submarines, etc., which are used in dangerous conditions (e.g., deep oceans, under ice, and nuclear reactor plants). To achieve these high-performance underwater systems, vibrations due to hydrodynamic parameters (such as added mass, buoyant force, drag force, and damping coefficient) should be suppressed, and vibration tests should be conducted on the actual equipment submerged in water. The proposed method yields the frequency response function by applying a pulsed-laser-ablation excitation force to an underwater structure and measuring the output using a laser Doppler vibrometer. Because the direction, strength, and effective duration of the pulsed-laser-ablation force are essentially constant, this force can be estimated by measuring these properties in advance. Hence, the proposed method realizes input-detection-free frequency response function measurements in underwater conditions.

In order to flexibly monitor the underwater structure in the complex underwater environments, a small multi-thrusters AUV (Autonomous Underwater vehicle) has been designed with the attitude fusions of monocular vision and IMU (Inertial Measurement Unit). The tasks for detecting the surface faults of the underwater structures in designated area, such as large vessel propellers and offshore oil rig, determine the functional requirements of underwater image processing, flexible attitude control and autonomous navigation for AUV. In this paper, we present a novel design scheme of AUV, including the AUV hardware platform, the corresponding hydrodynamic model for “SAIL”, and the modified attitude fusion algorithm based on monocular vision and IMU. Finally, the experiments are carried out to evaluate the performances of the AUV.

The integrate of underwater structure of dams and bridges are very important for the safety of the projects. However, due to the complex flow field near the underwater structure, it is a challenge for the underwater robot to maintain stable near the underwater structures when performing inspection tasks. Moreover, it is difficult for underwater robots to absorb on the surface of underwater structures due to rough surface. This paper proposes a design of an adsorption track wheel for underwater wall-climbing robot, which combines the swirl suction cup and track wheel. The relationship between the water flow velocity, the wall gap and the suction cup adsorption force are analyzed. The influence of the surface curvature diameter of the structure and the diameter of the suction cup on the suction force is explored, and the structure of the suction cup is optimized to resist the influence of the water flow on the suction cup as much as possible while considering the flow velocity. After the suction cup improved, a prototype is designed and built. The final experiment demonstrates that the track wheel can provide sufficient adsorption force and friction force, which helps the structure to resist the water flow velocity up to 2.5m/s. Finally, it proposes the track wheel prototype can provide stable adsorption on the surface of underwater buildings for complex flow field.

The sidewalk is often located in complex environments such as buildings or covered by dense trees. Satellite navigation systems are susceptible to the surrounding environment, resulting in multipath effects, resulting in reduced positioning accuracy. Therefore, the global satellite navigation system (GNSS) and inertial navigation system (INS) based on kalman filter (KF) algorithm are studied, and the navigation algorithm and system model suitable for pedestrians are given. Considering that the pedestrian speed is low, the satellite navigation system obtains the carrier orientation information (heading angle), which also produces a large error. Therefore, the pedestrian dead reckoning (PDR) correction method is adopted to make the positional accuracy of the integrated navigation continuously stable. This paper proposes a pedestrian navigation system solution based on a single navigation device.

In the last decade, the demand for accurate land-vehicle navigation (LVN) in several applications has grown rapidly. In this context, the idea of integrating multisensor navigation systems was implemented. For LVN, the most efficient multisensor configuration is the system integrating an inertial navigation system (INS) and a global positioning system (GPS), where the GPS is used for providing position and velocity and the INS for providing orientation. The optimal estimation of the system errors is performed through a Kalman filter (KF). Unfortunately, a major problem occurs in all INS/GPS LVN applications that is caused by the frequent GPS signal blockages. In these cases, navigation is provided by the INS until satellite signals are reacquired. During such periods, navigation errors increase rapidly with time due to the time-dependent INS error behavior. For accurate positioning in these cases, some approaches, known as bridging algorithms, should be used to estimate improved navigation information. In...

A method is presented to synchronize and aid the absolute navigation solutions of the members of a swarm by means of relative inter-swarm measurements. Moreover, the operational benefits thereof are discussed. Collaborative operation of vehicles, especially of unmanned aerial vehicles in close formation, requires accurate relative and absolute navigation. A novel method for collaborative multi-sensors fusion of relative and absolute navigation information is presented designed to cope with GNSS-denied environment. Relative inter-swarm measurements and absolute navigation means cannot be fused straight away as they are defined in fundamental different frames. Moreover, the sensor fusion has to fulfill two objectives – firstly, restoring the relative geometry of the swarm, and secondly, determining the absolute positions of each vehicle in an optimal way for the overall swarm. Key for collaborative navigation is to establish a common knowledge within the swarm which is achieved by exchanging the absolute state estimations, the state uncertainties and the relative measurements. Based on the common knowledge the “Consistent INS Drift Algorithm” computes for each vehicle a new position which is statistically optimal for the overall swarm. Simultaneously, these new positions represent the actual geometry of the swarm by essentially resampling the relative measurements. By fusing these new positions the navigation filters are synchronized and the relative measurements are implicitly used to aid the absolute navigation solutions. This reveals several operational benefits: the absolute navigation solutions are consistent – the whole swarm drifts consistently as only the Inertial Navigation Systems (INS) may remain as absolute navigation means, and hence, the naming “Consistent INS Drift Algorithm”. Moreover, the drift is statistically slower than the best individual INS. Having at least one vehicle equipped with a drift-free navigation mean such as Terrain Referenced Navigation stops the swarm to drift. Therefore, heterogeneous navigation systems are natively supported enabling true collaboration.

Nowadays, unmanned aerial vehicles (UAVs) play a vital role in many applications and have been successfully employed in a wide variety of applications such as road surveying, precision agriculture, landslide monitoring, cultural heritage mapping, and pipeline monitoring. All of these applications require accurate and stable navigation system. Currently, most of the commercially available UAVs rely on the integration of the Global Satellite Navigation Systems (GNSS) and Inertial Navigation Systems (INS) to estimate the position, velocity, and attitude. The small form factor of modern UAVs allowed them to operate in more challenging environments such as urban and natural canyons. In these environments, the GNSS availability cannot be guaranteed, and hence the navigation solution will deteriorate dramatically during these GNSS signal outages due to the drift exhibited by the inertial navigation solution. Using other aiding sensors is crucial to limit the accumulated errors associated with INS measurements. Onboard cameras can offer a useful clue to support the navigation solution during the GNSS signal outage periods. Varieties of monocular visual odometry based on photogrammetric and Structure from Motion (SfM) approaches have been proposed to assist the navigation estimation process. The main problem of using the monocular VO technique is the loss of scale if neither external measurement nor a priori knowledge about the surrounding environment are available. Moreover, the estimated camera pose from VO is prone to drift with time. This paper introduces a novel approach for estimating the navigation states of an Unmanned Aerial Vehicle (UAV) by integrating the visual information that obtained from a monocular camera with the Inertial Measurement Unit (IMU) observations via an Extended Kalman Filter (EKF). Most of the current monocular VO algorithms rely on a calibrated camera model and apply the conventional photogrammetric and SfM approaches. While these approaches can help towards estimating the relative rotation and translation by tracking image features and applying geometrical constraints, they cannot estimate the motion scale using only the image visual features.

We describe an integrated navigation system based on Global Navigation Satellite Systems (GNSS), an Inertial Navigation System (INS) and terrestrial ranging technologies that can support accurate and seamless indoor-outdoor positioning. To overcome severe multipath disturbance in indoor environments, Locata technology is used in this navigation system. Such a “Locata-augmented” navigation system can operate in different positioning modes in both indoor and outdoor environments. In environments where GNSS is unavailable, e.g. indoors, the proposed system is designed to operate in the Locata/INS “loosely-integrated” mode. On the other hand, in outdoor environments, all GNSS, Locata and INS measurements are available, and all useful information can be fused via a decentralised Federated Kalman filter. To evaluate the proposed system for seamless indoor-outdoor positioning, an indoor-outdoor test was conducted at a metal-clad warehouse. The test results confirmed that the proposed navigation system can provide continuous and reliable position and attitude solutions, with the positioning accuracy being better than five centimetres.

Precise navigation is crucial for the long range unmanned aerial vehicles (UAVs). But today’s UAVs still rely on global navigation satellite system (GNSS) for accurate navigation; this brings in the safety issues as the weak satellite signals can be interfered. To achieve accurate autonomous navigation, we propose to utilize publicly available remote sensing maps to compensate the drifts from the inertial navigation systems. To deal with severe visual variations between the camera images and the map image, we propose a hierarchical outlier filtering strategy to filter out the feature correspondence with a voting algorithm at the first stage, filter out the wrong matched position with the predictions from the inertial navigation system at the second stage. Then an Extended Kalman Filter (EKF) is constructed to combine the matched position with the Inertial Navigation System (INS) to achieve accurate and drift free localization performance. To test the proposed algorithm, we have conducted long range real flight experiments with a CH-4 UAV and the overall localization error is less than 10 m.KeywordsImage matching locationIntegrated navigationLong range UAV

An expanded form of the third-order Markov undulation model is developed which includes undulation of the geoid, vertical deflections, gravity anomaly, and the anomalous gravity gradients. First, the complete set of autocorrelation and cross-correlation functions, as specified by the autocorrelation of undulation, is obtained. Then, a time-domain approach is used to synthesize the corresponding linear system driven by white noise that represents the anomalous gravity field behavior along a straight horizontal track. The resulting nine-state Markov model provides a shaping filter for applying covariance analysis and optimal estimation techniques to inertial navigation and gravity gradiometry systems analysis. ONTINUED mechanical refinement has brought inertial navigation technology to the stage where unmodeled variations in the Earth's gravity field have become significant and sometimes dominant error sources.1 At the same time, inertial navigation system analysts have been facing the task of developing error models to evaluate the impact of gravity uncertainties on navigation system performance. Ad- ditionally, the development of gravity gradiometers has made it necessary to model gravity-gradient signals to specify sensor requirements and analyze gradiometer-aided inertial navigation or gradiometry survey performance. To apply the powerful tools of linear system theory and Kalman filtering, it has become common to consider gravity uncertainties random variables, and to model them as Markov processes. Following the first treatment of vertical deflections as a first-order Markov process,2 a succession of higher order Markov models was proposed culminating with the third- order Markov undulation model3'4 which is fully self- consistent; that is, the auto- and cross-correlation functions of undulation, vertical deflections, and gravity anomaly obey and are derived according to the mathematical relationships of physical geodesy. The motivation for using Markov models for gravity un- certainties is that they can be cast in the form of linear dif- ferential equations driven by white noise. This allows use of the model as a shaping filter in covariance analysis of linear dynamic systems, Kalman filters, and optimal smoothing algorithms. The expanded third-order Markov undulation model described in this paper is derived from the original version, but it has three differences. First, it was found that the original model could easily be extended to include all the anomalous gravity gradients by continued differentiation of auto- and cross-correlation functions. Second, the exact cross-correlation function between undulation and gravity anomaly contains modified Bessel functions.4 Modified Bessel functions are solutions to a class of differential equations whose coefficients are functions of the independent variable. To obtain a Markov shaping filter, it is necessary to substitute an approximate function which is the solution to a linear differential equation with constant coefficients. While the chosen exponential form did not approximate the exact Bessel function as well as at first hoped possible, it provides a suitable representation of the un- dulation-gravity anomaly cross-correlation.