Enhanced Attitude and Altitude Estimation for Indoor Autonomous UAVs

In recent years the use of Unmanned Aerial Vehicles (UAVs) has considerably grown in the civil sectors, due to their high flexibility of use. Currently, two important key points are making them more and more successful in the civil field, namely the decrease of production costs and the increase in navigation accuracy. In this paper, we propose a Kalman filtering-based sensor fusion algorithm, using a low cost navigation platform that contains an inertial measurement unit (IMU), five ultrasonic ranging sensors and an optical flow camera. The aim is to improve navigation in indoor or GPS-denied environments. A multi-rate version of the Extended Kalman Filter is considered to deal with the use of heterogeneous sensors with different sampling rates, and the presence of non-linearities in the model. The effectiveness of the proposed sensor platform is evaluated by means of numerical tests on the dynamic flight simulator of a quadrotor. Results show high precision and robustness of the attitude estimation algorithm, with a reduced computational cost, being ready to be implemented on low-cost platforms.

Researching and building a method for testing and adjusting the strapdown inertial navigation system

The strap-down inertial navigation system (SINS) is widely used and becoming very important in many areas, especially in the arms industry when the GPS signal is lost or not reliable. To ensure the precision of the system, in addition to optimizing the algorithm for the strap-down inertial navigation system, the testing, and adjusting of the SINS system when installed on vehicles also play a vital role. In the article, a method of displaying SINS data on a digital map is proposed. Furthermore, the article also proposes a method to assess the influence of misalignment angles on the navigation accuracy and how to estimate and correct them.

Key Techniques of SINS/DVL Integrated Navigation System

Doppler Velocity Log (DVL) aided Strapdown Inertial Navigation System (SINS) is commonly used for the applications of Autonomous Underwater Vehicles (AUVs). In lack of other aiding sensors, how to maintain precise integrated navigation is still a challenge issue. In this paper, the structure and basic principles of the SINS is presented. Alignment calibration of SINS and DVL, fast in-motion alignment and data fusion are key factors which has great influence on the navigation accuracy. Research efforts taken in these fields in recent years are surveyed. This paper may provide a firm foundation for the researchers in related areas.

How to iGuide: flat panel detector, CT-assisted, minimally invasive evacuation of intracranial hematomas

Evidence is growing to support minimally invasive surgical evacuation of intraparenchymal hematomas, particularly those with minimal residual hematoma volumes following evacuation. To maximize the potential for neurologic recovery, it is imperative that the trajectory for access to the hematoma minimizes disruption of normal parenchyma. Flat panel detector CT-based navigation and needle guidance software provides a platform that uses flat panel detector CT imaging obtained on the angiography table to aid reliable and safe access to the hematoma. In addition to providing a high degree of accuracy, this method also allows convenient and rapid re-imaging to assess navigation accuracy and the degree of hematoma evacuation prior to procedural completion. We provide a practical review of the syngo iGuide needle guidance software and the methodology for incorporating its use, and the software of other vendors, in a variety of minimally invasive methods for evacuation of intraparenchymal hematomas.

Multidimensional routing with the increased navigation accuracy with the maintenance of flight notifications of the flight vehicles

The article covers the problem the multidimensional routing of flights for the transportation of cargo and mail, with the condition of the corresponding equipment presence for performing navigation of increased precision to obtain the possibility of the formation flights under any weather conditions. The given circumstances are capably essential to reduce load while using the airspace, which will make it possible to achieve transportation independent of its saturation. While planning the routes it is also necessary to consider the interests of different interested groups, which are often opposite to one another. In the view of the different directivity of the tasks in question, the solution can require the sorting as excessively as large, so the smaller quantity of possible situations (versions of the solution), the lower the level of the calculation of these versions is, and the greater their quantity is. The exact example of multidimensional routing, which is affected by the interests of operational nature and the interests of the urgency of the performance of the claims, expressed by weight coefficients, is depicted in this work. The only version in favour of the general production process, which is obtained with the help of a genetic algorithm, is a solution of this problem. It was necessary to introduce some designations and assumptions, the enumeration of which can be supplemented. Optimal solution can be obtained both by the enumeration of the solution versions and with the help of the genetic algorithm, which is allowed for a smaller number of iterations, to obtain suboptimal in real time, which corresponds to the conditions of the task solution. In that the example dynamic priorities are assigned, based on multiplicative form by expert evaluation, which form criteria for the ranking of request for each step of route planning. As a result, there are the exact versions of the solution, which correspond to the interests of different groups and the version, obtained with the help of a genetic algorithm, which satisfy the opposite interests of these groups. All versions of the solution are proved to be different, which indicates the need of applying the objective and substantiated apparatus for making the decision, which the genetic algorithm actually is. The proposed mathematical apparatus has prospects for implementation.

An Intelligent Networked Car-Hailing System Based on the Multi Sensor Fusion and UWB Positioning Technology under Complex Scenes Condition

In order to solve the problems of difficulty and long times to pick up cars in complex traffic scenes, this paper proposes an intelligent networked car-hailing system in complex scenes based on multi sensor fusion and Ultra-Wide-Band (UWB) technology. UWB positioning technology is adopted in the system, and the positioning data is optimized by the untraceable Kalman filter algorithm. Based on the environment perception technology of multi sensor fusion, such as machine vision and laser radar technology, an anti-collision warning algorithm was proposed in the process of car-hailing, which improved the safety factor of car-hailing. When the owner enters the parking lot, the intelligent vehicle can automatically locate the owner’s position and drive to the owner without human intervention, which provides a new idea for the development of intelligent networked vehicles and effectively improves the navigation accuracy and intelligence of intelligent vehicles.

ZUPT-aided INS Bypassing Stance Phase Detection by Using Foot-Instability-Based Adaptive Covariance

<p>In this paper, we propose a Foot-Instability-Based Adaptive (FIBA) covariance to dynamically adjust the covariance matrix for the pseudo-zero-velocity measurements in the Zero velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS). The proposed ZUPT-aided INS using the FIBA covariance is implemented in an Adaptive Extended Kalman Filter (AEKF) framework, where the measurement covariance matrix is updated in each iteration according to the FIBA covariance. The FIBA covariance is designed to have a very high value during the swing phases in a gait cycle, and the value significantly decreases during the stance phases. As a result, the proposed method eliminates a need to use a binary stance phase detector in implementation of the ZUPT-aided INS. Two series of indoor pedestrian navigation experiments were conducted to investigate the navigation performance of the algorithm. In the first series of experiments, which included cases of walking and running, localization solutions produced by the system using the FIBA covariance demonstrated 36% and 64% improvements in navigation accuracy along the horizontal and vertical directions, respectively. In the second series of experiments, which included a pedestrian walking on different indoor terrains, such as flat planes, stairs, and ramps, the navigation accuracy of the system using the FIBA covariance reduced horizontal and vertical position errors by 12% and 45%, respectively, as compared to the conventional ZUPT-aided INS.</p>

ZUPT-aided INS Bypassing Stance Phase Detection by Using Foot-Instability-Based Adaptive Covariance

<p>In this paper, we propose a Foot-Instability-Based Adaptive (FIBA) covariance to dynamically adjust the covariance matrix for the pseudo-zero-velocity measurements in the Zero velocity UPdaTe (ZUPT)-aided Inertial Navigation Systems (INS). The proposed ZUPT-aided INS using the FIBA covariance is implemented in an Adaptive Extended Kalman Filter (AEKF) framework, where the measurement covariance matrix is updated in each iteration according to the FIBA covariance. The FIBA covariance is designed to have a very high value during the swing phases in a gait cycle, and the value significantly decreases during the stance phases. As a result, the proposed method eliminates a need to use a binary stance phase detector in implementation of the ZUPT-aided INS. Two series of indoor pedestrian navigation experiments were conducted to investigate the navigation performance of the algorithm. In the first series of experiments, which included cases of walking and running, localization solutions produced by the system using the FIBA covariance demonstrated 36% and 64% improvements in navigation accuracy along the horizontal and vertical directions, respectively. In the second series of experiments, which included a pedestrian walking on different indoor terrains, such as flat planes, stairs, and ramps, the navigation accuracy of the system using the FIBA covariance reduced horizontal and vertical position errors by 12% and 45%, respectively, as compared to the conventional ZUPT-aided INS.</p>

Modified Sage-Husa Adaptive Kalman Filter-Based SINS/DVL Integrated Navigation System for AUV

This paper presents a modified Sage-Husa adaptive Kalman filter-based SINS/DVL integrated navigation system for the autonomous underwater vehicle (AUV), where DVL is employed to correct the navigation errors of SINS that accumulate over time. When negative definite items are large enough, different from the positive definiteness of noise matrices which cannot be guaranteed for the conventional Sage-Husa adaptive Kalman filter, the proposed modified Sage-Husa adaptive Kalman filter deletes the negative definite items of adaptive update laws of the noise matrix to ensure the convergence of the Sage-Husa adaptive Kalman filter. In other words, this method sacrifices some filtering precision to ensure the stability of the filter. The simulation tests are implemented to verify that expected navigation accuracy for AUV can be obtained using the proposed modified Sage-Husa adaptive Kalman filter.