GPS-Free Terrain-Based Vehicle Tracking Performance as a Function of Inertial Sensor Characteristics

With the increasing demand for indoor navigation applications, indoor navigation has become a research hotspot in many technical fields. High-precision sensors are expensive, and they are often used in industrial and aerospace applications. Low-cost sensors can cause severe drift and noise. This paper uses a simple, low-cost Inertial measurement unit (IMU) and effectively evaluates the precise position of pedestrians. This paper designs a pedestrian position estimation based on low-cost inertial sensors. To solve the problem of inertial sensing noise and accumulated errors during the movement, based on the EKF algorithm, a multi-condition threshold detection method is proposed, and then a zero-speed update, an angular speed update, and a yaw angle update are designed. In the measurement state, the proposed measurement error vector effectively eliminates the cumulative error during walking. Finally, using this algorithm to perform straight and rectangular walking routes, corresponding experiments were performed on the effectiveness of the method used. The proposed method for estimating the inertial navigation of pedestrians has an error within 3.8%. This basic research is of great significance in rehabilitation training and somatosensory play.

Inertial navigation system (INS) pivotal error sources are related to the deterministic errors and random errors of the inertial measurement unit (IMU). Low-cost inertial sensors are distinguished by high noise and significant performance uncertainties. Consequently, errors related to low-cost INS states (position, velocity, and attitudes) are rising rapidly in stand-alone mode. If accurate performance can be achieved with low-cost IMU, the cost of real applications can be reduced and the development of new applications may be made feasible. Errors in gyroscopes (gyro) which are the main building block of IMU play a significant role, while the errors in the accelerometers can be calibrated and compensated with reasonable precision by comparison with the gravity of the earth. So precise modeling of gyro for navigation simulation for missiles and unmanned vehicles is essential. To achieve this goal, firstly an error estimation algorithm is implemented. This algorithm firstly is tested with a simulated signal with a known noise parameter injected in it. Moreover, the calibration of deterministic errors is performed. Furthermore, a study of the autocorrelation function of the sensor outputs is presented, and a method for calculating the frequency response to determine potential stability problems is provided by calculating the cutoff frequency. The typical analysis methods discussed throughout the paper are intended to assist designers to develop or choose gyro that is uniquely adapted to the design application requirements. The development technique is used to characterize a tactical grade Micro-Electro-Mechanical Systems (MEMSIC) IMU, and the test results prove its accuracy comparing to the manufacturing calibration sheet.

Abstract. In airborne laser scanning a high-frequency trajectory solution is typically determined from inertial sensors and employed to directly geo-reference the acquired laser points. When low-cost MEMS inertial sensors are used, such as in lightweight unmanned aerial vehicles, non-negligible errors in the estimated trajectory project to the final point-cloud, resulting in unsatisfactory accuracy on the ground. There are different multi-sensor fusion approaches to correct the point-cloud errors caused by an imperfect trajectory determination. Mismatches between different optical observations and/or in the overlapping regions of the point-cloud can allow the correction of the final point-cloud, either directly, by means of rigid transformations, or indirectly, via improving the scanner trajectory estimation. In this work we propose to fuse lidar and cameras in a single adjustment based on dynamic networks, considering 2D tie-points from the imagery and 3D tie-points from overlapping point-cloud sections. On a challenging corridor mapping scenario, we show that considering either 2D or 3D tie-points, along with inertial and GNSS observations, results in a remarkably accurate point-cloud, even when low-cost inertial sensors are employed and in presence of challenging surface textures, such as high vegetation. Furthermore, since the distribution of the 2D and 3D tie-points is complementary, considering them together further increases the robustness of the adjustment due to higher redundancy. By employing the proposed approach within this controlled example, we were able to improve the final point-cloud accuracy by more than three times with respect to conventional geo-referencing methodology and to reduce the magnitude of the errors.

Real-time and accurate vehicle tracking by Cameras and Surveillance can provide strong support for the acquisition and application of important traffic parameters, which is the basis of the traffic condition evaluation and the reasonable traffic command and dispatch. To deal with difficult problems of vehicle tracking research in a complex environments, such as occlusion, sudden illumination change, similar target interference and real-time tracking, measures are taken as follows. Firstly, the existing color local entropy particle filter tracking method is improved. The symmetry of information entropy is used to overcome the tracking failure caused by large-area occlusion. Secondly, the SIFT feature tracking method is improved to enhance real-time performance and robustness. Thirdly, two tracking methods were combined according to their characteristics, aiming at effectively improving the quasi-determination and real-time performance of vehicle tracking. Fourthly, Kalman filter was used to predict the motion state of vehicles. According to the SIFT characteristics and license plate information of vehicles, the exact position of the lost target vehicles is quickly located. It has been verified by experiments that our method has effectively improved the accuracy and real-time performance of vehicle tracking in complex situations.

To enhance the inertial sensors performance, accurate characterization as well as modeling of the corresponding stochastic errors are required. In addition, accurate position and orientation information can be achieved when integrating inertial sensors with other position fix systems, such as Global Navigation Satellite Systems (GNSS). This paper investigates the Generalized Method of Wavelet Moments (GMWM), as a Wavelet Variance (WV)-based approach, for the stochastic characterization and model identification of the stochastic errors related to low-cost MEMS-based inertial sensor errors. Raw inertial sensor measurements were collected using MTi-G-710 MEMS Inertial Measurement Unit (IMU) where the GMWM was utilized for estimating the coefficients of different random processes associated with inertial sensor residuals. Results demonstrated that highly complicated error model structures are identified for the tested IMU, which contradicts the common assumption of modeling such errors as a single 1st order Gauss-Markov (GM) random process. In addition, the results showed that the GMWM is an efficient framework that could be used for estimating the parameters associated with complex structured random processes with the advantage of correlated residual characterization and identification.

For their complete realization, autonomous vehicles (AVs) fundamentally rely on the Global Navigation Satellite System (GNSS) to provide positioning and navigation information. However, in area such as urban cores, parking lots, and under dense foliage, which are all commonly frequented by AVs, GNSS signals suffer from blockage, interference, and multipath. These effects cause high levels of errors and long durations of service discontinuity that mar the performance of current systems. The prevalence of vision and low-cost inertial sensors provides an attractive opportunity to further increase the positioning and navigation accuracy in such GNSS-challenged environments. This paper presents enhancements to existing multisensor integration systems utilizing the inertial navigation system (INS) to aid in Visual Odometry (VO) outlier feature rejection. A scheme called Aided Visual Odometry (AVO) is developed and integrated with a high performance mechanization architecture utilizing vehicle motion and orientation sensors. The resulting solution exhibits improved state covariance convergence and navigation accuracy, while reducing computational complexity. Experimental verification of the proposed solution is illustrated through three real road trajectories, over two different land vehicles, and using two low-cost inertial measurement units (IMUs).

Cooperative vehicle safety systems (CVSSs) rely on vehicular networking for broadcasting state information in order to track neighbors’ positions and, therefore, to predict potential collisions. In vehicular networking, a large number of vehicles compete for access to the limited channel resource, causing channel congestion. The vehicle tracking accuracy, which is the basis for CVSSs, therefore, can be heavily affected. Moreover, the available channel resources must be shared among vehicles in a fair way in order to maintain accurate tracking accuracy for each vehicle. To realize fair access to channel resources while maintaining an accurate tracking performance under conditions of dynamic vehicle density, in this paper, we present a distributed fair transmission rate control strategy, based on multi-agent model predictive control (MPC). We first propose a dynamic information dissemination rate model to capture the state information dissemination ability under conditions of dynamic vehicle density. Then, we present a multi-agent information dissemination model, in which each vehicle is controlled by a control agent that uses MPC and coordinates with its neighboring agents in order to determine its optimal transmission rate actions. We then design an augmented-Lagrangian-based distributed decision-making scheme to find the optimal transmission rate actions and, at the same time, reach an agreement on fair and efficient channel utilization among the vehicles. Simulation results confirm that the distributed transmission rate control strategy can guarantee fair access to channel resources while achieving the optimal vehicle tracking performance under conditions of dynamic vehicle density.

We present a novel framework to monitor the three- dimensional trajectory (orientation and position) of a golf swing using miniaturized inertial sensors. Firstly we employed a highly accurate and computationally efficient revised gradient descent algorithm to obtain the orientation of a golf club. Secondly, we designed a series of digital filters to determine the backward and forward segments of the swing, enabling us to calculate drift-free linear velocity along with the relative 3D position of the golf club during the entire swing. Finally, the calculated motion trajectory was verified against a ground truth VICON system using Iterative Closest Point (ICP) in conjunction with Principal Component Analysis (PCA). The computationally efficient framework present here achieves a high level of accuracy (r = 0.9885, p < 0.0001) for such a low-cost system. This framework can be utilized for reliable movement technique evaluation and can provide near real-time feedback for athletes in various unconstrained environments. It is envisaged that the proposed framework is applicable to other racket based sports (e.g. tennis, cricket and hurling).

Inertial navigation systems provides the platform’s position, velocity, and attitude during its operation. As a dead-reckoning system, it requires initial conditions to calculate the navigation solution. While initial position and velocity vectors are provided by external means, the initial attitude can be determined using the system’s inertial sensors in a process known as coarse alignment. When considering low-cost inertial sensors, only the initial roll and pitch angles can be determined using the accelerometers measurements. The accuracy, as well as time required for the for the coarse alignment process are critical for the navigation solution accuracy, particularly for pure-inertial scenarios, because of the navigation solution drift. In this paper, a machine learning framework for the stationary coarse alignment stage is proposed. To that end, classical machine learning approaches are used in a two-stage approach to regress the roll and pitch angles. Alignment results obtained both in simulations and field experiments, using a smartphone, shows the benefits of using the proposed approach instead of the commonly used analytical coarse alignment procedure.

• Design and development of a flexible 3D scanner, completely disjoined by trackers and markers. • 3D acquisition by the use of optical technologies together with an inertial MARG sensors. • The device measurement accuracy and flexibility guarantee the possibility of its usage for reverse engineering applications. The 3D scanner domain normally relies on the presence of many tools and technologies. They are mainly divided between contact and non-contact ones, but at present a new trend is coming up, starting from the articulated arms family, where the traditional mechanical joints, allowing flexible measures on complex objects, have been replaced by handy scanner with laser trackers, or with markers. These new systems have the advantage to get the entire object shape without changing the object position itself, but moving the scanning device without the involvement of long software alignments and post treatment operations. Anyway, the use of laser trackers obliges the operator to maintain the scanner head always visible, while the marker usage is quite critical because the measure precision depends strongly on the marker position. Starting from these considerations, the research project presented in this paper has been focused on the design and development of a flexible 3D scanner, without trackers and markers, that merge low-cost optical technologies together with low-cost inertial sensors. Thanks to this synergy, the developed 3D scanner is able to measure the three-dimensional shape of the object recording its movement and transferring it to a control unit for obtaining a point cloud. The paper presents the main concepts of the 3D scanner design and is concluded by an experimental phase made with the use of a benchmark.

Regardless of whether the global navigation satellite system (GNSS)/inertial navigation system (INS) is integrated or the INS operates independently during GNSS outages, the stochastic error of the inertial sensor has an important impact on the navigation performance. The structure of stochastic error in low-cost inertial sensors is quite complex; therefore, it is difficult to identify and separate errors in the spectral domain using classical stochastic error methods such as the Allan variance (AV) method and power spectral density (PSD) analysis. However, a recently proposed estimation, based on generalized wavelet moment estimation (GMWM), is applied to the stochastic error modeling of inertial sensors, giving significant advantages. Focusing on the online implementation of GMWM and its integration within a general navigation filter, this paper proposes an algorithm for online stochastic error calibration of inertial sensors in urban cities. We further develop the autonomous stochastic error model by constructing a complete stochastic error model and determining model ranking criterion. Then, a detecting module is designed to work together with the autonomous stochastic error model as feedback for the INS/GNSS integration. Finally, two experiments are conducted to compare the positioning performance of this algorithm with other classical methods. The results validate the capability of this algorithm to improve navigation accuracy and achieve the online realization of complex stochastic models.

AlliedSignal Defense & Space Systems is ‘developing a miniaturized low-cost Inertial Measurement Unit (IMU) with tactical performance for missiles and munitions and for commercial applications. Many technology solutions are available that satisfy performance. This IMU design architecture ias established’ addressing cost as the primary performance parameter and then selecting the best technical performance solution to meet the cost goals. The, salient feature of the IMU is the use of two new inertial sensors designed primarily to significantly reduce cost through the use of micro-machined optoelectronic systems (MOES) for the gyro and’ micromachined electromechanical systems (MEMS) accelerometer technology. The SiMMA, Silicon Micro-Machined Accelerometer, provides the first product launch of tactical and inertial grade precision accelerometers. The SiFORS, Silicon Fiber-Optic Rotation Sensor, is an all silicon integrated optics gyro with fiber-optic sensing coil. ~The focus of theis, paper will be on the SiFORS gyro. In this paper we will be describe the design architecture and error budgets and present validating performance.’ 1. SiFORSTG IMU Introduction ’ AlliedSignal has developed a highly integrated very low cost Inertial Measurement Unit (IMU) based on Silicon integrated optics Fiber Optics Rotation Sensor’ (SiFORS) using MOES, Micro-machined OpticalElectronic Systems, and MEMS accelerometer technologies. The SiFORS IMU is environmentally robust and is currently, used in gun-hardened applications. The SiFORS lMU provides 600 Hz ,body rate and acceleration autopilot information and generates accurate. 100 Hi ‘measurements of angle (A@) and velocity (AV) for navigation applications. Its programmable data format and scaling capabilities allow a wide range of inertial navigation applications with minimum ‘design effort. Extensive performance 1 American Institute of Aeronautics and Astronautics 3 evaluations have been performed and the results are in close agreement with the predicted performance envelope. 1.1. Accelerometers in SiFORS IMU L Many solutions are available that satisfy the role of the accelerometer for all IMU applications and the. modular architecture of the’SiFORS lMU provides the flexibility for accelerometer selection. AlliedSignal’s RRA500 is a good accelerometer choice for the l-5 mg bias stability ‘range and the AlliedSignal SiMMATM, Silicon Micro-Machined Accelerometer, a. developing MEMS device, will soon be available in this performance range to further lower the IMU price. The gun-hardened system uses the Fndevco 7591 that provides, 10 mg performance after 12,000g launch shock. 1.2. Focus on SiFORS Rotation Sensor In this paper, we focus on the development and performance of the SiFORS, Silicon Fiber Optic Rotation Sensor.’ This sensor has a unique design employing the best features of both fiber optics and MOES processing. A key component of the SiFORS rotation sensor is the silicon integrated optics MultiFunction Chip (MFC), which provides the optical ( bench and low noise electronic detection capabilities for this Fiber Optic Gyroscope (FOG). An important feature of FOG technology is its performance scalability. The well understood FOG theory has facilitated performance predictions during ,.’ our SiFORS design and development phase. It also provides a low risk approach to developing performance’grade levels for various applications with the benefit being the lowest cost IMU for each performance grade. Since the entire MFC is fabricated using silicon technology, it can be produced with very high yield and is therefore ‘ideally suited for high volume, .low ‘. Copyright

Visual and inertial sensors, in combination, are able to provide accurate motion estimates and are well suited for use in many robot navigation tasks. However, correct data fusion, and hence overall performance, depends on careful calibration of the rigid body transform between the sensors. Obtaining this calibration information is typically difficult and time-consuming, and normally requires additional equipment. In this paper we describe an algorithm, based on the unscented Kalman filter, for self-calibration of the transform between a camera and an inertial measurement unit (IMU). Our formulation rests on a differential geometric analysis of the observability of the camera—IMU system; this analysis shows that the sensor-to-sensor transform, the IMU gyroscope and accelerometer biases, the local gravity vector, and the metric scene structure can be recovered from camera and IMU measurements alone. While calibrating the transform we simultaneously localize the IMU and build a map of the surroundings, all without additional hardware or prior knowledge about the environment in which a robot is operating. We present results from simulation studies and from experiments with a monocular camera and a low-cost IMU, which demonstrate accurate estimation of both the calibration parameters and the local scene structure.

The capabilities of Ashtech XII for differential GPS (Global Positioning System) are discussed. Differential algorithms and communication protocols to exchange differential information between receivers are included in the internal software of the receiver. Hence, there is no need for external computers to provide the protocol link. The all-in-view capability of the receivers makes them suitable for reference stations as well as remotes. Integration of ASHTECH XII with the vehicle tracking system (VTS) is presented. Differential GPS messages are transferred via the VTS communication link. The integration of the VTS and differential GPS has resulted in an accurate vehicle tracking and positioning system called differential GPS/VTS.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

: A guidance and control (G\&C) design tool to rapidly assess the necessary control effort of a conceptual space access vehicle to track its flight trajectory is described. This tool can be used as part of the preliminary design cycle in configuration, trajectory planning, structural analysis, aerodynamic modeling, and control surface sizing. Given a conceptual configuration for a space access vehicle and a desired trajectory for a reentry flight, this G\&C tool provides an inner-loop feedback control law and outer loop feedback guidance law to track the given trajectory. Assessment of the vehicle's tracking performance and associated aero-control usage can be made. This assessment can then be used to determine the appropriate control.