Showing papers on "Inertial navigation system published in 2016"

Dual matter-wave inertial sensors in weightlessness

Abstract: Quantum technology based on cold-atom interferometers is showing great promise for fields such as inertial sensing and fundamental physics. However, the finite free-fall time of the atoms limits the precision achievable on Earth, while in space interrogation times of many seconds will lead to unprecedented sensitivity. Here we realize simultaneous 87 Rb– 39 K interferometers capable of operating in the weightless environment produced during parabolic flight. Large vibration levels (10 A 2 g Hz A 1/2), variations in acceleration (0–1.8 g) and rotation rates (5° s A 1) onboard the aircraft present significant challenges. We demonstrate the capability of our correlated quantum system by measuring the Eotvos parameter with systematic-limited uncertainties of 1.1 Â 10 A 3 and 3.0 Â 10 A 4 during standard-and microgravity, respectively. This constitutes a fundamental test of the equivalence principle using quantum sensors in a free-falling vehicle. Our results are applicable to inertial navigation, and can be extended to the trajectory of a satellite for future space missions.

Review of visual odometry: types, approaches, challenges, and applications

Abstract: Accurate localization of a vehicle is a fundamental challenge and one of the most important tasks of mobile robots For autonomous navigation, motion tracking, and obstacle detection and avoidance, a robot must maintain knowledge of its position over time Vision-based odometry is a robust technique utilized for this purpose It allows a vehicle to localize itself robustly by using only a stream of images captured by a camera attached to the vehicle This paper presents a review of state-of-the-art visual odometry (VO) and its types, approaches, applications, and challenges VO is compared with the most common localization sensors and techniques, such as inertial navigation systems, global positioning systems, and laser sensors Several areas for future research are also highlighted

Evaluation of Two WiFi Positioning Systems Based on Autonomous Crowdsourcing of Handheld Devices for Indoor Navigation

Abstract: Current WiFi positioning systems (WPSs) require databases – such as locations of WiFi access points and propagation parameters, or a radio map – to assist with positioning. Typically, procedures for building such databases are time-consuming and labour-intensive. In this paper, two autonomous crowdsourcing systems are proposed to build the databases on handheld devices by using our designed algorithms and an inertial navigation solution from a Trusted Portable Navigator (T-PN). The proposed systems, running on smartphones, build and update the database autonomously and adaptively to account for the dynamic environment. To evaluate the performance of automatically generated databases, two improved WiFi positioning schemes (fingerprinting and trilateration) corresponding to these two database building systems, are also discussed. The main contribution of the paper is the proposal of two crowdsourcing-based WPSs that eliminate the various limitations of current crowdsourcing-based systems which (a) require a floor plan or GPS, (b) are suitable only for specific indoor environments, and (c) implement a simple MEMS-based sensors’ solution. In addition, these two WPSs are evaluated and compared through field tests. Results in different test scenarios show that average positioning errors of both proposed systems are all less than 5.75 m.

Autonomous Underwater Vehicle Navigation

Abstract: This chapter surveys the problem of navigation for autonomous underwater vehicles (AUV s). Navigation is critical for the safety and effectiveness of AUV missions. The unavailability of global positioning system (GPS ) underwater makes AUV navigation a challenging research problem. Recent years have seen considerable improvements in performance and reduction in the cost and size of the various sensor devices available for ocean vehicle navigation. In concert with these developments, advances in algorithms such as simultaneous localization and mapping, and cooperative navigation have enabled dramatic improvements in the navigation capabilities of AUVs. These improvements in AUV navigation have contributed to the successful deployment of AUVs for a wide variety of applications over the past decade.

Tightly-Coupled Integration of WiFi and MEMS Sensors on Handheld Devices for Indoor Pedestrian Navigation

Abstract: The need for indoor pedestrian navigators is quickly increasing in various applications over the last few years. However, indoor navigation still faces many challenges and practical issues, such as the need for special hardware designs and complicated infrastructure requirements. This paper originally proposes a pedestrian navigator based on tightly coupled (TC) integration of low-cost microelectromechanical systems (MEMS) sensors and WiFi for handheld devices. Two other approaches are proposed in this paper to enhance the navigation performance: 1) the use of MEMS solution based on pedestrian dead reckoning/inertial navigation system (PDR/INS) integration and 2) the use of motion constraints, such as non-holonomic constraints, zero velocity update, and zero angular rate update for the MEMS solution. There are two main contributions in this paper: 1) TC fusion of WiFi, INS, and PDR for pedestrian navigation using an extended Kalman filter and 2) better heading estimation using PDR and INS integration to remove the gyro noise that occurs when only vertical gyroscope is used. The performance of the proposed navigation algorithms has been extensively verified through field tests in indoor environments. The experiment results showed that the average root mean square position error of the proposed TC integration solution was 3.47 m in three trajectories, which is 0.01% of INS, 10.38% of PDR, 32.11% of the developed MEMS solution, and 64.58% of the loosely coupled integration. The proposed TC integrated navigation system can work well in the environment with sparse deployment of WiFi access points.

Motion-Based Calibration of Multimodal Sensor Extrinsics and Timing Offset Estimation

Abstract: This paper presents a system for calibrating the extrinsic parameters and timing offsets of an array of cameras, 3-D lidars, and global positioning system/inertial navigation system sensors, without the requirement of any markers or other calibration aids. The aim of the approach is to achieve calibration accuracies comparable with state-of-the-art methods, while requiring less initial information about the system being calibrated and thus being more suitable for use by end users. The method operates by utilizing the motion of the system being calibrated. By estimating the motion each individual sensor observes, an estimate of the extrinsic calibration of the sensors is obtained. Our approach extends standard techniques for motion-based calibration by incorporating estimates of the accuracy of each sensor's readings. This yields a probabilistic approach that calibrates all sensors simultaneously and facilitates the estimation of the uncertainty in the final calibration. In addition, we combine this motion-based approach with appearance information. This gives an approach that requires no initial calibration estimate and takes advantage of all available alignment information to provide an accurate and robust calibration for the system. The new framework is validated with datasets collected with different platforms and different sensors’ configurations, and compared with state-of-the-art approaches.

Step Detection for ZUPT-Aided Inertial Pedestrian Navigation System Using Foot-Mounted Permanent Magnet

Abstract: A foot-mounted pedestrian dead reckoning system is a self-contained technique for indoor localization. An inertial pedestrian navigation system includes wearable MEMS inertial sensors, such as an accelerometer, gyroscope, or digital compass, which enable the measurement of the step length and the heading direction. Therefore, the use of zero velocity updates is necessary to minimize the inertial drift accumulation of the sensors. The aim of this paper is to develop a foot-mounted pedestrian dead reckoning system based on an inertial measurement unit and a permanent magnet. Our approach enables the stance phase and the step duration detection based on the measurements of the permanent magnet field during each gait cycle. The proposed system involves several parts: inertial state estimation, stance phase detection, altitude measurement, and error state Kalman Filter with zero velocity update and altitude measurement update. Real indoor experiments demonstrate that the proposed algorithm is capable of estimating the trajectory accurately with low estimation error.

An Integrated GNSS/INS/LiDAR-SLAM Positioning Method for Highly Accurate Forest Stem Mapping

Abstract: Forest mapping, one of the main components of performing a forest inventory, is an important driving force in the development of laser scanning. Mobile laser scanning (MLS), in which laser scanners are installed on moving platforms, has been studied as a convenient measurement method for forest mapping in the past several years. Positioning and attitude accuracies are important for forest mapping using MLS systems. Inertial Navigation Systems (INSs) and Global Navigation Satellite Systems (GNSSs) are typical and popular positioning and attitude sensors used in MLS systems. In forest environments, because of the loss of signal due to occlusion and severe multipath effects, the positioning accuracy of GNSS is severely degraded, and even that of GNSS/INS decreases considerably. Light Detection and Ranging (LiDAR)-based Simultaneous Localization and Mapping (SLAM) can achieve higher positioning accuracy in environments containing many features and is commonly implemented in GNSS-denied indoor environments. Forests are different from an indoor environment in that the GNSS signal is available to some extent in a forest. Although the positioning accuracy of GNSS/INS is reduced, estimates of heading angle and velocity can maintain high accurate even with fewer satellites. GNSS/INS and the LiDAR-based SLAM technique can be effectively integrated to form a sustainable, highly accurate positioning and mapping solution for use in forests without additional hardware costs. In this study, information such as heading angles and velocities extracted from a GNSS/INS is utilized to improve the positioning accuracy of the SLAM solution, and two information-aided SLAM methods are proposed. First, a heading angle-aided SLAM (H-aided SLAM) method is proposed that supplies the heading angle from GNSS/INS to SLAM. Field test results show that the horizontal positioning accuracy of an entire trajectory of 800 m is 0.13 m and is significantly improved (by 70%) compared to that of a traditional GNSS/INS; second, a more complex information added SLAM solution that utilizes both heading angle and velocity information simultaneously (HV-aided SLAM) is investigated. Experimental results show that the horizontal positioning accuracy can reach a level of six centimetres with the HV-aided SLAM, which is a significant improvement (by 86%). Thus, a more accurate forest map is obtained by the proposed integrated method.

Vision-based Obstacle Detection and Navigation for an Agricultural Robot

Abstract: This paper describes a vision-based obstacle detection and navigation system for use as part of a robotic solution for the sustainable intensification of broad-acre agriculture. To be cost-effective, the robotics solution must be competitive with current human-driven farm machinery. Significant costs are in high-end localization and obstacle detection sensors. Our system demonstrates a combination of an inexpensive global positioning system and inertial navigation system with vision for localization and a single stereo vision system for obstacle detection. The paper describes the design of the robot, including detailed descriptions of three key parts of the system: novelty-based obstacle detection, visually-aided guidance, and a navigation system that generates collision-free kinematically feasible paths. The robot has seen extensive testing over numerous weeks of field trials during the day and night. The results in this paper pertain to one particular 3 h nighttime experiment in which the robot performed a coverage task and avoided obstacles. Additional results during the day demonstrate that the robot is able to continue operating during 5 min GPS outages by visually following crop rows.

Inertial sensor arrays — A literature review

Abstract: Inertial sensor arrays present the possibility of improved and extended sensing capabilities as compared to customary inertial sensor setups. Inertial sensor arrays have been studied since the 1960s and have recently received a renewed interest, mainly thanks to the ubiquitous micro-electromechanical (MEMS) inertial sensors. However, the number of variants and features of inertial sensor arrays and their disparate applications makes the literature spread out. Therefore, in this paper we provide a brief summary and literature review on the topic of inertial sensor arrays. Publications are categorized and presented in a structured way; references to +300 publications are provide. Finally, an outlook on the main research challenges and opportunities related to inertial sensor arrays is given.

A Particle Filter-Based Matching Algorithm With Gravity Sample Vector for Underwater Gravity Aided Navigation

Abstract: Gravity matching algorithm is a key technique of gravity aided navigation for underwater vehicles. The reliability of traditional single point matching algorithm can be easily affected by environmental disturbance, which results in mismatching and decrease of navigation accuracy. Therefore, a particle filter (PF)-based matching algorithm with gravity sample vector is proposed. The correlation between adjacent sample points of inertial navigation system is considered in the vector matching algorithm in order to solve the mismatching problem. The current sampling point matching result is rectified by the vectors composed by the selected sampling points and matching point. The amount of selected sampling points is determined by the gravity field distribution and the real-time performance of the algorithm. A PF-based on Bayesian estimation is introduced in the proposed method to overcome the divergence disadvantage of the traditional point matching algorithm in some matching areas with obvious gravity variation. Simulation results prove that compared with the traditional methods, the proposed method is robust to the changes of gravity anomaly in the matching areas, with more accurate and reliable matching results.

A Tightly-Coupled GPS/INS/UWB Cooperative Positioning Sensors System Supported by V2I Communication

Abstract: This paper investigates a tightly-coupled Global Position System (GPS)/Ultra-Wideband (UWB)/Inertial Navigation System (INS) cooperative positioning scheme using a Robust Kalman Filter (RKF) supported by V2I communication. The scheme proposes a method that uses range measurements of UWB units transmitted among the terminals as augmentation inputs of the observations. The UWB range inputs are used to reform the GPS observation equations that consist of pseudo-range and Doppler measurements and the updated observation equation is processed in a tightly-coupled GPS/UWB/INS integrated positioning equation using an adaptive Robust Kalman Filter. The result of the trial conducted on the roof of the Nottingham Geospatial Institute (NGI) at the University of Nottingham shows that the integrated solution provides better accuracy and improves the availability of the system in GPS denied environments. RKF can eliminate the effects of gross errors. Additionally, the internal and external reliabilities of the system are enhanced when the UWB observables received from the moving terminals are involved in the positioning algorithm.

Localizing Ground Penetrating RADAR: A Step Toward Robust Autonomous Ground Vehicle Localization

Abstract: Autonomous ground vehicles navigating on road networks require robust and accurate localization over long-term operation and in a wide range of adverse weather and environmental conditions. GPS/INS inertial navigation system solutions, which are insufficient alone to maintain a vehicle within a lane, can fail because of significant radio frequency noise or jamming, tall buildings, trees, and other blockage or multipath scenarios. LIDAR and camera map-based vehicle localization can fail when optical features become obscured, such as with snow or dust, or with changes to gravel or dirt road surfaces. Localizing ground penetrating radar LGPR is a new mode of a priori map-based vehicle localization designed to complement existing approaches with a low sensitivity to failure modes of LIDAR, camera, and GPS/INS sensors due to its low-frequency RF energy, which couples deep into the ground. Most subsurface features detected are inherently stable over time. Significant research, discussed herein, remains to prove general utility. We have developed a novel low-profile ultra-low power LGPR system and demonstrated real-time operation underneath a passenger vehicle. A correlation maximizing optimization technique was developed to allow real-time localization at 126i¾?Hz. Here we present the detailed design and results from highway testing, which uses a simple heuristic for fusing LGPR estimates with a GPS/INS system. Cross-track localization accuracies of 4.3i¾?cm RMS relative to a "truth" RTK GPS/INS unit at speeds up to 100i¾?km/h 60i¾?mph are demonstrated. These results, if generalizable, introduce a widely scalable real-time localization method with cross-track accuracy as good as or better than current localization methods.

Long-range GPS-denied aerial inertial navigation with LIDAR localization

Abstract: Despite significant progress in GPS-denied autonomous flight, long-distance traversals (> 100 km) in the absence of GPS remain elusive. This paper demonstrates a method capable of accurately estimating the aircraft state over a 218 km flight with a final position error of 27 m, 0.012% of the distance traveled. Our technique efficiently captures the full state dynamics of the air vehicle with semi-intermittent global corrections using LIDAR measurements matched against an a priori Digital Elevation Model (DEM). Using an error-state Kalman filter with IMU bias estimation, we are able to maintain a high-certainty state estimate, reducing the computation time to search over a global elevation map. A sub region of the DEM is scanned with the latest LIDAR projection providing a correlation map of landscape symmetry. The optimal position is extracted from the correlation map to produce a position correction that is applied to the state estimate in the filter. This method provides a GPS-denied state estimate for long range drift-free navigation. We demonstrate this method on two flight data sets from a full-sized helicopter, showing significantly longer flight distances over the current state of the art.

Differential GNSS and Vision-Based Tracking to Improve Navigation Performance in Cooperative Multi-UAV Systems.

Abstract: Autonomous navigation of micro-UAVs is typically based on the integration of low cost Global Navigation Satellite System (GNSS) receivers and Micro-Electro-Mechanical Systems (MEMS)-based inertial and magnetic sensors to stabilize and control the flight. The resulting navigation performance in terms of position and attitude accuracy may not suffice for other mission needs, such as the ones relevant to fine sensor pointing. In this framework, this paper presents a cooperative UAV navigation algorithm that allows a chief vehicle, equipped with inertial and magnetic sensors, a Global Positioning System (GPS) receiver, and a vision system, to improve its navigation performance (in real time or in the post processing phase) exploiting formation flying deputy vehicles equipped with GPS receivers. The focus is set on outdoor environments and the key concept is to exploit differential GPS among vehicles and vision-based tracking (DGPS/Vision) to build a virtual additional navigation sensor whose information is then integrated in a sensor fusion algorithm based on an Extended Kalman Filter. The developed concept and processing architecture are described, with a focus on DGPS/Vision attitude determination algorithm. Performance assessment is carried out on the basis of both numerical simulations and flight tests. In the latter ones, navigation estimates derived from the DGPS/Vision approach are compared with those provided by the onboard autopilot system of a customized quadrotor. The analysis shows the potential of the developed approach, mainly deriving from the possibility to exploit magnetic- and inertial-independent accurate attitude information.

Optimization-based alignment for strapdown inertial navigation system: comparison and extension

Abstract: In this paper, the optimization-based alignment (OBA) methods are investigated with the main focus on the vector observation construction procedures for the strapdown inertial navigation system (SINS). The contributions of this study are twofold. First, the OBA method is extended to be able to estimate the gyroscope biases coupled with the attitude based on the construction process of the existing OBA methods. This extension transforms the initial alignment into an attitude estimation problem that can be solved by using the nonlinear filtering algorithms. The second contribution is the comprehensive evaluation of the OBA methods and their extensions with different vector observations construction procedures in terms of convergence speed and steady-state estimate by using field test data collected from different grades of SINS. This study is expected to facilitate the selection of appropriate OBA methods for different grades of SINS.

Automated Ortho-Rectification of UAV-Based Hyperspectral Data over an Agricultural Field Using Frame RGB Imagery

Abstract: Low-cost Unmanned Airborne Vehicles (UAVs) equipped with consumer-grade imaging systems have emerged as a potential remote sensing platform that could satisfy the needs of a wide range of civilian applications. Among these applications, UAV-based agricultural mapping and monitoring have attracted significant attention from both the research and professional communities. The interest in UAV-based remote sensing for agricultural management is motivated by the need to maximize crop yield. Remote sensing-based crop yield prediction and estimation are primarily based on imaging systems with different spectral coverage and resolution (e.g., RGB and hyperspectral imaging systems). Due to the data volume, RGB imaging is based on frame cameras, while hyperspectral sensors are primarily push-broom scanners. To cope with the limited endurance and payload constraints of low-cost UAVs, the agricultural research and professional communities have to rely on consumer-grade and light-weight sensors. However, the geometric fidelity of derived information from push-broom hyperspectral scanners is quite sensitive to the available position and orientation established through a direct geo-referencing unit onboard the imaging platform (i.e., an integrated Global Navigation Satellite System (GNSS) and Inertial Navigation System (INS). This paper presents an automated framework for the integration of frame RGB images, push-broom hyperspectral scanner data and consumer-grade GNSS/INS navigation data for accurate geometric rectification of the hyperspectral scenes. The approach relies on utilizing the navigation data, together with a modified Speeded-Up Robust Feature (SURF) detector and descriptor, for automating the identification of conjugate features in the RGB and hyperspectral imagery. The SURF modification takes into consideration the available direct geo-referencing information to improve the reliability of the matching procedure in the presence of repetitive texture within a mechanized agricultural field. Identified features are then used to improve the geometric fidelity of the previously ortho-rectified hyperspectral data. Experimental results from two real datasets show that the geometric rectification of the hyperspectral data was improved by almost one order of magnitude.

A Self-Calibration Method for Non-Orthogonal Angles of Gimbals in Tri-Axis Rotational Inertial Navigation System

Abstract: Navigation accuracy of the inertial navigation system (INS) could be greatly improved by rotating the inertial measurement unit around gimbals. However, the attitude output accuracy of rotational INS (RINS) would be affected by the non-orthogonal angles of gimbals, which should be accurately calibrated and compensated. In this paper, a novel self-calibration method for non-orthogonal angles of gimbals is proposed in tri-axis RINS. The three non-orthogonal angles could be calibrated using attitude errors and velocity errors as measurements without external equipment. The self-calibration scheme in this method could reduce the coupling of non-orthogonal angles with other errors, and the observability is clear to demonstrate during calibration. The proposed method is verified by both simulations and experiments. The experimental results show that the calibration accuracy of non-orthogonal angles could be less than 2”, and after compensation, the attitude output accuracy is improved from 200” to less than 10”. Therefore, the proposed calibration method could greatly improve the attitude output accuracy and make RINS more effective in some task systems where high attitude output accuracy is urgently required.

Development of compact cold-atom sensors for inertial navigation

Abstract: Inertial sensors based on cold atom interferometry exhibit many interesting features for applications related to inertial navigation, particularly in terms of sensitivity and long-term stability. However, at present the typical atom interferometer is still very much an experiment—consisting of a bulky, static apparatus with a limited dynamic range and high sensitivity to environmental effects. To be compliant with mobile applications further development is needed. In this work, we present a compact and mobile experiment, which we recently used to achieve the first inertial measurements with an atomic accelerometer onboard an aircraft. By integrating classical inertial sensors into our apparatus, we are able to operate the atomic sensor well beyond its standard operating range, corresponding to half of an interference fringe. We report atom-based acceleration measurements along both the horizontal and vertical axes of the aircraft with one-shot sensitivities of 2.3 × 10−4 g over a range of ∼ 0.1 g. The same technology can be used to develop cold-atom gyroscopes, which could surpass the best optical gyroscopes in terms of long-term sensitivity. Our apparatus was also designed to study multi-axis atom interferometry with the goal of realizing a full inertial measurement unit comprised of the three axes of acceleration and rotation. Finally, we present a compact and tunable laser system, which constitutes an essential part of any cold-atom-based sensor. The architecture of the laser is based on phase modulating a single fiber-optic laser diode, and can be tuned over a range of 1 GHz in less than 200 μs.

Development of compact cold-atom sensors for inertial navigation

Abstract: Inertial sensors based on cold atom interferometry exhibit many interesting features for applications related to inertial navigation, particularly in terms of sensitivity and long-term stability. However, at present the typical atom interferometer is still very much an experiment---consisting of a bulky, static apparatus with a limited dynamic range and high sensitivity to environmental effects. To be compliant with mobile applications further development is needed. In this work, we present a compact and mobile experiment, which we recently used to achieve the first inertial measurements with an atomic accelerometer onboard an aircraft. By integrating classical inertial sensors into our apparatus, we are able to operate the atomic sensor well beyond its standard operating range, corresponding to half of an interference fringe. We report atom-based acceleration measurements along both the horizontal and vertical axes of the aircraft with one-shot sensitivities of $2.3 \times 10^{-4}\,g$ over a range of $\sim 0.1\,g$. The same technology can be used to develop cold-atom gyroscopes, which could surpass the best optical gyroscopes in terms of long-term sensitivity. Our apparatus was also designed to study multi-axis atom interferometry with the goal of realizing a full inertial measurement unit comprised of the three axes of acceleration and rotation. Finally, we present a compact and tunable laser system, which constitutes an essential part of any cold-atom-based sensor. The architecture of the laser is based on phase modulating a single fiber-optic laser diode, and can be tuned over a range of 1 GHz in less than 200 $\mu$s.

Likelihood-Field-Model-Based Dynamic Vehicle Detection and Tracking for Self-Driving

Abstract: Dynamic vehicle detection and tracking is crucial for self-driving in urban environments. The main problem of the previous beam-model-based algorithms is that they cannot detect and track dynamic vehicles that are occluded by other objects. In this paper, we develop a novel dynamic vehicle detection and tracking algorithm to solve this problem for our autonomous land vehicle (ALV), which is equipped with a Velodyne LIDAR and a GPS-aid inertial navigation system. For detection, our improved two-dimensional virtual scan is presented to detect the potential dynamic vehicles with a scan differencing operation. Then, for each potential dynamic vehicle, a novel likelihood-field-based vehicle measurement model is proposed to weight its possible poses. Finally, our newly modified scaling series algorithm and the importance sampling technique are adopted to estimate the initial pose and the corresponding velocity for each vehicle, respectively. The scaling series algorithm coupled with a Bayesian filter (SSBF) was previously used to handle the tactile localization problem in static background scenes. For tracking dynamic vehicles, we improve the SSBF by adding the ego-motion compensation so that the improved algorithm is able to update the pose and velocity for each vehicle in dynamic background scenes. Both the quantitative and qualitative experimental results validate the performance of our dynamic vehicle detection and tracking algorithm on the KITTI datasets and the Velodyne data collected by our ALV in dynamic urban environments.

An Improved TERCOM-Based Algorithm for Gravity-Aided Navigation

Abstract: Gravity-aided inertial navigation is a leading issue in the application of autonomous underwater vehicle. An improved gravity matching algorithm, based on the principle of the terrain contour matching (TERCOM) algorithm, is proposed. The matching algorithm applies the shortest path algorithm to increase update frequency. In addition, the positioning error can be limited due to the novel correlation analysis method. Compared with existing algorithms, the improved TERCOM algorithm has better real-time performance, positioning accuracy, and reduced calculation burden. The reliability and the accuracy of the algorithm are verified via simulation tests.

Absolute Positioning Using the Earth's Magnetic Anomaly Field†

Abstract: Achieving worldwide dependable alternatives to the Global Positioning System is a challenging engineering problem. Current Global Positioning System alternatives often suffer from limitations such as where and when the systems can operate. Navigation using Earth’s magnetic anomaly field, which is globally available at all times, shows promise to overcome many of these limitations. We present a navigation framework that uses Earth’s magnetic anomaly field as a navigation signal to aid an inertial navigation system in an aircraft. The filter utilizes ultra-accurate optically pumped cesium magnetometers to make scalar intensity measurements of Earth’s magnetic field and compare them with a map using a particle filter approach. The accuracy of these measurements allows observability of not only the inertial navigation system errors but also the temporal effects of Earth’s magnetic field, which corrupt the navigation signal. These temporal effects are thoroughly analyzed, and we present a simple model that allows near worldwide use of the navigation filter. We analyze the dependencies on altitude and magnetic storm activity in a realistic simulation using data from test flights and magnetic observatories.

Systems and methods to determine a location of a mobile device

Abstract: Systems and methods to position beacons at traffic choke points, use a mobile device to detect the peaks of beacon signals corresponding to the mobile device traveling through the traffic "choke points", and thus determine accurately the position and speed of the mobile device in the transport corridor between the choke points. The determined position and speed of the mobile device can be used to improve the performance of other location determination technologies, such as radio frequency fingerprint-based location estimate and/or inertial guidance location estimate.

Pedestrian Dead Reckoning with Particle Filter for handheld smartphone

Abstract: Commonly used Global Navigation Satellite Systems (GNSS) are inappropriate as Location Based Services (LBS) in indoor environment. Therefore research teams are developing different systems, which can be used as a suitable alternative. One of options is to use Inertial Navigation System (INS) which consists of inertial sensors and mathematic procedures. This concept has been known for a long time, but with arrival of Microelectro Mechanical System (MEMS) INS found wide use. Smartphones with inertial sensors, such as accelerometers and gyroscopes, allow us to use them as input devices for Pedestrian Dead Reckoning (PDR). In this paper we present PDR by using smartphone sensors. They can be classified as low-cost Inertial Measurement Unit (IMU), and have been compared with more precise and expensive Xsens IMU. Accuracy of inertial sensors has increased in the past few years, but they still cannot alone provide proper accuracy because of many negative effects, such as heading drift due to gyroscope bias. Particle Filter (PF) has been successfully used with map constraints to increase the accuracy of proposed location system. Presented results show that low-cost smartphone IMU combined with PF can be applicable as proper navigation system.

Optical Flow Sensor/INS/Magnetometer Integrated Navigation System for MAV in GPS-Denied Environment

Abstract: The drift of inertial navigation system (INS) will lead to large navigation error when a low-cost INS is used in microaerial vehicles (MAV). To overcome the above problem, an INS/optical flow/magnetometer integrated navigation scheme is proposed for GPS-denied environment in this paper. The scheme, which is based on extended Kalman filter, combines INS and optical flow information to estimate the velocity and position of MAV. The gyro, accelerator, and magnetometer information are fused together to estimate the MAV attitude when the MAV is at static state or uniformly moving state; and the gyro only is used to estimate the MAV attitude when the MAV is accelerating or decelerating. The MAV flight data is used to verify the proposed integrated navigation scheme, and the verification results show that the proposed scheme can effectively reduce the errors of navigation parameters and improve navigation precision.

Error analysis of analytical coarse alignment formulations for stationary SINS

Abstract: This paper presents a comprehensive error analysis of coarse alignment formulations for stationary strapdown inertial navigation systems (SINS). Analytical expressions for the residual normality, orthogonality, alignment, and Euler angle errors are systematically derived, allowing us to establish comparisons in terms of rapidity, accuracy, and autonomy requirements. For the purpose of the error analysis, geographic latitude and local gravity acceleration uncertainties are considered, in addition to the inertial sensor uncertainties. As the main contribution of this paper, an improved coarse alignment method is proposed, which is based on orthonormality constraints existing between the Euler angles and the attitude matrix. In contrast to the traditional methods, the proposed formulation does not imply normality and orthogonality errors, besides producing time-independent alignment and Euler angle errors. The latter is seen to be particularly interesting for situations wherein, due to time constraints, posterior fine alignment and orthonormalization procedures cannot be implemented. The superiority of the proposed formulation is validated through simulated and experimental tests, regardless of the SINS initial orientation. The accuracy degradation produced by each alignment formulation in the navigation stage is used as the performance index.