Showing papers on "Inertial navigation system published in 2013"

Vision meets robotics: The KITTI dataset

Abstract: We present a novel dataset captured from a VW station wagon for use in mobile robotics and autonomous driving research. In total, we recorded 6 hours of traffic scenarios at 10-100 Hz using a variety of sensor modalities such as high-resolution color and grayscale stereo cameras, a Velodyne 3D laser scanner and a high-precision GPS/IMU inertial navigation system. The scenarios are diverse, capturing real-world traffic situations, and range from freeways over rural areas to inner-city scenes with many static and dynamic objects. Our data is calibrated, synchronized and timestamped, and we provide the rectified and raw image sequences. Our dataset also contains object labels in the form of 3D tracklets, and we provide online benchmarks for stereo, optical flow, object detection and other tasks. This paper describes our recording platform, the data format and the utilities that we provide.

A Survey of Indoor Inertial Positioning Systems for Pedestrians

Abstract: With the continual miniaturisation of sensors and processing nodes, Pedestrian Dead Reckoning (PDR) systems are becoming feasible options for indoor tracking. These use inertial and other sensors, often combined with domain-specific knowledge about walking, to track user movements. There is currently a wealth of relevant literature spread across different research communities. In this survey, a taxonomy of modern PDRs is developed and used to contextualise the contributions from different areas. Techniques for step detection, characterisation, inertial navigation and step-and-heading-based dead-reckoning are reviewed and compared. Techniques that incorporate building maps through particle filters are analysed, along with hybrid systems that use absolute position fixes to correct dead-reckoning output. In addition, consideration is given to the possibility of using smartphones as PDR sensing devices. The survey concludes that PDR techniques alone can offer good short- to medium- term tracking under certain circumstances, but that regular absolute position fixes from partner systems will be needed to ensure long-term operation and to cope with unexpected behaviours. It concludes by identifying a detailed list of challenges for PDR researchers.

Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems, Second Edition

Abstract: This newly revised and greatly expanded edition of the popular Artech House book Principles of GNSS, Inertial, and Multisensor Integrated Navigation Systems offers you a current and comprehensive understanding of satellite navigation, inertial navigation, terrestrial radio navigation, dead reckoning, and environmental feature matching . It provides both an introduction to navigation systems and an in-depth treatment of INS/GNSS and multisensor integration. The second edition offers a wealth of added and updated material, including a brand new chapter on the principles of radio positioning and a chapter devoted to important applications in the field. Other updates include expanded treatments of map matching, image-based navigation, attitude determination, acoustic positioning, pedestrian navigation, advanced GNSS techniques, and several terrestrial and short-range radio positioning technologies. The book shows you how satellite, inertial, and other navigation technologies work, and focuses on processing chains and error sources. In addition, you get a clear introduction to coordinate frames, multi-frame kinematics, Earth models, gravity, Kalman filtering, and nonlinear filtering. Providing solutions to common integration problems, the book describes and compares different integration architectures, and explains how to model different error sources. You get a broad and penetrating overview of current technology and are brought up to speed with the latest developments in the field, including context-dependent and cooperative positioning. DVD Included: Features eleven appendices, interactive worked examples, basic GNSS and INS MATLAB simulation software, and problems and exercises to help you master the material.

Velocity/Position Integration Formula Part I: Application to In-Flight Coarse Alignment

Abstract: The in-flight alignment is a critical stage for airborne inertial navigation system/Global Positioning System (INS/GPS) applications. The alignment task is usually carried out by the Kalman filtering technique that necessitates a good initial attitude to obtain a satisfying performance. Due to the airborne dynamics, the in-flight alignment is much more difficult than the alignment on the ground. An optimization-based coarse alignment approach that uses GPS position/velocity as input, founded on the newly-derived velocity/position integration formulae is proposed. Simulation and flight test results show that, with the GPS lever arm well handled, it is potentially able to yield the initial heading up to 1 deg accuracy in 10 s. It can serve as a nice coarse in-flight alignment without any prior attitude information for the subsequent fine Kalman alignment. The approach can also be applied to other applications that require aligning the INS on the run.

A UAV system for inspection of industrial facilities

Abstract: This work presents a small-scale Unmanned Aerial System (UAS) capable of performing inspection tasks in enclosed industrial environments. Vehicles with such capabilities have the potential to reduce human involvement in hazardous tasks and can minimize facility outage periods. The results presented generalize to UAS exploration tasks in almost any GPS-denied indoor environment. The contribution of this work is twofold. First, results from autonomous flights inside an industrial boiler of a power plant are presented. A lightweight, vision-aided inertial navigation system provides reliable state estimates under difficult environmental conditions typical for such sites. It relies solely on measurements from an on-board MEMS inertial measurement unit and a pair of cameras arranged in a classical stereo configuration. A model-predictive controller allows for efficient trajectory following and enables flight in close proximity to the boiler surface. As a second contribution, we highlight ongoing developments by displaying state estimation and structure recovery results acquired with an integrated visual/inertial sensor that will be employed on future aerial service robotic platforms. A tight integration in hardware facilitates spatial and temporal calibration of the different sensors and thus enables more accurate and robust ego-motion estimates. Comparison with ground truth obtained from a laser tracker shows that such a sensor can provide motion estimates with drift rates of only few centimeters over the period of a typical flight.

Real-time motion tracking on a cellphone using inertial sensing and a rolling-shutter camera

Abstract: All existing methods for vision-aided inertial navigation assume a camera with a global shutter, in which all the pixels in an image are captured simultaneously. However, the vast majority of consumer-grade cameras use rolling-shutter sensors, which capture each row of pixels at a slightly different time instant. The effects of the rolling shutter distortion when a camera is in motion can be very significant, and are not modelled by existing visual-inertial motion-tracking methods. In this paper we describe the first, to the best of our knowledge, method for vision-aided inertial navigation using rolling-shutter cameras. Specifically, we present an extended Kalman filter (EKF)-based method for visual-inertial odometry, which fuses the IMU measurements with observations of visual feature tracks provided by the camera. The key contribution of this work is a computationally tractable approach for taking into account the rolling-shutter effect, incurring only minimal approximations. The experimental results from the application of the method show that it is able to track, in real time, the position of a mobile phone moving in an unknown environment with an error accumulation of approximately 0.8% of the distance travelled, over hundreds of meters.

GPS-denied Indoor and Outdoor Monocular Vision Aided Navigation and Control of Unmanned Aircraft

Abstract: GPS-denied closed-loop autonomous control of unstable Unmanned Aerial Vehicles (UAVs) such as rotorcraft using information from a monocular camera has been an open problem. Most proposed Vision aided Inertial Navigation Systems (V-INSs) have been too computationally intensive or do not have sufficient integrity for closed-loop flight. We provide an affirmative answer to the question of whether V-INSs can be used to sustain prolonged real-world GPS-denied flight by presenting a V-INS that is validated through autonomous flight-tests over prolonged closed-loop dynamic operation in both indoor and outdoor GPS-denied environments with two rotorcraft unmanned aircraft systems (UASs). The architecture efficiently combines visual feature information from a monocular camera with measurements from inertial sensors. Inertial measurements are used to predict frame-to-frame transition of online selected feature locations, and the difference between predicted and observed feature locations is used to bind in real-time the inertial measurement unit drift, estimate its bias, and account for initial misalignment errors. A novel algorithm to manage a library of features online is presented that can add or remove features based on a measure of relative confidence in each feature location. The resulting V-INS is sufficiently efficient and reliable to enable real-time implementation on resource-constrained aerial vehicles. The presented algorithms are validated on multiple platforms in real-world conditions: through a 16-min flight test, including an autonomous landing, of a 66 kg rotorcraft UAV operating in an unconctrolled outdoor environment without using GPS and through a Micro-UAV operating in a cluttered, unmapped, and gusty indoor environment. © 2013 Wiley Periodicals, Inc.

A Comparison between Different Error Modeling of MEMS Applied to GPS/INS Integrated Systems

Abstract: Advances in the development of micro-electromechanical systems (MEMS) have made possible the fabrication of cheap and small dimension accelerometers and gyroscopes, which are being used in many applications where the global positioning system (GPS) and the inertial navigation system (INS) integration is carried out, i.e., identifying track defects, terrestrial and pedestrian navigation, unmanned aerial vehicles (UAVs), stabilization of many platforms, etc. Although these MEMS sensors are low-cost, they present different errors, which degrade the accuracy of the navigation systems in a short period of time. Therefore, a suitable modeling of these errors is necessary in order to minimize them and, consequently, improve the system performance. In this work, the most used techniques currently to analyze the stochastic errors that affect these sensors are shown and compared: we examine in detail the autocorrelation, the Allan variance (AV) and the power spectral density (PSD) techniques. Subsequently, an analysis and modeling of the inertial sensors, which combines autoregressive (AR) filters and wavelet de-noising, is also achieved. Since a low-cost INS (MEMS grade) presents error sources with short-term (high-frequency) and long-term (low-frequency) components, we introduce a method that compensates for these error terms by doing a complete analysis of Allan variance, wavelet de-nosing and the selection of the level of decomposition for a suitable combination between these techniques. Eventually, in order to assess the stochastic models obtained with these techniques, the Extended Kalman Filter (EKF) of a loosely-coupled GPS/INS integration strategy is augmented with different states. Results show a comparison between the proposed method and the traditional sensor error models under GPS signal blockages using real data collected in urban roadways.

Introduction to Avionics Systems

Abstract: Foreword. Preface. Acknowledgements. 1: Introduction. 1.1. Importance and role of avionics. 1.2. The avionic environment. 1.3. Choice of units. 2: Displays and man-machine interaction. 2.1. Introduction. 2.2. aHead up displays. 2.3. Helmet mounted displays. 2.4. Computer aided optical design. 2.5. Discussion of HUDs vs HMDs. 2.6. Head down displays. 2.7. Data fusion. 2.8. Intelligent displays management. 2.9. Displays technology. 2.10. Control and data entry. Further reading. 3: Aerodynamics and aircraft control. 3.1. Introduction. 3.2. aBasic aerodynamics. 3.3. Aircraft stability. 3.4. Aircraft dynamics. 3.5. Longitudinal control and response. 3.6. Lateral control. 3.7. Powered flying controls. 3.8. Auto-stabilisation systems. Further reading. 4: Fly-by-wire flight control. 4.1. Introduction. 4.2. aFly-by-wire flight control features and advantages. 4.3. Control laws. 4.4. Redundancy and failure survival. 4.5. Digital implementation. 4.6. Fly-by-light flight control. Further reading. 5: Inertial sensors and attitude derivation. 5.1. Introduction. 5.2. Gyros and accelerometers. 5.3. Attitude derivation. Further reading. 6: Navigation systems. 6.1. Introduction and basic principles. 6.2. Inertial navigation. 6.3. Aided IN systems and Kalman filters. 6.4. Attitude and heading reference systems. 6.5. GPS - global positioning systems. 6.6. Terrain reference navigation. Further reading. 7: Air data and air data systems. 7.1. Introduction. 7.2. Air data information and its use. 7.3. Derivation of air data laws and relationships. 7.4. Air data sensors and computing. Further reading. 8: Autopilots and flight management systems. 8.1. Introduction. 8.2. Autopilots. 8.3. Flight management systems. Further reading. 9: Avionic systems integration. 9.1. Introduction and background. 9.2. Data bus systems. 9.3. Integrated modular avionics. 9.4. Commercial off-the-shelf (COTS). Further reading. 10: Unmanned air vehicles. 10.1. Importance of unmanned air vehicles. 10.2. UAV avionics. Further reading. Glossary of terms. List of symbols. List of abbreviations. Index.

Tutorial: Implementing a Pedestrian Tracker Using Inertial Sensors

Abstract: Shoe-mounted inertial sensors offer a convenient way to track pedestrians in situations where other localization systems fail. This tutorial outlines a simple yet effective approach for implementing a reasonably accurate tracker. This Web extra presents the Matlab implementation and a few sample recordings for implementing the pedestrian inertial tracking system using an error-state Kalman filter for zero-velocity updates (ZUPTs) and orientation estimation.

Global Navigation Satellite Systems, Inertial Navigation, and Integration

Abstract: An updated guide to GNSS, and INS, and solutions to real-world GNSS/INS problems with Kalman filteringWritten by recognized authorities in the field, this third edition of a landmark work provides engineers, computer scientists, and others with a working familiarity of the theory and contemporary applications of Global Navigation Satellite Systems (GNSS), Inertial Navigational Systems, and Kalman filters. Throughout, the focus is on solving real-world problems, with an emphasis on the effective use of state-of-the-art integration techniques for those systems, especially the application of Kalman filtering. To that end, the authors explore the various subtleties, common failures, and inherent limitations of the theory as it applies to real-world situations, and provide numerous detailed application examples and practice problems, including GNSS-aided INS (tightly and loosely coupled), modeling of gyros and accelerometers, and SBAS and GBAS.Drawing upon their many years of experience with GNSS, INS, and the Kalman filter, the authors present numerous design and implementation techniques not found in other professional references. The Third Edition includes:Updates on the upgrades in existing GNSS and other systems currently under developmentExpanded coverage of basic principles of antenna design and practical antenna design solutionsExpanded coverage of basic principles of receiver design and an update of the foundations for code and carrier acquisition and tracking within a GNSS receiverExpanded coverage of inertial navigation, its history, its technology, and the mathematical models and methods used in its implementationDerivations of dynamic models for the propagation of inertial navigation errors, including the effects of drifting sensor compensation parametersGreatly expanded coverage of GNSS/INS integration, including derivation of a unified GNSS/INS integration model, its MATLAB implementations, and performance evaluation under simulated dynamic conditionsThe companion website includes updated background material; additional MATLAB scripts for simulating GNSS-only and integrated GNSS/INS navigation; satellite position determination; calculation of ionosphere delays; and dilution of precision.

Continuous Hidden Markov Model for Pedestrian Activity Classification and Gait Analysis

Abstract: This paper presents a method for pedestrian activity classification and gait analysis based on the microelectromechanical-systems inertial measurement unit (IMU). The work targets two groups of applications, including the following: 1) human activity classification and 2) joint human activity and gait-phase classification. In the latter case, the gait phase is defined as a substate of a specific gait cycle, i.e., the states of the body between the stance and swing phases. We model the pedestrian motion with a continuous hidden Markov model (HMM) in which the output density functions are assumed to be Gaussian mixture models. For the joint activity and gait-phase classification, motivated by the cyclical nature of the IMU measurements, each individual activity is modeled by a “circular HMM.” For both the proposed classification methods, proper feature vectors are extracted from the IMU measurements. In this paper, we report the results of conducted experiments where the IMU was mounted on the humans' chests. This permits the potential application of the current study in camera-aided inertial navigation for positioning and personal assistance for future research works. Five classes of activity, including walking, running, going upstairs, going downstairs, and standing, are considered in the experiments. The performance of the proposed methods is illustrated in various ways, and as an objective measure, the confusion matrix is computed and reported. The achieved relative figure of merits using the collected data validates the reliability of the proposed methods for the desired applications.

A low-cost INS/GPS integration methodology based on random forest regression

Abstract: This paper, for the first time, introduces a random forest regression based Inertial Navigation System (INS) and Global Positioning System (GPS) integration methodology to provide continuous, accurate and reliable navigation solution. Numerous techniques such as those based on Kalman filter (KF) and artificial intelligence approaches exist to fuse the INS and GPS data. The basic idea behind these fusion techniques is to model the INS error during GPS signal availability. In the case of outages, the developed model provides an INS error estimates, thereby maintaining the continuity and improving the navigation solution accuracy. KF based approaches possess several inadequacies related to sensor error model, immunity to noise, and computational load. Alternatively, neural network (NN) proposed to overcome KF limitations works unsatisfactorily for low-cost INS, as they suffer from poor generalization capability due to the presence of high amount of noise. In this study, random forest regression has shown to effectively model the highly non-linear INS error due to its improved generalization capability. To evaluate the proposed method effectiveness in bridging the period of GPS outages, four simulated GPS outages are considered over a real field test data. The proposed methodology illustrates a significant reduction in the positional error by 24-56%.

A Wireless Micro Inertial Measurement Unit (IMU)

Abstract: In this paper, we present a wireless micro inertial measurement unit (IMU) with the smallest volume and weight requirements available at the moment. With a size of 22 mm × 14 mm × 4 mm (1.2 cm3), this IMU provides full control over the data of a three-axis accelerometer, a three-axis gyroscope, and a three-axis magnetometer. It meets the design prerequisites of a space-saving design and eliminates the need for hard-wired data communication, while still being competitive with state-of-the-art commercially available MEMS IMUs. A CC430 microcontroller sends the collected raw data to a base station wirelessly with a maximum sensor sample rate of 640 samples/s. Thereby, the IMU performance is optimized by moving data post processing to the base station. This development offers important features in portable applications with their significant size and weight requirements. Due to its small size, the IMU can be integrated into clothes or shoes for accurate position estimation in mobile applications and location-based services. We demonstrate the performance of the wireless micro IMU in a localization experiment where it is placed on a shoe for pedestrian tracking. With sensor data-fusion based on a Kalman filter combined with the zero velocity update, we can precisely track a person in an indoor area.

Relocating Underwater Features Autonomously Using Sonar-Based SLAM

Abstract: This paper describes a system for reacquiring features of interest in a shallow-water ocean environment, using autonomous underwater vehicles (AUVs) equipped with low-cost sonar and navigation sensors. In performing mine countermeasures, it is critical to enable AUVs to navigate accurately to previously mapped objects of interest in the water column or on the seabed, for further assessment or remediation. An important aspect of the overall system design is to keep the size and cost of the reacquisition vehicle as low as possible, as it may potentially be destroyed in the reacquisition mission. This low-cost requirement prevents the use of sophisticated AUV navigation sensors, such as a Doppler velocity log (DVL) or an inertial navigation system (INS). Our system instead uses the Proviewer 900-kHz imaging sonar from Blueview Technologies, which produces forward-looking sonar (FLS) images at ranges up to 40 mat approximately 4 Hz. In large volumes, it is hoped that this sensor can be manufactured at low cost. Our approach uses a novel simultaneous localization and mapping (SLAM) algorithm that detects and tracks features in the FLS images to renavigate to a previously mapped target. This feature-based navigation (FBN) system incorporates a number of recent advances in pose graph optimization algorithms for SLAM. The system has undergone extensive field testing over a period of more than four years, demonstrating the potential for the use of this new approach for feature reacquisition. In this report, we review the methodologies and components of the FBN system, describe the system's technological features, review the performance of the system in a series of extensive in-water field tests, and highlight issues for future research.

The performance analysis of a real-time integrated INS/GPS vehicle navigation system with abnormal GPS measurement elimination.

Abstract: The integration of an Inertial Navigation System (INS) and the Global Positioning System (GPS) is common in mobile mapping and navigation applications to seamlessly determine the position, velocity, and orientation of the mobile platform. In most INS/GPS integrated architectures, the GPS is considered to be an accurate reference with which to correct for the systematic errors of the inertial sensors, which are composed of biases, scale factors and drift. However, the GPS receiver may produce abnormal pseudo-range errors mainly caused by ionospheric delay, tropospheric delay and the multipath effect. These errors degrade the overall position accuracy of an integrated system that uses conventional INS/GPS integration strategies such as loosely coupled (LC) and tightly coupled (TC) schemes. Conventional tightly coupled INS/GPS integration schemes apply the Klobuchar model and the Hopfield model to reduce pseudo-range delays caused by ionospheric delay and tropospheric delay, respectively, but do not address the multipath problem. However, the multipath effect (from reflected GPS signals) affects the position error far more significantly in a consumer-grade GPS receiver than in an expensive, geodetic-grade GPS receiver. To avoid this problem, a new integrated INS/GPS architecture is proposed. The proposed method is described and applied in a real-time integrated system with two integration strategies, namely, loosely coupled and tightly coupled schemes, respectively. To verify the effectiveness of the proposed method, field tests with various scenarios are conducted and the results are compared with a reliable reference system.

On the Consistency of Vision-Aided Inertial Navigation

Abstract: In this paper, we study estimator inconsistency in Vision-aided Inertial Navigation Systems (VINS). We show that standard (linearized) estimation approaches, such as the Extended Kalman Filter (EKF), can fundamentally alter the system observability properties, in terms of the number and structure of the unobservable directions. This in turn allows the influx of spurious information, leading to inconsistency. To address this issue, we propose an Observability-Constrained VINS (OC-VINS) methodology that explicitly adheres to the observability properties of the true system.We apply our approach to the Multi-State Constraint Kalman Filter (MSC-KF), and provide both simulation and experimental validation of the effectiveness of our method for improving estimator consistency.

Cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging

Abstract: The implementation challenges of cooperative localization by dual foot-mounted inertial sensors and inter-agent ranging are discussed, and work on the subject is reviewed. System architecture and sensor fusion are identified as key challenges. A partially decentralized system architecture based on step-wise inertial navigation and step-wise dead reckoning is presented. This architecture is argued to reduce the computational cost and required communication bandwidth by around two orders of magnitude while only giving negligible information loss in comparison with a naive centralized implementation. This makes a joint global state estimation feasible for up to a platoon-sized group of agents. Furthermore, robust and low-cost sensor fusion for the considered setup, based on state space transformation and marginalization, is presented. The transformation and marginalization are used to give the necessary flexibility for presented sampling-based updates for the inter-agent ranging and ranging free fusion of the two feet of an individual agent. Finally, the characteristics of the suggested implementation are demonstrated with simulations and a real-time system implementation.

A Fast SINS Initial Alignment Scheme for Underwater Vehicle Applications

Abstract: To achieve high Strapdown Inertial Navigation System (SINS) alignment accuracy within a short period of time is still a challenging issue for underwater vehicles. In this paper, a new SINS initial alignment scheme aided by the velocity derived from Doppler Velocity Log (DVL) is proposed to solve this problem. In the stage of the coarse alignment, the velocity of DVL is employed to reduce the impact of the linear motion. With a backtracking framework, the fine alignment runs with the data recorded during the process of the coarse alignment and thus will speed up the overall alignment process. In addition, by using this new scheme, it is equivalent to length the alignment time for both coarse and fine alignments, so the accuracy of the alignments will be improved. In order to reduce the volume of the data that has to be recorded, a new model for SINS fine alignment is derived in the inertial reference frame which makes it feasible for real time applications. The experimental results are presented for both unaided static and in-motion alignment using DVL aiding. It is clearly shown that the proposed method meets the requirement of SINS alignment for underwater vehicles.

Tightly coupled ultrashort baseline and inertial navigation system for underwater vehicles: An experimental validation

Abstract: This paper presents a new ultrashort baseline (USBL) tightly coupled integration technique to enhance error estimation in low-cost strapdown inertial navigation systems (INSs), with application to underwater vehicles. In the proposed strategy, the acoustic array spatial information is directly exploited in an extended Kalman filter (EKF) implemented in a direct feedback structure. Instead of using the USBL position fixes or computed range and elevation/bearing angles to correct the INS error drifts, as in classical loosely coupled strategies, the novel tightly coupled strategy directly embeds in the EKF the round-trip-time and time-difference-of-arrival of the acoustic signals arriving at the onboard receivers. The enhanced performance of the proposed filtering technique is evidenced both through extensive numerical simulations and with experimental data obtained in field tests at sea. The tightly coupled filter is also shown to be able to operate closer to theoretical performance lower bounds, such as the posterior Cramer-Rao lower bound, using Monte-Carlo simulations. This paper details the design and description of an USBL/INS prototype to be used as a low-cost navigation system, including the acoustic processing and positioning system, fully developed in-house. The developed system validates the usage of the proposed technique with real data in real world operation scenarios, and its enhanced performance compared to classical strategies is evaluated experimentally (median improvement level of 15% in typical operating conditions). Improved and faster convergence to nominal trajectories from multiple initial conditions, as well as enhanced accelerometer and rate gyros estimation capabilities, are also demonstrated experimentally for the new tightly coupled filter. © 2013 Wiley Periodicals, Inc.

Improving Accuracy of the Vehicle Attitude Estimation for Low-Cost INS/GPS Integration Aided by the GPS-Measured Course Angle

Abstract: This paper presents a method using the Global Positioning System (GPS)-measured course angle to improve the accuracy of the vehicle attitude estimation for low-cost inertial navigation system/GPS (INS/GPS) integration. Observability properties of the error states in the low-cost integration navigation system are first analyzed, indicating that the attitude estimation is severely affected by vehicle maneuvers, particularly the yaw angle. The pitch and roll angles are strongly observed; hence, the observability of these two angles is nearly free of influence caused by vehicle maneuvers, and these two angles can be accurately estimated. To improve the yaw-angle estimation, we propose a cascaded Kalman filter to deal with the yaw angle separately with the aid of the GPS-measured course angle. Additionally, two switching rules are established to remove the influence caused by the sideslip angle and GPS noise. The experimental results validate the observability analysis of the low-cost INS/GPS system and show that the proposed attitude estimation method can effectively improve the accuracy of the vehicle attitude estimation, suggesting that this technique is a viable candidate for many control applications used in cars.

Pedestrian Navigation Based on Inertial Sensors, Indoor Map, and WLAN Signals

Abstract: As satellite signals, e.g. GPS, are severely degraded indoors or not available at all, other methods are needed for indoor positioning. In this paper, we propose methods for combining information from inertial sensors, indoor map, and WLAN signals for pedestrian indoor navigation. We present results of field tests where complementary extended Kalman filter was used to fuse together WLAN signal strengths and signals of an inertial sensor unit including one gyro and three-axis accelerometer. A particle filter was used to combine the inertial data with map information. The results show that both the map information and WLAN signals can be used to improve the pedestrian dead reckoning estimate based on inertial sensors. The results with different combinations of the available sensor information are compared.

Towards Consistent Vision-Aided Inertial Navigation

Abstract: In this paper, we study estimator inconsistency in Vision-aided Inertial Navigation Systems (VINS) from a standpoint of system observability. We postulate that a leading cause of inconsistency is the gain of spurious information along unobservable directions, resulting in smaller uncertainties, larger estimation errors, and possibly even divergence.We develop an Observability-Constrained VINS (OC-VINS), which explicitly enforces the unobservable directions of the system, hence preventing spurious information gain and reducing inconsistency. Our analysis, along with the proposed method for reducing inconsistency, are extensively validated with simulation trials and real-world experiments.

A novel scheme for DVL-aided SINS in-motion alignment using UKF techniques

Abstract: In-motion alignment of Strapdown Inertial Navigation Systems (SINS) without any geodetic-frame observations is one of the toughest challenges for Autonomous Underwater Vehicles (AUV). This paper presents a novel scheme for Doppler Velocity Log (DVL) aided SINS alignment using Unscented Kalman Filter (UKF) which allows large initial misalignments. With the proposed mechanism, a nonlinear SINS error model is presented and the measurement model is derived under the assumption that large misalignments may exist. Since a priori knowledge of the measurement noise covariance is of great importance to robustness of the UKF, the covariance-matching methods widely used in the Adaptive KF (AKF) are extended for use in Adaptive UKF (AUKF). Experimental results show that the proposed DVL-aided alignment model is effective with any initial heading errors. The performances of the adaptive filtering methods are evaluated with regards to their parameter estimation stability. Furthermore, it is clearly shown that the measurement noise covariance can be estimated reliably by the adaptive UKF methods and hence improve the performance of the alignment.

Invariant Observer Design for a Helicopter UAV Aided Inertial Navigation System

Abstract: The invariant observer is a recently introduced constructive nonlinear design method for symmetry-possessing systems such as the magnetometer-plus-global positioning system (GPS)-aided inertial navigation system (INS) example considered in this paper. The resulting observer guarantees a simplified form of the nonlinear estimation error dynamics, which can be stabilized by a proper choice of observer gains using a nonlinear analysis. A systematic approach to this step is the invariant Extended Kalman Filter (EKF), which is modified from its originally proposed form and applied to the aided INS example to obtain the observer gains. The resulting invariant observer is implemented onboard an outdoor helicopter unmanned aerial vehicle platform and successfully validated in experiment and demonstrates an improvement in performance over a conventional (non-invariant) EKF design.

Stabilization of a flying vehicle on a taut tether using inertial sensing

Abstract: Given a hover-capable flying vehicle attached to a fixed point by a taut tether, we present a novel method to recover the vehicle's relative position and absolute orientation. The proposed method requires only on-board inertial sensors, and indirectly measures the string force, enabling the additional use of the tether as a physical user interaction medium. We present the vertical-plane dynamics of such a system and the localization approach, discuss sensitivity issues, and implement an estimator and controller based on the presented model. We demonstrate the method experimentally on a tethered quadrocopter in the Flying Machine Arena, using both a vertical-plane-constrained vehicle and in 3D.

Change detection in urban areas by object-based analysis and on-the-fly comparison of multi-view ALS data

Abstract: The use of helicopters as a sensor platform offers flexible fields of application due to adaptable flying speed at low flight levels. Modern helicopters are equipped with radar altimeters, inertial navigation systems (INS), forward-looking cameras and even laser scanners for automatic obstacle avoidance. If the 3D geometry of the terrain is already available, the analysis of airborne laser scanner (ALS) measurements may also be used for terrain-referenced navigation and change detection. In this paper, we present a framework for on-the-fly comparison of current ALS data to given reference data of an urban area. In contrast to classical difference methods, our approach extends the concept of occupancy grids known from robot mapping. However, it does not blur the measured information onto the grid cells. The proposed change detection method applies the Dempster–Shafer theory to identify conflicting evidence along the laser pulse propagation path. Additional attributes are considered to decide whether detected changes are of man-made origin or occurring due to seasonal effects. The concept of online change detection has been successfully validated in offline experiments with recorded ALS data streams. Results are shown for an urban test site at which multi-view ALS data were acquired at an interval of 1 year.

Strapdown inertial navigation system alignment based on marginalised unscented kalman filter

Abstract: This study concerns the strapdown inertial navigation system (SINS) initial alignment under marine mooring condition with large initial error. The ten-dimensional state initial alignment error functions of the SINS with inclusion of non-linear characteristics have been derived. It is pointed out for the first time that the non-linear functions are applied to only a subset of the elements of the state vector, that is, the velocities error and the misalignment angles. Then a computationally efficient refinement of the unscented transformation (UT) called marginalised UT (MUT) is investigated in these special non-linear systems with a linear substructure. A performance comparison between the extended Kalman filter (EKF), the UT-based Kalman filter (UKF) and the MUT-based Kalman filter (MUKF) demonstrates that both the UKF and the MUKF can outperform the EKF and the MUKF and can achieve, if not better, at least a comparable performance to the UKF, at a significantly lower expense.