This paper provides an overview of the most significant existing methods for indoor positioning on a contemporary smartphone. The approaches include Wi-Fi and Bluetooth based positioning, magnetic field fingerprinting, map aided navigation using building floor plans, and aiding from self-contained sensors. Wi-Fi and Bluetooth based positioning methods considered in this survey are fingerprint approaches that determine a user's position using a database of radio signal strength measurements that were collected earlier at known locations. Magnetic field fingerprinting can be used in an information fusion algorithm to improve positioning. The map-matching algorithms include application of wall constraints, topological indoor maps, and building geometry for heading correction. Finally, methods for step counting, step length and direction estimation, orientation tracking, motion classification, transit mode detection, and floor change detection in multi-storey buildings are discussed.

A Survey of Selected Indoor Positioning Methods for Smartphones

Recent years have seen the advent of new RF-localization systems that demonstrate tens of centimeters of accuracy. However, such systems require either deployment of new infrastructure, or extensive fingerprinting of the environment through training or crowdsourcing, impeding their wide-scale adoption. We present Ubicarse, an accurate indoor localization system for commodity mobile devices, with no specialized infrastructure or fingerprinting. Ubicarse enables handheld devices to emulate large antenna arrays using a new formulation of Synthetic Aperture Radar (SAR). Past work on SAR requires measuring mechanically controlled device movement with millimeter precision, far beyond what commercial accelerometers can provide. In contrast, Ubicarse's core contribution is the ability to perform SAR on handheld devices twisted by their users along unknown paths. Ubicarse is not limited to localizing RF devices; it combines RF localization with stereo-vision algorithms to localize common objects with no RF source attached to them. We implement Ubicarse on a HP SplitX2 tablet and empirically demonstrate a median error of 39 cm in 3-D device localization and 17 cm in object geotagging in complex indoor settings.

/pdf/accurate-indoor-localization-with-zero-start-up-cost-4dgmdhgi5l.pdf

Accurate indoor localization with zero start-up cost

The particle filter (PF) provides a kind of novel technique for estimating the hidden states of the nonlinear and/or non-Gaussian systems. However, the general PF always suffers from the particle impoverishment problem, which can lead to the misleading state estimation results. To cope with this problem, a modified particle filter, i.e., intelligent particle filter (IPF), is proposed in this paper. It is inspired from the genetic algorithm. The particle impoverishment in general PF mainly results from the poverty of particle diversity. In IPF, the genetic-operators-based strategy is designed to further improve the particle diversity. It should be pointed out that the general PF is a special case of the proposed IPF with the specified parameters. Two experiment examples show that IPF mitigates particle impoverishment and provides more accurate state estimation results compared with the general PF. Finally, the proposed IPF is implemented for real-time fault detection on a three-tank system, and the results are satisfactory.

Intelligent Particle Filter and Its Application to Fault Detection of Nonlinear System

https://cyberleninka.org/article/n/1310866.pdf

Transport poverty meets the digital divide : accessibility and connectivity in rural communities

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

/pdf/review-of-visual-odometry-types-approaches-challenges-and-51oufuic7c.pdf

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

Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration is an introduction to the field of Integrated Navigation Systems. It serves as an It emphasizes that a survey of future inertial navigation concepts associated with the kalman filtering. He has been active in discussing the necessary details inertial navigator. Accuracy an introduction to the material. The penn state's navigation systems engineers, as it is derived his background. Students new to the design and, several others. The concept of the future applications engineering activity fundamentals. The explanation of newton's second law, is a best navigation focusing on the practical. The classical techniques for the area theory as they apply to critical. It emphasizes the detection or other systems emphasizing gps ephemeris solution path loss. It works and gbas he has been an easy to real.

Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration

To have a continuous navigation solution that does not suffer from interruption, GPS is integrated with relative positioning techniques such as odometry and inertial navigation Targeting a low-cost navigation solution for land vehicles, this paper uses a reduced multisensor system consisting of one microelectromechanical-system (MEMS)-based single-axis gyroscope used together with the vehicle's odometer, and the whole system is integrated with GPS This system provides a 2-D navigation solution, which is adequate for land vehicles The traditional technique for this multisensor integration problem is Kalman filtering (KF) Due to the inherent errors of MEMS inertial sensors and their stochastic nature, which is difficult to model, the KF with its linearized models has limited capabilities in providing accurate positioning Particle filtering (PF) has recently been suggested as a nonlinear filtering technique to accommodate arbitrary inertial sensor characteristics, motion dynamics, and noise distributions An enhanced version of PF is utilized in this paper and is called the Mixture PF Since PF can accommodate nonlinear models, this paper uses total-state nonlinear system and measurement models In addition, sophisticated models are used to model the stochastic drift of the MEMS-based gyroscope A nonlinear system identification technique based on parallel cascade identification (PCI) is used to model this stochastic gyroscope drift In this paper, the performance of the PCI model is compared with that of higher order autoregressive (AR) stochastic models Such higher order models are difficult to use with KF since the size of the dynamic matrix and the error-covariance matrix becomes very large and complicates the KF operation The performance of the proposed 2-D navigation solution using Mixture PF with both PCI and higher order AR models is examined by road-test trajectories in a land vehicle The two proposed combinations are compared with four other 2-D solutions: a Mixture PF with the Gauss-Markov (GM) model for the gyro drift, a Mixture PF with only white Gaussian noise (WGN) for stochastic gyro errors, and two different KF solutions with GM model for the gyro drift The experimental results show that the two proposed solutions outperform all the compared counterparts

Modeling the Stochastic Drift of a MEMS-Based Gyroscope in Gyro/Odometer/GPS Integrated Navigation

Recent technological advances in both GPS and low-cost microelectromechanical-system (MEMS)-based inertial sensors have enabled the monitoring of the location of moving platforms for numerous positioning and navigation (POS/NAV) applications. GPS is presently widely used in land vehicles. However, in some environments, the GPS signal may suffer from signal blockage and multipath effects that deteriorate the positioning accuracy. When miniaturized inside any moving platforms, the MEMS-based inertial navigation system (INS) can be integrated with GPS and enhance the performance in denied GPS environments (like in urban canyons). Targeting a low-cost navigation solution for land vehicles, this paper uses a reduced inertial sensor system (RISS) with MEMS-based inertial sensors. In this paper, the RISS consists of one single-axis gyroscope and a two-axis accelerometer used together with the vehicle's odometer, and the whole system is integrated with GPS to obtain a 3-D navigation solution. The traditional technique for this integration problem is Kalman filtering (KF). Due to the inherent errors of MEMS inertial sensors and the relatively high noise levels associated with their measurements, KF has limited capabilities in providing accurate positioning. Particle filtering (PF) was recently suggested as a nonlinear filtering technique to accommodate arbitrary inertial sensor characteristics, motion dynamics, and noise distributions. An enhanced version of PF is utilized in this paper and is called Mixture PF. The performance of the proposed 3-D navigation solution using Mixture PF for RISS/GPS integration is examined by road-test trajectories in a land vehicle. The proposed method is compared with four other solutions: 1) 3-D solution using KF for full INS/GPS integration; 2) 2-D solution using KF for RISS/GPS integration; 3) 2-D solution using Mixture PF for RISS/GPS integration; and 4) 3-D solution using sampling/importance resampling (SIR) PF for RISS/GPS integration. The experimental results show that the proposed solution outperforms all the compared counterparts.

Low-Cost Three-Dimensional Navigation Solution for RISS/GPS Integration Using Mixture Particle Filter

Dead reckoning techniques such as inertial navigation and odometry are integrated with GPS to avoid interruption of navigation solutions due to lack of visible satellites. A common method to achieve a low-cost navigation solution for land vehicles is to use a MEMS-based inertial measurement unit (IMU) for integration with GPS. This integration is traditionally accomplished by means of a Kalman filter (KF). Due to the significant inherent errors of MEMS inertial sensors and their time-varying changes, which are difficult to model, severe position error growth happens during GPS outages. The positional accuracy provided by the KF is limited by its linearized models. A Particle filter (PF), being a nonlinear technique, can accommodate for arbitrary inertial sensor characteristics and motion dynamics. An enhanced version of the PF, called Mixture PF, is employed in this paper. It samples from both the prior importance density and the observation likelihood, leading to an improved performance. Furthermore, in order to enhance the performance of MEMS-based IMU/GPS integration during GPS outages, the use of pitch and roll calculated from the longitudinal and transversal accelerometers together with the odometer data as a measurement update is proposed in this paper. These updates aid the IMU and limit the positional error growth caused by two horizontal gyroscopes, which are a major source of error during GPS outages. The performance of the proposed method is examined on road trajectories, and results are compared to the three different KF-based solutions. The proposed Mixture PF with velocity, pitch, and roll updates outperformed all the other solutions and exhibited an average improvement of approximately 64% over KF with the same updates, about 85% over KF with velocity updates only, and around 95% over KF without any updates during GPS outages.

Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter

The present disclosure relates to methods of enhancing a navigation solution about a device and a platform, wherein the mobility of the device may be constrained or unconstrained within the platform, and wherein the navigation solution is provided even in the absence of normal navigational information updates (such as, for example, GNSS). More specifically, the present method comprises utilizing measurements from sensors (e.g. accelerometers, gyroscopes, magnetometers etc.) within the device to calculate and resolve the attitude of the device and the platform, and the attitude misalignment between the device and the platform.

Jacques Georgy

Papers

Fundamentals of Inertial Navigation, Satellite-based Positioning and their Integration

Modeling the Stochastic Drift of a MEMS-Based Gyroscope in Gyro/Odometer/GPS Integrated Navigation

Low-Cost Three-Dimensional Navigation Solution for RISS/GPS Integration Using Mixture Particle Filter

Enhanced MEMS-IMU/odometer/GPS integration using mixture particle filter

Methods of attitude and misalignment estimation for constraint free portable navigation