Radio Frequency (RF) fingerprinting, based onWiFi or cellular signals, has been a popular approach to indoor localization. However, its adoption in the real world has been stymied by the need for sitespecific calibration, i.e., the creation of a training data set comprising WiFi measurements at known locations in the space of interest. While efforts have been made to reduce this calibration effort using modeling, the need for measurements from known locations still remains a bottleneck. In this paper, we present Zee -- a system that makes the calibration zero-effort, by enabling training data to be crowdsourced without any explicit effort on the part of users. Zee leverages the inertial sensors (e.g., accelerometer, compass, gyroscope) present in the mobile devices such as smartphones carried by users, to track them as they traverse an indoor environment, while simultaneously performing WiFi scans. Zee is designed to run in the background on a device without requiring any explicit user participation. The only site-specific input that Zee depends on is a map showing the pathways (e.g., hallways) and barriers (e.g., walls). A significant challenge that Zee surmounts is to track users without any a priori, user-specific knowledge such as the user's initial location, stride-length, or phone placement. Zee employs a suite of novel techniques to infer location over time: (a) placement-independent step counting and orientation estimation, (b) augmented particle filtering to simultaneously estimate location and user-specific walk characteristics such as the stride length,(c) back propagation to go back and improve the accuracy of ocalization in the past, and (d) WiFi-based particle initialization to enable faster convergence. We present an evaluation of Zee in a large office building.

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Zee: zero-effort crowdsourcing for indoor localization

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.

A Survey of Indoor Inertial Positioning Systems for Pedestrians

This paper addresses reliable and accurate indoor localization using inertial sensors commonly found on commodity smartphones. We believe indoor positioning is an important primitive that can enable many ubiquitous computing applications. To tackle the challenges of drifting in estimation, sensitivity to phone position, as well as variability in user walking profiles, we have developed algorithms for reliable detection of steps and heading directions, and accurate estimation and personalization of step length. We've built an end-to-end localization system integrating these modules and an indoor floor map, without the need for infrastructure assistance. We demonstrated for the first time a meter-level indoor positioning system that is infrastructure free, phone position independent, user adaptive, and easy to deploy. We have conducted extensive experiments on users with smartphone devices, with over 50 subjects walking over an aggregate distance of over 40 kilometers. Evaluation results showed our system can achieve a mean accuracy of 1.5m for the in-hand case and 2m for the in-pocket case in a 31m×15m testing area.

A reliable and accurate indoor localization method using phone inertial sensors

Location information is an important source of context for ubiquitous computing systems. This paper looks at how a foot-mounted inertial unit, a detailed building model, and a particle filter can be combined to provide absolute positioning, despite the presence of drift in the inertial unit and without knowledge of the user's initial location. We show how to handle multiple floors and stairways, how to handle symmetry in the environment, and how to initialise the localisation algorithm using WiFi signal strength to reduce initial complexity.We evaluate the entire system experimentally, using an independent tracking system for ground truth. Our results show that we can track a user throughout a 8725 m2 building spanning three floors to within 0.5m 75% of the time, and to within 0.73 m 95% of the time.

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Pedestrian localisation for indoor environments

In this paper, we investigate the problem of detecting-time epochs when zero-velocity updates can be applied in a foot-mounted inertial navigation (motion-tracking) system. We examine three commonly used detectors: the acceleration-moving variance detector, the acceleration-magnitude detector, and the angular rate energy detector. We demonstrate that all detectors can be derived within the same general likelihood ratio test (LRT) framework, given the different prior knowledge about the sensor signals. Further, by combining all prior knowledge, we derive a new LRT detector. Subsequently, we develop a methodology to evaluate the performance of the detectors. Employing the developed methodology, we evaluate the performance of the detectors using leveled ground, slow (approximately 3 km/h) and normal (approximately 5 km/h) gait data. The test results are presented in terms of detection versus false-alarm probability. Our preliminary results show that the new detector performs marginally better than the angular rate energy detector that outperforms both the acceleration-moving variance detector and the acceleration-magnitude detector.

Zero-Velocity Detection—An Algorithm Evaluation

In this paper we describe a new Bayesian estimation approach for simultaneous mapping and localization for pedestrians based on odometry with foot mounted inertial sensors. When somebody walks within a constrained area such as a building, then even noisy and drift-prone odometry measurements can give us information about features like turns, doors, and walls, which we can use to build a form of a map of the explored area, especially when these features are revisited over time. Our initial results for our novel scheme which we call "FootSLAM" are very surprising in that true SLAM with stable relative positioning accuracy of 1-2 meters for pedestrians is indeed possible based on inertial sensors alone without any prior known building indoor layout. Furthermore, the 2D maps obtained even for just 10 minutes of walking converge to a good approximation of the true layout forming the basis for future automated collaborative mapping of buildings.

/pdf/simultaneous-localization-and-mapping-for-pedestrians-using-5ao80xydn3.pdf

Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors

An algorithm for integrating foot-mounted inertial sensors into a Bayesian location estimation framework is presented. The proposed integration scheme is based on a cascaded estimation architecture. A lower Kalman filter is used to estimate the step-wise change of position and direction of the foot. These estimates are used in turn as measurements in an upper particle filter, which is able to incorporate nonlinear map-matching techniques. Experimental data is used to verify the proposed algorithm.

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Integration of foot-mounted inertial sensors into a Bayesian location estimation framework

An algorithm for integrating foot-mounted inertial sensor platforms is presented. The proposed integration scheme is based on a cascaded estimation architecture. A lower Kalman filter is used to estimate the step-wise change of position and direction of one or optionally both feet respectively. These estimates are used in turn as measurements in an upper particle filter, which is able to incorporate nonlinear map-matching techniques. To ease the integration of both feet a simple mechanical pedestrian model is developed. The proposed algorithm is verified using computer simulations and experimental data.

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Cascaded estimation architecture for integration of foot-mounted inertial sensors

In this paper we have presented an indoor positioning system for pedestrians combining Wireless LAN fingerprinting with foot mounted inertial and magnetometer sensors. To achieve a system capable of real-time processing we have employed a hierarchical Bayesian filtering approach using cascaded extended Kalman filters. The accuracy of the combined system was quantitatively evaluated in a real building against ground-truth. Our results show that accuracy is much higher than using Wireless LAN fingerprinting alone; in our experiment we achieved a positioning error (standard deviation) of roughly 1.6 meters. The approach requires only minimal fingerprinting effort since the high accuracy is achieved by the support of the inertial-based step estimation in the overall estimation process.

/pdf/development-and-evaluation-of-a-combined-wlan-and-inertial-2rrks5rksr.pdf

Development and Evaluation of a Combined WLAN and Inertial Indoor Pedestrian Positioning System

A sequential Bayesian estimation algorithm for multipath mitigation is presented, with an underlying movement model that is especially designed for dynamic channel scenarios. In order to facilitate efficient integration into receiver tracking loops, it builds upon complexity reduction concepts that previously have been applied within maximum likelihood (ML) estimators. To demonstrate its capabilities under different GNSS signal conditions, simulation results are presented for both BPSK-modulated and BOC-(1,1) modulated navigation signals.

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Bernhard Krach

Papers

Simultaneous localization and mapping for pedestrians using only foot-mounted inertial sensors

Integration of foot-mounted inertial sensors into a Bayesian location estimation framework

Cascaded estimation architecture for integration of foot-mounted inertial sensors

Development and Evaluation of a Combined WLAN and Inertial Indoor Pedestrian Positioning System

Bayesian Time Delay Estimation of GNSS Signals in Dynamic Multipath Environments