ETH - Indoor Positioning & Indoor Navigation


08:15 - 08:45	Michael Angermann, Patrick Robertson, Thomas Kemptner, Mohammed Khider (presenting author): A High Precision Reference Data Set for Pedestrian Navigation using Foot-Mounted Inertial Sensors In this paper we will describe a data collection methodology and reference data set that can be used by the indoor navigation community to verify and improve algorithms based on foot mounted inertial sensors. The data set is collected using a high precision optical reference system that is traditionally used in the film industry for human motion capturing or in specialist applications such as analysis of human motion in sports and medical rehabilitation. The data provides synchronous 6 degrees of freedom inertial measurement sensor readings from a foot mounted MEMS sensor array as well as the high resolution data from the optical tracking system providing location and orientation ground truth. We will also provide the results of algorithms that identify the rest phase during the human gait cycle which is an essential part of pedestrian dead reckoning systems for positioning.

08:45 - 09:15	Isaac Skog (presenting author, at KTH), John-Olof Nilsson, Peter Händel: Evaluation of Zero-Velocity Detectors for Foot-Mounted Inertial Navigation Systems An experimental study of the performance of four different zero-velocity detectors for a foot-mounted inertial sensor based pedestrian navigation system is presented. The four detectors in the study are the (i) acceleration moving variance detector, (ii) the acceleration magnitude detector, (iii) the angular rate energy detector, and (iv) a generalized likelihood ratio detector. The performance of each detector is assessed by the accuracy of the position solution provided by the navigation system employing the detector to perform zero-velocity updates. The results show that for leveled ground, forward gait, at a speed of 5 km/h, all detectors yields the same position accuracy. Moreover, the results also show that for the generalized likelihood ratio detector the threshold that yield the highest position accuracy, and the position accuracy at this threshold, is independent of the data window size of the detector.

09:15 - 09:30	Jasper Jahn (presenting author, at the Fraunhofer IIS), Jochen Seitz, Lucila Patino-Studencka, Ulrich Batzer, Javier Gutiérrez Boronat: Comparison and Evaluation of Acceleration Based Step Length Estimators for Handheld Devices The growing market of mobile phones equipped with accelerometers leads to new opportunities for personal navigation solutions. In this work we investigate current step length estimation algorithms regarding their performance using measurement data of handheld devices. The requirements for step length estimation with handheld devices differ from regular approaches which are usually based on known sensor poses. We compare mathematically four step length estimators and evaluate them with real data. The acceleration measurements at hip and hand show a distortion of the signal which makes it necessary to use more complex biomechanical models or to exploit empirical relations between measurements and step lengths.

09:30 - 09:45	Nadir Castaneda (presenting author, at the French Atomic Energy Commission) and Sylvie Lamy-Perbal: An improved shoe-mounted inertial navigation system This paper proposes an improved shoe-mounted inertial navigation system for pedestrian tracking. The improvements consist of a fuzzy logic (FL) procedure for better foot stance phase detection and an indirect Kalman filter (IKF) for drift correction based on the typical zero-updating (ZUPT) measurement and our proposed angular updating (AUPT) pseudo-measurement. We illustrate our findings using a real time implementation of the proposed approach.

Foot Mounted Pedestrian Navigation, Part 2

Session Chair: Prof. Harald Sternberg, HCU Hamburg

10:15 - 10:45	Ulrich Walder (presenting author), Thomas Bernoulli, Thomas Wiesflecker: Context-Adaptive Algorithms to Improve Indoor Positioning with Inertial Sensors Body-mounted inertial systems for indoor positioning and pedestrian guidance have some major advantages against other technologies. They do not require any preinstalled facilities, i.e. they can run completely autonomous and all the necessary components are standard equipment of a modern smart phone. The quick availability and autonomy is a special advantage in fields of application such as first responders and military, while the integration of all system components in an everyday life gadget makes it especially attractive for the consumer market. But in both cases there is a severe problem to solve: the positioning accuracy is very weak, if only a simple double integration of the accelerations is performed. In the following two context-adaptive algorithms to improve indoor positioning with inertial sensors are presented and the achieved results are discussed. The first algorithm enhances the detection of zero-velocity updates; the other method improves positioning by map matching.

10:45 - 11:00	Christian Ascher (presenting author, at Karlsruhe Institute of Technology), C. Kessler, M. Wankerl, G.F. Trommer: Dual IMU Indoor Navigation with Particle Filter based Map-Matching on a Smartphone In this paper an Indoor Navigation System with map-matching capabilities online on a smart phone is presented. The basis of the system is an in-house developed Integrated Pedestrian Navigation System, based on 2 low-cost IMUs and electronic compass and altimeter with a slightly drifting navigation solution. Combining this system with an additional laser ranger and SLAM algorithms, we are able to build accurate maps from office buildings for already visited rooms in post processing. To use these maps later on in real-time and without an expensive laser ranger but only the dual inertial system, this paper presents a map matching algorithm based on a new reduced particle filter. It can be used with both, pre-processed SLAM maps or with already available maps. Finally to smooth the resulting trajectory after particle filtering we propose the use of a new “balanced bubble band smoother” allowing the trajectory to optimally match to both, map and recorded IMU data. This new approach makes it possible to do map matching online on a smart phone.

11:00 - 11:15	John-Olof Nilsson (presenting author, at KTH), Isaac Skog, Peter Händel: Design choices, filter parameter tuning, and calibration of zero-velocity update aided inertial navigation systems for pedestrian navigation Inertial navigation systems (INSs) placed on the foot of the carrier together with a zero-velocity detector providing so-called zero-velocity-updates (ZUPTs) is a promising technique for infrastructure free, pedestrian indoor navigation. The ZUPTs aid the INS to limit its error growth, without the ZUPTs the errors growth is such that the system becomes useless after only a few seconds. When implementing the ZUPT-aided INS numerous design choices have to be made and multiple parameters tuned. This together with the high dynamic and frequency component ranges of the motion of the foot makes the system hard to optimize. In this study, we look at what design choices and tuning parameters of a ZUPT-aided INS there are. A heuristic approach to handling the large number of design choices and parameters is suggested. Based on analysis and experimental data we argue how the design choice should be made and the parameters set. The parameter settings are in clear contrast to many of the one found in the literature. Correct settings are shown to give a significant.

11:15 - 11:30	Antonio R. Jiménez (presenting author), Fernando Seco, J. Carlos Prieto and Jorge Guevara: Pedestrian Indoor Navigation by aiding a Foot-mounted IMU with RFID Signal Strength Measurements We present a method to accurately locate persons indoors using an Inertial Navigation System (INS) with a foot-mounted IMU, aided by the the Received Signal Strenght (RSS) of some active RFID tags. Other authors [Renaudin2007, Zhang2008] have already integrated IMUs with RFID tags in “loose” Kalman Filter (KF)-based solutions. They feed the KF with the residuals between inertial- and RFID-calculated positions, no Zero Velocity Updates (ZUPT) are employed. In this paper, we present a “tight” KF-based [Retscher2007] INS/RFID integration method using the residual between the INS-predicted range to tag, and the range derived from a generic RSS model. Our approach also includes ZUPTs at detected foot stances, and heading drift reduction using magnetometers. A 15-element error state Extended KF [Foxlin2005, Jiménez 2010] compensates positioning, velocity and attitude erros of the INS solution, as well as IMU biases. This methodology is valid for any kind of motion (lateral/backwards walk, running), and does not requiere an specific off-line calibration, nor for the user gait, neither for the location-dependent RSS fading in the building.

11:30 - 11:45	Peter Strömbäck (presenting author, at FOI), Jouni Rantakokko, Erika Emilsson: On the use of foot-mounted INS, UWB-ranging and opportunistic cooperation in high-accuracy indoor positioning systems In order to achieve an accurate, robust indoor localization system it is anticipated that a multi-sensor system approach is required. High-sensitivity GPS (HSGPS) receivers, inertial sensors, and local radio-based ranging are natural choices. These sensors may need to be complemented with e.g. barometric altimeters, magnetometers ultra-sonic sensors or soft information such as (full or partial) building floor-plans. In this paper we focus on examining the performance of a sub-set of the full system, namely foot-mounted inertial measurement units (IMUs) in combination with ultra-wideband impulse-radio (IR-UWB) ranging between cooperating soldiers. Experimental data is collected with two persons moving inside a building, where each individual is equipped with foot-mounted IMUs and UWB-ranging devices. In conjunction to ranging, the UWB-units are used to convey position and error estimates, which are used to improve the position accuracy. The increased accuracy obtained through cooperation will be evaluated for different movement scenarios.

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