Francisco Zampella

Unscented Kalman filter and Magnetic Angular Rate Update (MARU) for an improved Pedestrian Dead-Reckoning

The Extended Kalman Filter (EKF) has been the state of the art in Pedestrian Dead-Reckoning for foot-mounted Inertial Measurements Units. However due to the non-linearity in the propagation of the orientation the EKF is not the optimal Bayesian filter. We propose the usage of the Unscented Kalman Filter (UKF) as the integration algorithm for the inertial measurements. The UKF improves the mean and covariance propagation needed for the Kalman filter. Although the UKF provides a better estimate of the orientation, with Zero velocity UPdaTes (ZUPT) measurements, the yaw and the bias in the gyroscope associated with it becomes unobserved and might generate errors in the positioning. We studied the changes in the magnetic field during the stance phase and their relationship with the turn rates to propose three measurements using the magnetometer signal that will be called Magnetic Angular Rate Updates (MARUs). The first measurement uses the change in the angle of the magnetic field in the horizontal plane to measure the change in the yaw and provides a simple measurement for the UKF implementation. The second measurement relates the change in the magnetic field vector to the turn rate and provides information on the bias of the gyroscope for an UKF. The last measurement uses a first order approximation to generate a linear relationship with the gyroscope bias and therefore it can be used in an EKF. Finally we proposed a metric for the reliability of the stance as a way to use the pre and post stance information but adjusting the covariance of the measurements gradually from swing to stance. These methods were tested on real and simulated signals and they have shown improvements over the original PDR algorithms.

PDR with a Foot-Mounted IMU and Ramp Detection

The localization of persons in indoor environments is nowadays an open problem. There are partial solutions based on the deployment of a network of sensors (Local Positioning Systems or LPS). Other solutions only require the installation of an inertial sensor on the person’s body (Pedestrian Dead-Reckoning or PDR). PDR solutions integrate the signals coming from an Inertial Measurement Unit (IMU), which usually contains 3 accelerometers and 3 gyroscopes. The main problem of PDR is the accumulation of positioning errors due to the drift caused by the noise in the sensors. This paper presents a PDR solution that incorporates a drift correction method based on detecting the access ramps usually found in buildings. The ramp correction method is implemented over a PDR framework that uses an Inertial Navigation algorithm (INS) and an IMU attached to the person’s foot. Unlike other approaches that use external sensors to correct the drift error, we only use one IMU on the foot. To detect a ramp, the slope of the terrain on which the user is walking, and the change in height sensed when moving forward, are estimated from the IMU. After detection, the ramp is checked for association with one of the existing in a database. For each associated ramp, a position correction is fed into the Kalman Filter in order to refine the INS-PDR solution. Drift-free localization is achieved with positioning errors below 2 meters for 1,000-meter-long routes in a building with a few ramps.

Indoor Positioning Using Efficient Map Matching, RSS Measurements, and an Improved Motion Model

Unlike outdoor positioning, there is no unique solution to obtain the position of a person inside a building or in Global Navigation Satellite System (GNSS)-denied areas. Typical implementations indoor rely on dead reckoning or beacon-based positioning, but a robust estimation must combine several techniques to overcome their own drawbacks. In this paper, we present an indoor positioning system based on foot-mounted pedestrian dead reckoning (PDR) with an efficient map matching, received signal strength (RSS) measurements, and an improved motion model that includes the estimation of the turn rate bias. The system was implemented using a two-level structure with a low-level PDR filter and a high-level particle filter (PF) to include all the measurements. After studying the effect of the step displacement on the PFs proposed in the literature, we concluded that a new state with the turn rate bias (a nonobservable state in PDR) is needed to correctly estimate the error growth and, in the long term, correct the position and heading estimation. Additionally, the wall crossing detection of map matching was optimized as matrix operations, and a room grouping algorithm was proposed as a way to accelerate the process, achieving real-time execution with more than 100 000 particles in a building with more than 600 wall segments. We also include a basic path-loss model to use RSS measurements that allows a better initialization of the filter, fewer particles, and faster convergence, without the need for an extensive calibration. The inclusion of the map matching algorithm lowers the error level of the RSS-PDR positioning, from 1.9 to 0.75 m, 90% of the time. The system is tested in several trajectories to show the improvement in the estimated positioning, the time to convergence, and the required number of particlesThis work was supported by the LEMUR project (TIN2009-14114-C04-02), LORIS project (TIN2012-38080-C04-04),SMARTLOC project (CSIC-PIE Ref.201450E011) and the JAE PREDoc program. European Commission y Consejo Superior de Investigaciones Cientificas (Espana)

Improved Heuristic Drift Elimination (iHDE) for pedestrian navigation in complex buildings

The main problem of Pedestrian Dead-Reckoning (PDR) using only a body-attached IMU is the accumulation of heading errors. The heading provided by magnetometers in indoor buildings is in general not reliable. Recently, a new method was proposed called Heuristic Drift Elimination (HDE) that minimizes the heading error when navigating in buildings. It assumes that the majority of buildings have their corridors parallel to each other, or they intersect at right angles, and consequently most of the time the person walks along a straight path with a heading constrained to one of four possible directions. In this paper we study the performance of HDE-based methods in complex buildings, i.e. with pathways also oriented at 45°, long curved corridors, and wide areas where non-oriented motion is possible. We explain how the performance of the original HDE method can be deteriorated in complex buildings. We also propose an improved HDE method called iHDE, that is implemented over a PDR framework that uses foot-mounted inertial navigation with an Extended Kalman Filter (EKF). The EKF is fed with the iHDE-estimated orientation error, as well as the confidence over that correction. We experimentally evaluated the performance of the proposed iHDE-based PDR method, comparing it with the original HDE implementation. Results show that both methods perform very well in ideal orthogonal narrow-corridor buildings, and iHDE outperforms HDE for non-ideal trajectories (e.g. curved paths).

Improving indoor positioning using an efficient Map Matching and an extended motion model

Unlike outdoor positioning, there is no unique solution to obtain the position of a person inside a building or in Global Navigation Satellite System (GNSS)-denied areas. Typical implementations indoor rely on dead reckoning or beacon-based positioning, but a robust estimation must combine several techniques to overcome their own drawbacks. In this paper, we present an indoor positioning system based on foot-mounted pedestrian dead reckoning (PDR) with an efficient map matching, received signal strength (RSS) measurements, and an improved motion model that includes the estimation of the turn rate bias. The system was implemented using a two-level structure with a low-level PDR filter and a high-level particle filter (PF) to include all the measurements. After studying the effect of the step displacement on the PFs proposed in the literature, we concluded that a new state with the turn rate bias (a nonobservable state in PDR) is needed to correctly estimate the error growth and, in the long term, correct the position and heading estimation. Additionally, the wall crossing detection of map matching was optimized as matrix operations, and a room grouping algorithm was proposed as a way to accelerate the process, achieving real-time execution with more than 100 000 particles in a building with more than 600 wall segments. We also include a basic path-loss model to use RSS measurements that allows a better initialization of the filter, fewer particles, and faster convergence, without the need for an extensive calibration. The inclusion of the map matching algorithm lowers the error level of the RSS-PDR positioning, from 1.9 to 0.75 m, 90% of the time. The system is tested in several trajectories to show the improvement in the estimated positioning, the time to convergence, and the required number of particlesThis work was supported by the LEMUR project (TIN2009-14114-C04-02), LORIS project (TIN2012-38080-C04-04),SMARTLOC project (CSIC-PIE Ref.201450E011) and the JAE PREDoc program. European Commission y Consejo Superior de Investigaciones Cientificas (Espana)

Improved heuristic drift elimination with magnetically-aided dominant directions MiHDE for pedestrian navigation in complex buildings

The main problem of pedestrian dead-reckoning (PDR) using only a body-attached inertial measurement unit is the accumulation of heading errors. The heading provided by magnetometers in indoor buildings is in general not reliable and therefore it is commonly not used. Recently, a new method was proposed called heuristic drift elimination (HDE) that minimises the heading error when navigating in buildings. It assumes that the majority of buildings have their corridors parallel to each other, or they intersect at right angles, and consequently most of the time the person walks along a straight path with a heading constrained to one of the four possible directions. In this article we study the performance of HDE-based methods in complex buildings, i.e. with pathways also oriented at 45°, long curved corridors, and wide areas where non-oriented motion is possible. We explain how the performance of the original HDE method can be deteriorated in complex buildings, and also, how severe errors can appear in the case of false matches with the buildings dominant directions. Although magnetic compassing indoors has a chaotic behaviour, in this article we analyse large data-sets in order to study the potential use that magnetic compassing has to estimate the absolute yaw angle of a walking person. Apart from these analysis, this article also proposes an improved HDE method called Magnetically-aided Improved Heuristic Drift Elimination (MiHDE), that is implemented over a PDR framework that uses foot-mounted inertial navigation with an extended Kalman filter (EKF). The EKF is fed with the MiHDE-estimated orientation error, gyro bias corrections, as well as the confidence over that corrections. We experimentally evaluated the performance of the proposed MiHDE-based PDR method, comparing it with the original HDE implementation. Results show that both methods perform very well in ideal orthogonal narrow-corridor buildings, and MiHDE outperforms HDE for non-ideal trajectories (e.g. curved paths) and also makes it robust against potential false dominant direction matchings.

Simulation of foot-mounted IMU signals for the evaluation of PDR algorithms

A common problem in the evaluation of Pedestrian Dead Reckoning (PDR) algorithms is the determination of a good ground truth. Some authors propose the use of external motion capture systems, however, their setup, complexity, synchronization and limited coverage are important limitations. We propose the generation of a simulated IMU signal for pedestrians, that is obtained from a given 3D trajectory (position and attitude). The trajectory can be artificially generated or based on a real human walk pattern. This information can be used as a ground truth for the identification of systematic errors, or to obtain a statistical analysis of the effect of any noise added to the simulated signal. Any specific IMU can be simulated by adding its characteristic error pattern, and modifying them, the most influential IMU characteristics can be determined, and if possible minimized. We tested a PDR method based on an Inertial Navigation System (INS) using an Extended Kalman Filter (EKF) with a noiseless IMU signal. Since failures were detected in the stance phase, we proposed and tested some improvements. The influence of adding specific error patterns to the IMU signal were determined measuring their effect on the evolution of the standard deviation of the position error over time. The most influential source of error for an INS mechanization is the bias in the gyroscope, however the EKF-based PDR algorithm showed to diminish in a significant way many of the positioning errors. The IMU-simulation method is proposed as a way to compare several algorithms and to test new PDR improvements during algorithm design.

Robust indoor positioning fusing PDR and RF technologies: The RFID and UWB case

Indoor positioning is usually based on individual technologies that provide estimates of the trajectory of the person, or measures the ranges or angles between the user and known positions. Each technique has its advantages and problems, and a common way to overcome the drawbacks of single-technology solutions is to fuse the information from several system, but due to their non linear measurements, there is no optimal linear solution. We propose the use of a particle filter to fuse foot mounted inertial measurements with any additional Radio Frequency (RF) measurement. The information fusion is achieved propagating the position of the particles using the relative step displacements obtained from foot mounted Pedestrian Dead Reckoning (PDR), and updating the weights of the particles according to the RF measurements. In our experiments the inertial unit was located in the foot of the user, and the RF system consisted in a Radio Frequency Identification (RFID) receiver in the waist, and an Ultra Wide band (UWB) tag in the chest, but the scheme can be used in any sensor configuration. As the UWB measurements have a significant amount of outliers due to non line of sight conditions generated by the position of the tag in the body, received reflections, etc., we propose a new outlier rejection algorithm based on the compatibility of groups of measurements. The fusion was tested evaluating the inclusion of each of the RF systems and varying the number RFID tags used. The proposed method is able to locate a person with less than 2 m of error (for 90 % of the obtained estimations) in the studied trajectory. This particle filter scheme offers robust indoor positioning with 100 % availability and smooth trajectory estimation thanks to the PDR and limited error due to the RF measurements.

A constraint approach for UWB and PDR fusion

Pedestrian Dead-Reckoning (PDR) and Radio Frequency (RF) ranging/positioning are complementary techniques for position estimation but they usually locate different points in the body (RF in the head/hand and PDR in the foot). We propose to fuse the information from both navigation points using a constraint filter with an upper bound in the distance between the estimated positions of both sensors.

Light-matching: A new signal of opportunity for pedestrian indoor navigation

This paper presents a new indoor location concept named Light-matching which uses the perceived gradient in the illumination from unmodified indoor lights to achieve accurate physical location. The proposed method does not require any illumination calibration, just the pre-storage of the position and size of all lights in a building, irrespective of their current on/off state. The Light-matching method also requires the estimation of the relative displacement and orientation change of a person which is done by inertial Pedestrian Dead-Reckoning (PDR). Even from an initially unknown location and orientation, whenever the person passes below a switched-on light spot, the location likelihood is iteratively updated until it potentially converges to an unimodal probability density function. The time to converge to an unimodal position hypothesis depends on the number of lights detected and the asymmetries/irregularities of light distributions. The light-matching technique can be used alone or in cooperation with other signals of opportunity (WiFi, Magnetometers or Map-matching) to obtain a continuous high accuracy indoor localization system. This paper presents the basic description of the light-matching concept, the implementation details using a particle filter, and the evaluation of the method by simulation. The performance of the integrated solution can achieve a localization error lower than 1 m in most of the cases.