Smartphone Pedestrian Navigation
by Foot-IMU Sensor Fusion
Tobias G¨adeke, Johannes Schmid, Marc Zahnlecker, Wilhelm Stork, Klaus D. M¨uller-Glaser
Karlsruhe Institute of Technology (KIT)Institute for Information Processing Technologies (ITIV) Engesserstr. 5, 76131 Karlsruhe, Germany
Email:{tobias.gaedeke, johannes.schmid, marc.zahnlecker, wilhelm.stork, klaus.mueller-glaser}@kit.edu
Abstract—Determining one’s own position by means of a smartphone is an important issue for various applications in the fields of personal navigation or location-based services. Places like large airports, shopping malls or extensive underground parking lots require personal navigation but satellite signals and GPS connection cannot be obtained. Thus, alternative or complementary systems are needed.
In this paper a system concept to integrate a foot-mounted inertial measurement unit (IMU) with an Android smartphone is presented. We developed a prototype to demonstrate and evaluate the implementation of pedestrian strapdown navigation on a smartphone. In addition to many other approaches we also fuse height measurements from a barometric sensor in order to stabilize height estimation over time. A very low-cost single-chip IMU is used to demonstrate applicability of the outlined system concept for potential commercial applications.
In an experimental study we compare the achievable accuracy with a commercially available IMU. The evaluation shows very competitive results on the order of a few percent of traveled distance. Comparing performance, cost and size of the presented IMU the outlined approach carries an enormous potential in the field of indoor pedestrian navigation.
I. INTRODUCTION ANDPROBLEMSTATEMENT
With the more or less ubiquitously available smartphones, location-based services and personal navigation have become important areas of interest for research and industries. Most of the time, positioning of a person carrying a smartphone is achieved by means of the global positioning system (GPS). Then, based on this position, the user is provided with spatially related services such as information during a sightseeing trip or museum visit, or with directions on how to find his destination. However, GPS has the fundamental disadvantage of usually not functioning sufficiently inside buildings. Also ”urban canyons” between skyscrapers in larger cities often pose a problem. Both phenomena result in unavailable or at least inaccurate position information. One possible way to deal with this is to add a redundant localization system that can take over navigation for a period of time. For example automotive GPS systems usually rely on the cars velocity input and map information to bridge the time a car is in a tunnel. For pedestrian localization various approaches exist employing other smartphone sensors. On one hand, a possi-bility is to make use of WiFi signals and to determine one’s position from a comparison of the current WiFi fingerprint with a predetermined database [1], [2]. These fingerprinting
Fig. 1. Implementation of IMU prototype, top and bottom view. Casing with clip on the backside for foot mounting.
systems require, however, a database that has to be established beforehand. The performance of such systems heavily depends on the quality of the database and the WiFi access points in range. On the other hand, completely infrastructure-free systems exist that make use of inertial sensors to perform dead reckoning [3], [4]. These systems usually rely on step detection in combination with step length and direction es-timation to sequentially determine a new position from the previous position. A common extension to this method is the inclusion of map knowledge to limit localization to possible paths within the area of interest [5]. The major issue of these inertial dead reckoning systems is, however, the obtainable accuracy and long term stability due to the sensor drift of the smartphone’s inertial sensors. To cope with this, the usual way is to use a foot mounted inertial measurement unit (IMU) for inertial strapdown navigation allowing to make use of human motion patterns in an adapted system model [6], [7]. However, most previous work did not set high value on the actual system integration and implementation issues but limited their assessment to a PC based evaluation of the concept. Also, most of the time expensive sensor equipment was used that does not allow a mass market adoption.
In this paper, we present a system level concept and pro-totype implementation of a foot mounted IMU that connects to an Android smartphone via Bluetooth. Figure 1 shows the prototype printed circuit board (PCB) and the casing that allows simple fixing to a person’s shoestring with a special clip on the backside of the box. A size of 2∗3 cm2
improve 3D localization accuracy.
The structure of the paper is as follows: in Section II the current state of the art is outlined and relevant previous work is described. In Section III the proposed overall system concept and the hardware concept of the sensor box is presented. In Section IV the system design in terms of the developed hard- and software and algorithmic approach is outlined. A focus has been put on additional fusion of height information from a barometric pressure sensor. After an evaluation of the achievable accuracies and smartphone performance in Section V the results are discussed in Section VI. The paper is concluded and a short outlook on future research directions is given in Section VII.
II. RELATEDWORK A. Foot-mounted Pedestrian Navigation
During the last decade interest in pedestrian inertial naviga-tion has grown with the rapid price decline of micro-electro-mechanical systems (MEMS). Two major research directions can be identified by categorizing the placement of inertial sen-sors: on one hand mounting the sensor on the foot and on the other carrying it at an arbitrary position on the person’s body (e.g. hip, pocket, chest, rucksack). From the algorithmic view, both methods initially started by employing step counting and step length estimation [4]. With further improvements in inertial sensor technology foot mounted methods started using strapdown navigation techniques with zero-velocity updates (ZUPT) [6]. The ZUPT resets the velocity to zero each time the foot comes to rest on the ground and thus limits the error growth of the system to a linear function. Similarly to ZUPT zero angular rate updates (ZARU) were introduced to correct drift from gyro sensors [8]. Other methods have been presented in order to reduce the errors resulting from the integration of the sensor readings. The main focus of these methods lies on yaw angle drift reduction since this error is not observable with inertial sensors only (inclinometer use of accelerometer can correct errors in the roll and pitch angles). Therefore, the inclusion of a compass sensor has been considered extensively along with software algorithms like heuristic drift reduction (HDR) and others [9]–[11]. Another very important process for foot mounted pedestrian navigation is the reliable detection of the stance and swing phase of the foot. Many different methods based on acceleration and angular rates or combinations of them have been studied. It has been shown that most information about the steps phase is carried by the angular rates [12]. When acceleration or acceleration variance is also considered, results can be slightly improved [10].
The errors of current foot mounted approaches are typically on the order of a few percent of the traveled distance. With the mentioned HDR techniques or by means of improved sensor qualities these figures can be lowered down to less than1 % of traveled distance depending on the scenario.
On the other hand, mounting the inertial sensors somewhere else on the body and performing step counting and step length estimation is still the method of choice when processing power
or practicability are important issues [13]. The advantage of this method lies in the possible single device integration in other products like smartphones, cameras, etc. and also allows navigation based on low cost sensors. However, achievable accuracies are typically on the order of 10 % of traveled distance with such approaches. These figures can be further improved by employing activity classification (e.g. walking, running), resulting in higher processing costs [14]. Still, the accuracy performance of foot mounted methods cannot be matched. Especially if the system is to be used for longer runs without any correcting system like GPS, better accuracies are needed. This could be the case for smartphone navigation in large shopping areas, airports or underground parking.
B. Inertial Smartphone Navigation
With the widespread market penetration of smartphones one of the major applications is their use for personal navigation. Many applications provide location based services (LBS) like ”the nearest coffee shop, hotel” etc. All these services are based upon GPS or WiFi connectivity and are not available with high precision in many indoor or underground environ-ments.
The inertial sensors employed in today’s smartphones are typically low cost sensors mainly intended for games and enhanced user experience. An external IMU for the use with a smartphone is considered in [15] but this work focuses on the sensor and hardware selection mostly. Some algorithmic approaches are mentioned but an overall system concept has not yet been worked out. An activity classification based on a smartphone carried on the foot is presented in [16]. In this work localization is mentioned but only evaluated on a very coarse level (detection of floor changes). More promising results in the field of smartphone navigation are obtained by step counting and activity classification algorithms [3]. A new interesting approach is to use the smartphones camera for step detection by comparing a previously acquired image of the foot with the current image of the smartphone’s camera [17]. As a complementary system, GPS and map matching is often used to further improve the localization accuracy [3], [18], [19].
When summarizing the related work, the importance of indoor smartphone localization is evident but the achievable accuracies with inertial approaches are not yet sufficient for navigation in large buildings. On the other hand accuracy of foot mounted inertial navigation has reached a state which allows for very precise localization even for longer runs. Thus, in this paper we try to combine the strengths of easy and low cost sensor deployment with higher accuracies by using foot mounted sensors connected to a smartphone for navigation.
III. SYSTEMCONCEPT
Processing Visualization
Data recording
Possible: wireless sensor network
Fig. 2. IMU and smartphone based pedestrian navigation.
data from the foot as illustrated in Figure 2. For the use with other ultra low power sports devices or for the integration with other on body or external sensors (wireless sensor network, WSN) an additional low power wireless connection is possi-ble. In comparison to state of the art smartphone navigation systems that make use of the smartphone’s internal sensors, the use of an additional sensor box mounted on the foot of the person allows to exploit strapdown inertial navigation. This enables accurate position tracking even under severe indoor conditions. The heterogeneous wireless connectivity allows for the integration of the IMU in various other application scenarios. This also includes other sensor placements on the human body and other technical use cases (e.g. stabilization of robots or autonomous vehicles). One such use case could be person localization in ad-hoc scenarios (e.g. localization of firefighters, patients and doctors) where a wireless network for long term localization is established on-site by means of dead reckoning [13].
A. Choice of Sensors and Hardware Design
Figure 3 gives a design overview of the sensors and peripherals considered in the sensor box. A microcontroller handles the sensor data, communication links and controls the power supply. A low-cost single chip IMU is used for a very small and cheap overall design. To allow for attitude correction a magnetometer is considered as well as a barometer for additional height correction. The communication between IMU and smartphone for the transmission of raw or prepro-cessed data is established either by Bluetooth or an ANT+ interface. The ANT+ standard is widely used in the field of sports electronics and is characterized by its very low power consumption. Some of today’s smartphones are also equipped with ANT+ interfaces (e.g. Sony Ericson Xperia arc). To allow for an integration of the IMU into a ZigBee WSN, the ANT+ chipset can be exchanged by an 802.15.4 chipset as a hardware design alternative.
B. Foot-mounted Inertial Pedestrian Navigation
The basic idea of the strapdown calculation is to determine the position by double integration of the 3D-acceleration signal in the appropriate direction. Therefore, the attitude of the inertial system needs to be known and can be obtained by an integration of the angular rates. These angular rates are
–
6D IMU (MPU6050)
Barometer (MS5611) Bluetooth
(PAN1321)
Microcontroller
(STM32F103) Magnetometer
(HMC5883L) Power supply (LTC3554 step down converter +
Li-Ion battery)
ANT + ZigBee (CC2571) (CC2530)
I²C
Antenna (Chip) UART
UART User interface
(2 Buttons, 2 LEDs)
Micro USB (UART / Charging)
Fig. 3. Hardware concept of the foot mounted sensor box.
measured by a set of orthogonal gyroscopes. The sensors measure in body-frame, which is the coordinate system of the IMU, with respect to the inertial frame. In general, the sensors also measure the earths turn rate, the location depended gravity and transport rate [20]. However, the effects of these influences are usually below the noise level of today’s MEMS sensors. This means that these influences can be neglected for pedestrian navigation.
This leads to a simplified model of the strapdown calcula-tion as shown in the upper part of Figure 4. It is basically reduced to the integration process of angular rates ~ω and double-integration of accelerations~a. Due to the inherent drift introduced by the integration step a stochastic filter is typically used to correct this drift based on an underlying system model. Additionally, for foot mounted inertial pedestrian navigation, ZUPT and ZARU mechanisms correct the velocity and angular rates in the system model. These inputs are pseudo measure-ments as no additional sensors are used, but a step detection algorithm decides whether to apply the correction. For further long term stabilization of the yaw attitude a magnetic sensor is used as a compass usually.
IV. IMPLEMENTATION
A. Hardware
The hardware for the foot-mounted IMU was designed with the intention of a very small, low-cost and low-power device. The developed hardware together with the battery is built into a casing measuring less than14cm3
Fig. 4. Strapdown algorithm with additional correction mechanisms.
Honeywell HMC5883 magnetometer and the MS5611 barom-eter from Measurement Specialities. The MS5611 allows for very precise pressure measurements which result in a height resolution of about 15 cm according to the datasheet. This resolution allows for the detection of single stairs, but is also useful in other contexts like human activity classification (e.g. sitting, standing with hip mounted IMU). The communication link between the hardware and the smartphone is realized with a Bluetooth module as this is probably the most widespread communication standard in smartphones. Additionally, another wireless low power and lower data rate communication module is implemented for increased interoperability with various other systems. This can be either a Texas Instruments ANT+ chipset (CC2571) or an 802.15.4 chipset (CC2530) for the use in a ZigBee network. These chipsets share a very similar footprint and are implemented as a design alternative. Beside the sensors and communication the hardware provides a user interface in the form of two buttons and LEDs. The power supply is realized with a small lithium-ion battery which can be charged by a micro-USB connector. A voltage converter supplies the sensors and digital components independently so that influences on the measured data are kept to a minimum.
B. Strapdown Mechanization
The strapdown mechanization in our work is based on the implementations presented in [6] and [10]. We did not consider any HDR algorithms from [10] because the assumption of mostly straight or rectangular paths does not hold in arbitrary scenarios. The lower part in Figure 4 shows the Error State Space Kalman filter and the implemented correction mech-anisms based on step detection and the additional sensors. The systems state vector δ~x contains the errors of attitude
δ ~ϕ, angular ratesδ~ω, positionδ~p, velocityδ~vand acceleration
δ~a. It turned out in previous studies that small errors in the measured data can lead to significant errors in z-direction for longer runs. Thus, a pressure sensor (barometer) is integrated to correct height measurements additionally. Obtaining height from atmospheric pressure measurements is a fairly complex procedure as it depends on various unknown parameters in the atmosphere. That means that the pressure greatly varies with
changing weather conditions. Obtaining an absolute height over sea level is not accurate enough under these circum-stances. However, differential small pressure changes are very accurate and allow for a sub-meter resolution. Therefore, some assumptions on the atmospheric parameters are made commonly. Such assumptions consider an average standard atmosphere in which the pressure is only dependent from the current height. This relation is described by the barometric formula which is given in (1).
p=p0·
1−L·h
T0
gR·M·L
(1)
The barometric formula describes the pressurepin dependence of the current heighth. The other parameters can be assumed constant for standard atmospheric conditions and the values are given in Table I [21].
In the basic Kalman filter implementation the position error in z-direction is predicted from the integration of the turn-rate and acceleration error. Thus, it drifts with erroneous measurements and the error growth is limited by the ZUPT to a linear increase. The differential height information is considered as an additional measurement input to the Kalman filter. It is thus fused in the correction step of the filter. In our implementation we fuse height updates only in stance phases of the foot. This reduces influences from high frequency and other noise in the signal. The difference from strapdown height estimation hk and hk−1 between successive steps and the measured height difference from the barometer hk,dif f are then fused as a height error in the correction step of the Kalman filter. The measurement matrix H is extended with an additional line corresponding to the additional elementmh in the measurement vector m.
Hpz = [01×3 01×3 [001] 01×3 01×3] (2)
mh= (hk−hk−1)−hk,dif f (3)
C. Sensor Calibration
One very important issue in the context of inertial navigation is the calibration of the sensor system. Often, a major problem is the lack of a high quality reference calibration system. This means that other calibration methods need to be considered. These have to be robust under difficult conditions and carry the potential of an in-field calibration. For the accelerometer, one calibration method is to solve a set of linear equations
TABLE I
CONSTANTS FOR STANDARD ATMOSPHERIC CONDITIONS.
Symbol [Unit] Value Description
p0[hP a] 1013.25 Sea level pressure L[K/m] 0.0065 Temperature lapse rate
T0[K] 288.15 Sea level standard temperature M [kg/mol] 0.0289644 Molar mass of dry air
R[J/(mol·K)] 8.31447 Universal gas constant
g[m/s2
Fig. 5. Visualization of normalized calibration data of magnetic sensor.
from a set of different attitudes [22]. Another method is to use maximum likelihood estimation. We implemented the method described in [22] for the calibration of the accelerometers. For the magnetometer we adopted the procedure presented in [23]. The parameters of an ellipsoid are estimated based on multiple measurement points vxi, vyi, vzi from different directions. Such an ellipsoid is described by (4) with the six unknown parametersvox, voy, voz, bkx, bky, bkz.
vxi−vox
bkx
2
+
vyi−voy
bky
2
+
vzi−voz
bkx
2
= 1 (4)
These parameters can be determined by at least six inde-pendent equations from measurements. Figure 5 shows the measurement points or the trajectory of the described calibra-tion procedure in 3D space. Based on these measurements the algorithm tries to fit an ideal ellipsoid. This gives the 3D center point and the semi-axis length of the ellipsoid. For calibration this means that the obtained ellipsoids center is the estimated sensor bias and the semi-axis length represents the scale factor.
D. Smartphone Navigation
The smartphone has two major tasks in our setup: First, the smartphone is used as a navigation device visualizing the user’s path on Google Maps. Therefore, an intuitive user interface and position calculation is provided by the smart-phone. Second, it is possible for further detailed studies to collect raw measurements from the IMU on the phones SD-card. Typically, data collection for later offline processing is a cumbersome but necessary task for algorithm development and optimization. Often, laptop computers and a lot of equipment has been used for this task. With today’s smartphones and their large processing and memory capabilities data collection with mobile devices proofs to be a very handy method. Walking with a smartphone for data collection also allows for more accurate walking patterns as no other equipment carried by the person influences their walking behavior.
Fig. 6. Trajectory visualization on Android smartphone in Google Maps.
E. Software Design
Fig. 7. Indoor trajectory over two floors in typical office building recorded with our own IMU compared to the Xsens MTi-G IMU.
V. EXPERIMENTALEVALUATION
For the purpose of a performance evaluation we compared the results obtained with our IMU with the widely used Xsens MTi-G IMU. This IMU from Xsens is often considered low-cost but the price is still not yet in the consumer range. Considering the size of more than 100 cm3
and a weight of 68git is useful for research applications but not applicable for real world deployment. Figure 7 shows an example trajectory from our experimental evaluation. The trajectory was recorded inside our institute building which is considered a typical office building. Both IMUs, MTi-G and our own (ITIV BT IMU) were fixed on the same foot of the walking person and sent data with a rate of100Hz. The trajectory started on the second floor and the test person took the stairs down to the first floor, walked around and went back upstairs to the starting point.
A. Accuracy Analysis
In Figure 7 it can be seen, that the position estimation of both IMUs exhibits a drift over the distance. Comparing the resulting errors of both systems shows that they are on the order of a few percent of the traveled distance and very comparable. On an absolute scale, the error adds up to2.2m
(own ITIV BT IMU) respectively1.8m(Xsens MTi-G) for a total traveled distance of about82m.
When taking a closer look on the 3D performance evaluation in Figure 8 it can be seen that the position estimation in z-direction drifts over time. The figure shows the estimated height over time starting with an initial value of zero on the second floor. Especially, the estimation from the MTi-G IMU drifts away towards the end of the trajectory as no additional height information is fused. When fusing height measurements from the barometer the height is corrected to a certain degree as can be seen in the red graph in Figure 8. It can also be seen that at the beginning of the run a strong drift occurs
Fig. 8. 3D performance evaluation to analyze height information fusion from barometer.
for both estimations. We assume this drift occurs from initial values of the system as no dedicated initialization procedure is considered. Here, this error adds up to about 0.5 m. After this drift, it can be seen that the test person walked down to the first floor resulting in a height change of about3mwhich corresponds well to the story height of the building. For the height estimation based on the Xsens data it can be seen that a strong drift occurs still after initialization over the whole run. This adds up to an additional error of about 0.5 meven after approximately 100 s. The developed information processing with our IMU still exhibits the drift from initialization but the height after100scorresponds well to the height at about10s
from the starting point of the trajectory.
B. Real-time Performance Evaluation on a Smartphone
Considering that today most smartphones are equipped with fast processors and comparatively large memory resources it is likely that the smartphone handles the strapdown calculation in real time. We evaluated the performance on a Sony Ericsson Xperia arc with a 1 GHz Qualcomm Snapdragon processor and 512 M B RAM. Figure 9 shows the processor load for the strapdown calculation on the Sony Ericson Xperia arc. On the left side the phone carries out the strapdown calculation only and on the right side the trajectory is visualized on Google maps. It can be seen that less than half of the time is used for navigation. When the visualization is not used, the processor load decreases from 40 % down to 37 %. This shows that strapdown pedestrian navigation on today’s smartphones can be carried out even while other processes run in the background. Operating on a lower processor load also implies longer battery lifetimes of the phone. With the upcoming new generations of smartphones employing even more capable processors (dual and quad cores and higher clock rates) this figure will be brought down even more.
VI. DISCUSSION
Fig. 9. Real-time processing performance.
application provides a user interface for configuration of the IMU and position visualization of the trajectory walked. State of the art smartphone processors are capable of handling the strapdown calculations efficiently while still providing enough processing capabilities for other tasks.
To allow for 3D or at least 2.5D navigation (determination of discrete floors) a barometer is a very useful supplement. Determining changes between floors from the evaluation of the atmospheric pressure is fairly easy. However, long-term effects on the atmospheric pressure, e.g., from weather changes do not allow for an absolute height measurement over sea level. This information has to be obtained from another data source, e.g., correction from GPS whenever available (outdoors). For further improvements or when no barometer is available we propose to introduce a Zero Height Update which makes the assumption of planar floors within a building. The drawback of this method is that some floor changes will be hard to be recognized, e.g., if the person moves too slowly or over a ramp.
The outlined practical tests show feasibility and applicability to indoor navigation with accuracy on the order of a few percent of traveled distance making the approach sufficient for a wide range of applications. Especially, when looking into potential indoor location based services where precise indoor position information is necessary, the presented concept carries an enormous potential. Accuracy of foot-mounted strapdown navigation cannot be outperformed by step detection with the smartphone’s internal sensors. Considering the low costs of the sensory equipment used for the presented evaluation mass market adoption becomes even more likely. A drawback of the system is, however, that the user needs a small sensor box on the foot in addition to the smartphone.
However, the results presented in this paper can serve as a starting point only and therefore open a wide range of possible further research topics in the various details of the systems components. Here, for example, the integration and possibilities of indoor maps generation and matching techniques or the combination with sparsely available WiFi access points carry a huge potential.
VII. CONCLUSION ANDFUTUREWORK
A. Conclusion
In this paper we proposed a foot-mounted IMU combined with a smartphone for pedestrian navigation in indoor sce-narios. The good performance in terms of accuracy of foot-mounted approaches is combined with the ease of use and visualization capabilities of today’s smartphones. Therefore, we presented a hardware design which is based on a very low cost and low power single-chip IMU. A custom casing with a volume of less than 14 cm3
was designed to fix the IMU on the shoestring of a person’s foot. Comparing the performance, cost and size of the developed IMU sensor-box it is one of the smallest ever developed wireless IMUs to the best of our knowledge. Also, in terms of power consumption the developed IMU is very competitive by employing an intelligent power management and sensors with low power requirements. On the algorithmic side, a basic strapdown algorithm is implemented and further extended with sensor fusion capabilities of relative height information measured with a barometer. To verify real-world applicability of our presented approach we carried out an experimental study in a typical office environment and analyzed real-time requirements and capabilities. It has been shown that the obtainable local-ization accuracies are comparable with commercially available IMUs. Especially, by fusing measurements from an additional barometer the cumulative errors in z-direction of the strapdown navigation can be corrected. Thus, the overall performance of the system is increased. We also showed the feasibility and performance of a smartphone performing the computational complex strapdown calculation with a processor load lower than 50 %. For navigation purposes the visualization of the trajectory has been integrated into the Google Maps interface. Additionally, our presented hardware setup allows for raw IMU data collection on the smartphone which also enables easy evaluation of other IMU positions, algorithms and sensor fusion approaches.
B. Future Work
This paper presented a first study of the concept of foot-mounted inertial sensors combined with a smartphone. The experimental evaluation was done on a relatively small data set so far. Thus, further evaluation of the system under different environmental conditions is planned for the next future. Along with a more comprehensive evaluation of the system further algorithmic aspects will be covered to gain more insight in the error behavior at initialization and possible correction mechanisms will be developed.
A promising research will also be map building and match-ing especially with the availability of indoor maps provided by Google or OpenStreetMap. In this context fusion of further information sources (e.g. localization based on WiFi or Near Field Communication) carries a huge potential.
higher processor load of the microcontroller in the sensor box. Therefore, it has to be analyzed how to achieve further optimizations, e.g., by efficient fixed point implementation of the algorithms.
ACKNOWLEDGMENT
This work was supported by the German Research Foun-dation (DFG) within the Research Training Group GRK 1194 ”Self-organizing Sensor-Actuator Networks”. We would like to thank the DFG for supporting our work.
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