[Summary Week4] Pedestrian tracking with Shoe Mounted Inertial sensors – Eric Foxlin

In this paper, the author describes NavShoe, a novel shoe-mounted inertial sensor system that is capable of tracking 6-DOF in an un-instrumented environment. The proposed system builds on the prevalent MEMS gyroscopes that report 3-DOF (orientation in space) by also providing highly accurate position estimates.

Un-instrumented inertial tracking is made feasible by the identification of alternating stationary and moving stride phases of human locomotion. The inertial sensors, being shoe-mounted, can detect this pattern. The stationary phases are used to apply zero-velocity updates (ZUPTs), to ensure that the inertial integrator’s velocity is set to zero intermittently. Another novel aspect is the feeding of ZUPTs to an Extended Kalhman Filter(EKF) to correct velocity errors, leading to error accumulation that is linear in strides as opposed to cubic. The ZUPT feedback to the EKF is able to correct for all drifts, barring yaw drift. The author minimizes this heading drift using corrective measurements obtained from a magnetic compass.

To realize the proposed system, the author employs triaxialrate-gyroscopes, accelerometers and magnetometers. To drive real-world consumption, the system is also provided with a RF transceiver that receives sensor information which is then processed by a host of stochastic prediction/correlation algorithms which yield the corrected position and orientation.

The author describes how compensating for magnetic interference was a major problem for him to solve, and discusses a scheme that allows the system to be calibrated initially and thus be subsequently free of magnetic-field distortions created by metal in shoe-soles. He also elaborates on the technique that he uses for modeling the magnetic compass output that accounts for noise as well as spatial magnetic field variations due to declination and deviation.

Finally, the author present his results of the tracking system in an indoor and an outdoor setting. The results are quite impressive, and there is only a 0.3% heading error in both settings. The author also describes a possible hybrid system that could incorporate GPS data as a position-correction term, thus making the system viable even for long treks. As extensions to his work, the author describes the possibility of using additional spatial information for improving accuracy (much akin to the GeoSpots of Argon), and also the potential to use the system for driving a HMD.


NavShoe is a position tracking system, small enough to truck into shoe laces. It is much more accurate than head mounted inertia orientation tracker because the higher foot acceleration enables the use of transfer alignment from GPS. InterSense InertiaCube3 was used to get multisensory data such as gyro, accelerometer, and magnetometer.

Calibration of sensors is conducted after inserting the sensors in shoelaces. This calibration is done by wiggling the sensorized shoe for 30 seconds. When the user takes first steps, the calibration mode terminates and the system enters navigation mode. An algorithm was developed to detect the stance phase of walking and apply zero-velocity updates to the Extended Kalman Filter.

One indoor and outdoor experiment was conducted to test the system. In the indoor experiment, a user walked through a typical wood-frame house, starting at position (0,0,0) and moving along a pre-defined path and then coming back to the origin. The results show 0.3 percent error in the indoor environment. In an outdoor experiment, a user walked along a closed loop and backed to its origin, indicating the same error rate, 0.3 percent.

Week 4 – NavShoe

The paper highlights the problems faced by modern day navigation and tracking systems. They then introduce NavShoe a device that provides accurate tracking as well as orientation feedback. The major hurdle faced by such trackers is to maintain the accuracy with both orientation and position. Position tracking is based on inertial sensing but it is impossible to track a position for more than a few second based only on the inertial sensing. The main problem caused is due to the drift which causes a slowly growing tilt error. This is cubically related to position error which brings up the point that even a slight tilt error varies cubically for position. NavShoe works on the principle which brings down this cubic relation to a linear one. NavShoe decreases the sampling time and uses zero velocity updates to achieve this functionality. The authors propose several new methods. In one of their methods they talk about making use of commercial grade MEMS (microelectronic mechanical system) gyros and carefully calibrated and modeled solid state magnetic compass to achieve high accuracy results. They also state that the method is also cost effective one when compared to others. They further discuss on the implementation of NavShoe. The hardware basically consists of an inertia cube in the foot wired to a mounted antenna on a baseball cap; this was also one of the basic hurdles of NavShoe. It was overcome by getting rid of the cables and making use of RF transceivers. NavShoe uses standard strapdown INS (inertial navigation system). The IMU (Inertial Measurement Unit) reports the changes in velocity and position which are then used to update velocity and position states of the INS. After this a drift correction is performed. After each estimation cycle, filter delivers error estimates to the INS and clears the error vectors back to 0. Despite being fairly accurate NavShoe still came up with errors due the metal component present in the shoes. To overcome this compass calibration was performed once NavShoe was installed and it remained accurate till it was not removed from the shoe. The authors then show their test results which confirm the accuracy of NavShoe. The tests were performed both indoors and outdoors. The error indoors was found to be 0.06% while outdoor it was 0.3%.

week 4 summary Aurelien Bonnafont

The current GPSs cannot track the real position without prepare the environment with markers or instrument. The NavShoe device can overcome this problem by providing an inertial sensing.

The NavShoe allows to correct the velocity error by applying a zero velocity into an EKF navigation error corrector when a foot is in stance phase. A ZUPT alone looses accuracy because of the heading drift. This heading can be avoided with a much higher performance gyro, a heading correcting measurement from a magnetic compass, or a GPS correction.

The Compass measurement has some difficulty to give accurate results and this is due to the presence of metals in the shoe’s sole. This problem is overcome by a calibration of the sensor before starting. A preprocessed yaw measurement is necessary before each stance phase because the vector magnetic field is insensitive to the magnetic fields inclination.

Outdoor and indoor without any aided experiment provide relatively good results especially concerning the elevation and this can help to localize exactly the person in a building, like a firefighter. To fix the errors we can use a GPS to correct the yaw drift, indeed test with gps are accurate, even if it still some little errors wich can be generated by gps anomalies, but cannot be use inside building. There exist other way to fix it, like using tracker, map correlation techniques, overhead cameras or covariance intersection techniques. A good MR application which require precise head positioning necessit an head tracking with computer vision or hybrid vision and the naveshoe could help with an approximation of the initial position.

NaveShoe cannot replace head tracking in MR system but can be fully implemented to enhance the accuracy of this systems in the future.