Bounding drift in pedestrian navigation with inertial measurement unit and human gait model

Yan Cui, Purdue University

Abstract

Today the Global Positioning System (GPS) is widely used in the estimation of position. But the GPS signal is not always available and not precise in some certain case. For pedestrian navigation applications, daily non-GPS systems mainly use inertial measurement units (IMUs), which are more portable and potentially inexpensive. However, the bias or drift in the process of inertial measurement largely limits the precision of position and attitude estimation when integrated directly from accelerations and angular rates. In our study, we have designed an integrated system to analyze the data collected from a MicroStrain Inertial Measurement Unit. For convenience and comfort, the inertial measurement instrument in our experiment is strapped at the middle of the beltline at the back to collect data. This is commonly considered closest to the center of gravity of a person. From frequency domain analysis, the fundamental frequency of walking is clear enough which shows the different stepping periods, and the noise is also easily seen from the spectra. Thus, in the integrated system, a Kalman Filter (KF) is applied first to reduce the noise, especially in the measurement system. The filtered angular rate is then integrated to the three by three rotation matrix, to make an output of walking directions, as well as the attitudes. The mainly auxiliary part to correct the estimation of position is the application of human gait model. From the investigated former results, the dynamic gait for individuals can be expressed clearly as a relationship between stride length and stride frequency. It is a basic characteristic of human walking and running. Here, we perform several series of experiments again to obtain the correct functional form of this relationship. After adding a non-zero constant in the formula instead of a sole power function, the updated curve of simulated result between stride length and stride interval fits the experimental data much better than before. Also we can know the variance of this method by analyzing the confidence interval of this relationship. Having the relationship in human gait model, both walking and running modes are covered by the function. Thus, the estimation of position can be corrected in each step by integrating both the results from integration of acceleration and the gait model. When detecting each step of human walking or running, we use the methodology of detecting the zero-crossing points from the compensated acceleration in vertical direction. After filtering the candidate zero points, which means potentially a moment that the force from the ground balanced by gravity, some false detections need to be filtered again to obtain the satisfied result. The idea here is to use the human gait again. Since human cannot run in a very large frequency, commonly not far beyond 3Hz, we can neglect some zero points if the time interval between them is very small which means oscillations may happen. Having found all the correct points when lift off (LO) and touch down (TD) events, we have produced an impulsive function illustrating two phases (on stance and on flight) in each stride interval very well. We have built up several experiments for human walking and running process and analyze by the integrated system. The typical experiment is implemented by walking back and forth in a straight line, as well as walking around a square closed-loop. In the square loop experiment, a close loop having a total length of 120 meters is set up. The pedestrian walked or ran in clockwise (CW) and counter-clockwise (CCW) then return to origin. Having estimated from the method, our most current result shows that overall drift can be minimized to below 4% and even 1.5% in one direction. Moreover, we are improving the system by investigating on the elimination of initial drift from IMUs by the use of rotation transformation matrix, as well as the application of gyroscope for 3-D model. We have expand the tests to about four hundred meters length and obtain a relatively error around 1%, and about the ∼2% drift of position tracking. At last, as an exploration, a 3-D test including stairs is established and a potential approach of analysing has been introduced. The result from this integrated method indicates that the drift can be limited below some certain percentage. Based on the precise results from our experiments, we can improve this methodology a lot and apply this method to the area of pedestrian walking estimation, soldier and firefighter tracking, and urgent rescue.

Degree

M.S.M.E.

Advisors

Ariyur, Purdue University.

Subject Area

Mechanical engineering

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