ROTATING IMU FOR PEDESTRIAN NAVIGATION

Transcription

1 ROTATING IMU FOR PEDESTRIAN NAVIGATION ABSTRACT Khairi Abdulrahim Faculty of Science and Technology Universiti Sains Islam Malaysia (USIM) Malaysia A pedestrian navigation system using a low-cost inertial sensor can be used indoors when useful GPS signals cannot be used reliably. To slow down a fast accumulating error of the low-cost inertial sensor, a concept of Zero Velocity Update (ZUPT) is often used. However, there remains a heading drift error which cannot be completely estimated by the use of ZUPT alone. This results in a huge positioning error when navigating only in a few seconds using only the low-cost inertial sensor. Therefore this paper discusses an innovative concept to improve the heading drift error. An innovative technique of rotating the sensor on a single axis is proposed and explained mathematically. Then a walking simulation is performed and its result is presented. It will be shown that the technique reduces the heading drift error for such a lowcost pedestrian navigation system. KEYWORDS: ZUPT, drift, inertial sensor, rotating I. INTRODUCTION Inertial sensor has long been used as an interim solution to a standard GPS positioning system during GPS outages. During this period, the inertial sensor or commonly know n as Inertial Measurement Unit (IMU) solution is used to bridge the positioning solution from GPS during the outage. For example in vehicle navigation system, when a car navigates into a tunnel or under the bridge, the GPS is often unavailable and if it is, it is not reliable due to problems such as multipath. Therefore, IMU is used to provide a position solution from the start of GPS outage until the end of GPS outage to navigate the vehicle. Its initial position and attitude are usually taken from GPS initial position and attitude just prior to the outage. On its own, however, a low-cost IMU is still not capable to provide a reliable and an accurate position solution for navigation purpose. Due to the integration of IMU data to output position and attitude solution, slightest error will accumulate into a bigger solution error and eventually, it is so big that the solution is totally useless for navigation. The time for error accumulation depends on how good the IMU is. Low cost IMUs will therefore accumulate error faster because it typically suffers from large sensor errors such as biases and scale factor errors. One successful recent idea to use a low-cost IMU for pedestrian navigation is to attach the IMU on foot/shoe [1]. This is to take advantage of Zero Velocity Update (ZUPT) 44 Chris Hide, Terry Moore, Chris Hill Nottingham Geospatial Institute The University of Nottingham United Kingdom [2-5] available to estimate the IMU errors. During walking, the foot has to be briefly stationary while it is on the ground, thus activating the ZUPT in the error estimation process. Furthermore, if the ZUPT measurements are used in a Kalman filter [1], they can not only be used to correct the user s velocity, but also help restricting the position and attitude errors and estimate the sensor bias errors. The frequent use of ZUPT measurements consistently bounds many of the errors and as a result, even relatively low cost sensors can provide useful navigation performance. However, there remains a problem with ZUPT if it is to be used alone heading drift during navigation. This is because it is not possible to estimate heading error using only ZUPT measurements. This causes a significant issue since there then becomes a requirement to use heading measurements from external sensors. Typically magnetometers are used; however their measurements are often unreliable when navigating in environments such as indoors where there are significant magnetic disturbances. Instead, it is desirable to use measurements from other systems or methods to control heading drift. In this paper, an innovative technique to control the heading drift is discussed by continuously rotating the lowcost IMU on its pitch axis called Rotating IMU (R IMU) afterwards. The idea is that if it were possible to physically flip the IMU at regular intervals around certain axis such that the other axes are flipped, the errors on other axes would cancel as it would have a positive and negative effect along the path every time when the IMU is flipped. The concept of rotating IMU to reduce IMU errors was introduced by [6], where he described and computed the mathematical equations relating to rotating gyros about its azimuth axis. Two terminologies were proposed by [7] for this concept; carouselling and indexing. The former was defined as rotating the IMU with continuous rotation in multiple orientations, while the latter was defined as rotating the IMU with discrete known rotation. This paper, however, uses neither carouselling because of the single rotati on axis nor indexing because of the ambiguity of the rotation rate to the user. The effect of rotating the IMU on the estimation of its heading error is investigated using simulated data in a Kalman filter environment. GNSS measurements are used to initialize the position. A walking scenario is simulated with single axis rotations and the results are discussed afterwards.

2 II. KALMAN FILTER AND ZERO VELOCITY UPDATE Normal strapdown navigation equation such as in [8] are used to resolve and update the position and attitude of the IMU while Kalman filter is used as an estimation filter. The Kalman filter is widely used for optimal state estimation and details about it can be found for example in [8-12]. For this paper, the states vector that was used is shown below: where r is the vector of latitude, longitude and height errors; is the vector of navigation frame velocity errors; is the vector of attitude errors (roll, pitch and heading); g is the vector of gyro bias errors and a is the vector of accelerometer bias errors. The filter is used in feedback form, which means that errors calculated from Kalman filter are used to correct the inertial sensor measurements and navigation parameters. accelerometer bias errors. The filter is used in feedback form, which means that errors calculated from Kalman filter are used to correct the inertial sensor measurements and navigation parameters. The ZUPT information is useful because the system will have an external update to estimate IMU errors. Since low cost IMUs are not capable of measuring Earth rotation and also navigation is done with a small velocity, the relationship between velocity errors and attitude errors is shown as: proportional to the change in roll and pitch errors. By improving the velocity estimation through ZUPT, it means that roll and pitch errors are improved as well but not the heading error. As a result, heading error is still unobservable if ZUPT is the only measurement used to update Kalman filter. Accumulation of this error therefore results in a massive heading drift of the IMU. In the result section, it will be shown how by rotating the IMU on its pitch axis is capable of reducing this massive heading drift. III. TRIAL A straight walking trajectory was chosen simply because it would be easier to analyze! the heading drift error and was constructed for simulated such that the pitch was rotated from 0 0 to back and forth. This is visible in Fig. 1 (top), where the acceleration on the x- and z-axes resembles a sinusoidal plot. The spikes modulated onto the sinusoidal plot are the simulated walking velocity. The data was processed and analyzed in a Kalman Filter (KF) environment using the POINT software [13]. The sinusoidal plot, for example z-axis acceleration, increased sinusoidally from about -1 g (pointing down) during levelled platform, to about 1 g in the middle of the rotation (pointing up), and back to about! -1 g at the end of the first rotation (pointing down again) at 60 (6 0 /s x 60 s = ). Then it rotated back from about -1 g to 1 g and finished at about -1 g at the end of the second rotation at 120 s. Acceleration on the y-axis did not undergo a flipping motion as the y-axis was the RIMU rotation axis, therefore it did not measure any g s before the walking trial began at 60 s. Fig. 1 (bottom) shows the RIMU rate used, rotating about the y-axis, with a mean of 6 0 /s. Note that because the angular rate for the other two axes are close to zero, the simulated walking trial is not as realistic as it should have been for a walking pedestrian, as it only simulates the horizontal acceleration. It is thought nevertheless to be sufficient to understand the effect of the RIMU when used for the low-cost PNS. During ZUPT, the specific forces f n and fe are essentially zero and specific force f d in downward direction is approximately close to negative gravity constant. Attitude errors and force errors are modelled as directly correlated with gyro biases and accelerometer biases respectively. Therefore from equation (2) and (3), it can be seen that the velocity errors and force errors are modelled as directly correlated with gyro biases and accelerometer biases respectively. Therefore from equation (2) and (3), it can be seen that the velocity errors in North and East directions are only related to roll and picth errors via a specific force f d in downwards direction. This means that during ZUPT period, the dynamic change in horizontal acceleration is 45

3 Fig. 1: Simulated inertial sensor output in b-frame for acceleration (top), and angular rate (bottom) IV. RESULTS A. Analysis of heading estimation Fig. 2 shows the estimated heading for the RIMU (blue dots) and non-rimu (green dots) with two reference headings of 45 0 (reference 1) and apart (reference 2). Fig. 2 (bottom) magnifies Fig. 2 (top) for clearer view. The reference heading of 45 0 (solid red line) was the true heading, while the second reference heading (dashed red line) was the heading when the z-axis was flipped because of the RIMU. It now appears, apart from the sudden change of heading quadrant for the RIMU resulting from the flipping of z- axis, the RIMU heading is now bounded and follows closely the reference heading. In contrast, the heading for non-rimu appears to be growing. The growth appears to be linear and it was actually resulting from the simulated constant bias. In reality, the heading drift for non-rimu may be non-linear because of variations in bias. Therefore, the simulated heading output of non-rimu is considered valid because it was meant to show that it was drifting, which is a typical output of a low-cost IMU. Fig. 2: Comparison of heading angle for RIMU and non- RIMU (top) and the magnified comparison (bottom). B. Heading initialization issue If the IMU was not rotating, the heading would be drifting (discussed previously). However, when rotating the IMU, the initial heading before the modelled z-axis gyro bias is observable is also drifting (assuming that that the modelled gyro bias is the only error affecting the gyro z-axis measurement). This is plotted in Fig. 3 (top). For example, at the beginning of the plot (at 0 s < t < 15 s), the heading was still drifting (increasing). For this trial, the z-axis gyro bias was successfully resolved at about t = 100 s. Therefore, the drift in heading for the RIMU now appears to be reduced only after this time, shown in Fig. 3 (bottom) as between 240 s < t < 260 s and 290 s < t < 310 s, and for the rest of the simulation trajectory. Therefore, an issue to consider when using the RIMU technique is the initialization of the heading for the lowest-cost IMU (or uncalibrated IMU). Standard coarse alignment for a strapdown IMU would be to set the IMU initial heading during coarse alignment, based on information from for example the GPS heading or magnetometer. The same approach however cannot be applied to the RIMU because of the reason described next. Suppose the IMU horizontal alignment is performed for 1 s (heading is initialized manually), and the walk is performed after 20 s. Suppose also the true initial heading is 45 0 and the low-cost RIMU drifts at a rate of 10 0 /s. If the RIMU heading is manually set during heading alignment to 45 0, the actual initial heading at the start of the walk (at 20 s) for the RIMU would be different. This is because the RIMU heading will still drift because the z- axis gyro bias will take some time to be observed by the RIMU. After 20 s, the IMU heading would have been off from the true heading. This will cause the actual initial heading (used for the IMU mechanization) to be 245 0, which is wrong. This subsequently will affect the position computation (although the heading drift after this period would be reduced because of the RIMU effect). 46

4 Nevertheless, this case assumed that the z-axis gyro bias was zeroed during initialization for simulation purpose. Usually in practice, gyro biases were initialized with its average values taken during stationary alignment. This therefore gives some information to the KF when estimating the biases and the heading errors. The only probable dilemma is if the initialized gyro bias value (using its average values during alignment) may not represent the correct estimation of the true values (the uncertainty is too large). For example, the biases might change so much and very rapidly, subsequently affecting the estimation of the heading error even when initialized properly. If this is the case, then the heading initialization issue must be addressed appropriately. C. Comparison of position solution Fig. 4 shows the computed relative position solution for the RIMU (blue), without RIMU (green) and the simulated reference (red). It is clearly shown that without RIMU, the position solution drifted quite significantly. When the RIMU was used, the position solution did not drift as much as without RIMU because the heading error was now observable. Nonetheless, as discussed in the previous section, it appears that because the z-axis gyro bias was not resolved until after 100 s, the RIMU position has drifted slightly during the first 100 s. This, however, was not as bad as it might have been, because of the low drift rate used for the simulation of heading (approximately /s, based on the z-axis gyro bias reference). Consider this. Heading was initialized manually as 45 0 during coarse alignment for this simulation. It appears that because the drift rate was at about /s, the heading after 100 s into simulation would have been about 35 0 off from the reference (because the z-axis gyro bias was not resolved until after 100 s). However, note that after 15 s or 90 0 rotation (see Fig. 2), the flipping of the z-axis has caused the heading to change its value. Heading is now decreasing after this period (heading is drifting in the opposite direction because of the flipping of the axis). After another flipping at 45 s, the heading was increasing again because heading is drifting again in the opposite direction (note that the heading is still drifting at 0 s < t < 100 s because the z-axis gyro bias has not yet resolved). The drift nevertheless after 15 s and before 100 s was considered to average out thanks to this increasing and decreasing in the heading. Therefore what remains is the drift that had happened for the first 15 s of the simulation as shown in Fig. 3, which shows the drift in the initial RIMU heading. This is because the unresolved z-axis gyro bias and the flipping of the axis had not yet happened. As shown in Fig. 3 (top), the initial heading has drifted about 5 0 from the reference, which agrees theoretically (i.e /s 15 s = ). This therefore signifies the issue of heading initialization as discussed previously. Fig. 3: The RIMU heading after resolving the error in gyro Down-axis 47 Fig. 4: The comparison of the RIMU position with the reference and without-rimu This does, though, highlight the advantage of the RIMU over a non-rimu, where the RIMU significantly mitigated heading drift error. When the RIMU was not used, the position trajectory drifted quite significantly against the reference. In contrast, when the RIMU was used, the

5 position trajectory improved and the heading drift was no longer visible, apart from the initial drift in heading resulting from the issue discussed in the previous paragraph. V. CONCLUSION This paper describes an investigation made on the heading drift error of a low-cost IMU after introducing a rotation around IMU pitch axis for a foot mounted pedestrian navigation system. A rotating approch on a single axis was then simulated around pitch axis for walking scenario. Using rotating approach, it was shown that the heading drift of a low-cost IMU was bounded. Although the drift error range is still big, it does at least show the effective result of flipping the axis of the low-cost IMU. A comparison with normal approach clearly shows the difference in heading drift where heading drift for normal approach grows unbounded. This gives an impression that the use of rotation on low-cost pedestrian navigation system is possible, and therefore may realize a high accuracy but low-cost navigation system, although more trials and analysis are worth to be performed to investigate more the advantages and disadvantages of this technique. ACKNOWLEDGMENT Thank you to Ministry of Higher Education of Malaysia (MOHE) and Universiti Sains Islam Malaysia (USIM) for partly sponsoring this research REFERENCES 1) E. Foxlin, "Pedestrian tracking with shoe-mounted inertial sensors," Computer Graphics and Applications, IEEE, vol. 25, pp , Navigation, vol. 61, pp , ) Geller, E. S., Inertial system platform rotation. IEEE Transactions on Aerospace and Electronic Systems, AES-4, ) Curey, R. K., Ash, M. E., Thielman, L. O. & Barker, C. H., Proposed IEEE inertial systems terminology standard and other inertial sensor standards. In Proceedings of Position, Location and Navigation Symposium (PLANS 2004). California, USA, April ) D. H. Titterton and J. L. Weston, Strapdown Inertial Navigation Technology, 2nd ed.: The IET, ) Hide, C., Integration of GPS and low cost INS measurements. Ph.D. The University of Nottingham. 10) Groves, P. D., Principles of GNSS, inertial, and multi-sensor Integrated Navigation Systems. Boston: Artech House. 11) Godha, S. & Lachapelle, G., Foot mounted inertial system for pedestrian navigation. Measurement Science and Technology, 19, ) Abdulrahim, K., Hide, C., Moore, T., & Hill, C. (2011). Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation. Journal Of Navigation, 64(2), ) Hide, C., Algorithm documentation for POINT software, Geospatial Research Centre, New Zealand. 2) J. B. Bancroft, G. Lachapelle, M.E. Cannon and M.G. Petovello, "Twin IMU-HSGPS Integration for Pedestrian Navigation," in Proceedings of ION GNSS, Savannah, ) D. A. Grejner-Brzezinska, Y. Yi, and C. K. Toth, "Bridging GPS gaps in urban canyons: The benefits of ZUPTs," Journal of Navigation, vol. 48, pp , ) D. A. Grejner-Brzezinska, C. Toth, S. Moafipoor, Y. Jwa, and J. Kwon, "Multi-sensor personal navigator supported by human motion dynamics model," in Proceedings of 3rd IAG Symposium on Geodesy Baden, ) P. Aggarwal, Z. Syed, X. Niu, and N. El-Sheimy, "A Standard Testing and Calibration Procedure for Low Cost MEMS Inertial Sensors and Units," Journal of 48

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