Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation

Abstract

In environments where GNSS is unavailable or not useful for positioning, the use of low cost MEMS-based inertial sensors has paved a way to a more cost effective solution. Of particular interest is a foot mounted pedestrian navigation system, where zero velocity updates (ZUPT) are used with the standard strapdown navigation algorithm in a Kalman filter to restrict the error growth of the low cost inertial sensors. However heading drift still remains despite using ZUPT measurements since the heading error is unobservable. External sensors such as magnetometers are normally used to mitigate this problem, but the reliability of such an approach is questionable because of the existence of magnetic disturbances that are often very difficult to predict. Hence there is a need to eliminate the heading drift problem for such a low cost system without relying on external sensors to give a possible stand-alone low cost inertial navigation system. In this paper, a novel and effective algorithm for generating heading measurements from basic knowledge of the orientation of the building in which the pedestrian is walking is proposed to overcome this problem. The effectiveness of this approach is demonstrated through three field trials using only a forward Kalman filter that can work in real-time without any external sensors. This resulted in position accuracy better than 5 m during a 40 minutes walk, about 0 . 1% in position error of the total distance. Due to its simplistic algorithm, this simple yet very effective solution is appealing for a promising future autonomous low cost inertial navigation system. K E Y W O R D S 1. MEMS. 2. INS. 3. Pedestrian Navigation.

Multisensor Navigation Systems: A Remedy for GNSS Vulnerabilities?

“…Also within this category of ad hoc infrastructure it is possible to include information about the environment such as road, street and building plans. This data can often provide valuable constraints on the viable positions of users, on their possible movements and to bound the growth of errors inherent in some dead reckoning sensors [10].…”

“…The most recent implementation of this system integrates image sensors, including 2D and 3D imagery to provide navigation solution [52]. More on pedestrian navigation can be found in [10,53,54].…”

A Novel Zero Velocity Interval Detection Algorithm for Self-Contained Pedestrian Navigation System with Inertial Sensors

“…However, the adaptive ZVI detection algorithm can adjust the thresholds in real time according to the gait frequency, which ensures the proper execution of ZUPT and can correct the navigation error adequately. In addition, the trajectories calculated by the two methods share the common characteristics that the heading drifts gradually with the increase of the walking distance, the reason for which is that ZUPT fails to correct the heading error [21,22], leading to the accumulation of heading error calculated by SINS. Heading drift, another important problem in the PNS, is an issue that will be discussed in a future study.…”

Rotating a Mems Inertial Measurement Unit for a Foot-Mounted Pedestrian Navigation

“…The two trials lasted for about 650 s each. The reference for comparison of error estimation was created based on the second walk, in which a method developed by authors (Abdulrahim et al, 2010;2011) was applied. The example of raw acceleration data from the IMU is plotted in Fig.…”