Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU

Jiménez, Antonio R.; Seco, Fernando; Prieto, José Carlos Sánchez; Guevara, J.

doi:10.1109/wpnc.2010.5649300

Cited by 401 publications

(322 citation statements)

References 3 publications

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“…b) PDR trajectory in the third floor of the building above (an ideal floor for HDE navigation). In green color, the INS-based IEZ method (no magnetometers) [4]. The HDE solution ( =45 o ) is represented in magenta color, with black circles at the detected steps where the HDE correction is performed.…”

Section: B Hde Limitationsmentioning

confidence: 99%

“…Each measurement contains the horizontal Yaw angle of the sensor's X-axis with respect to the North (see Figure 5). This Yaw angle is computed from the magnetometer (Yawmag) as in [4]. Also, we have a reference Yaw angle or ground truth (Yaw ), which is real deduced automatically from our PDR algorithms with the HDE heuristic enabled in order to keep the trajectory well aligned with respect to the dominant principal directions of the building [11].…”

Section: B Benefits: Finding Useful Information In Magnetic Ya Wmentioning

confidence: 99%

“…We use the foot-mounted IMU-based PDR algorithm proposed by Foxlin [3] and later refined by Jime´nez et al [4]. This PDR method was termed as IEZ [4]; it stands for INS-EKF-ZUPT i.e.…”

Section: A the Iez Framework For Pedestrian Navigationmentioning

confidence: 99%

“…This INS mechanization is corrected by a complementary Kalman filter (see [3] and [4] for INSbased PDR implementation details). The heading difference between the dominant directions of the building and that of the user's stride (heading error) is fed as a measurement into the Kalman filter.…”

Section: Introductionmentioning

confidence: 99%

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Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings

Jiménez

Seco

Zampella

et al. 2012

Journal of Location Based Services

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Abstract-The main problem of Pedestrian Dead-Reckoning (PDR) using only a body-attached IMU is the accumulation of heading errors. The heading provided by magnetometers in indoor buildings is in general not reliable and therefore it is commonly not used. Recently, a new method was proposed called Heuristic Drift Elimination (HDE) that minimizes the heading error when navigating in buildings. It assumes that the majority of buildings have their corridors parallel to each other, or they intersect at right angles, and consequently most of the time the person walks along a straight path with a heading constrained to one of four possible directions. In this paper we study the performance of HDE-based methods in complex buildings, i.e. with pathways also oriented at 45°, long curved corridors, and wide areas where non-oriented motion is possible. We explain how the performance of the original HDE method can be deteriorated in complex buildings, and also, how severe errors can appear in case of false matches with the building's dominant directions. Although magnetic compassing indoors has a chaotic behavior, in this paper we analyze large data-sets in order to study the potential use that magnetic compassing has to estimate the absolute yaw angle of a walking person. Apart from these analysis, this paper also proposes an improved HDE method called MiHDE (Magnetically-aided Improved Heuristic Drift Elimination), that is implemented over a PDR framework that uses foot-mounted inertial navigation with an Extended Kalman Filter (EKF). The EKF is fed with the MiHDE-estimated orientation error, gyro bias corrections, as well as the confidence over that corrections. We experimentally evaluated the performance of the proposed MiHDE-based PDR method, comparing it with the original HDE implementation. Results show that both methods perform very well in ideal orthogonal narrow-corridor buildings, and MiHDE outperforms HDE for non-ideal trajectories (e.g. curved paths) and also makes it robust against potential false dominant direction matchings.

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Section: B Hde Limitationsmentioning

confidence: 99%

Section: B Benefits: Finding Useful Information In Magnetic Ya Wmentioning

confidence: 99%

“…We use the foot-mounted IMU-based PDR algorithm proposed by Foxlin [3] and later refined by Jime´nez et al [4]. This PDR method was termed as IEZ [4]; it stands for INS-EKF-ZUPT i.e.…”

Section: A the Iez Framework For Pedestrian Navigationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings

Jiménez

Seco

Zampella

et al. 2012

Journal of Location Based Services

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“…In reference [11], [12], the zero velocity intervals are determined based on the variance of accelerometer values. In reference [13], the zero velocity is determined based on norms of accelerometers and gyroscopes along with variance of accelerations. In reference [14], the zero-velocity detection problem is abstracted as a hypothesis-testing problem.…”

Section: Introductionmentioning

confidence: 99%

Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors

Zhang¹,

Li²,

Yang³

et al. 2015

Measurement Science Review

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In this paper, a foot-mounted pedestrian navigation system using MEMS inertial sensors is implemented, where the zero-velocity detection is abstracted into a hidden Markov model with 4 states and 15 observations. Moreover, an observations extraction algorithm has been developed to extract observations from sensor outputs; sample sets are used to train and optimize the model parameters by the Baum-Welch algorithm. Finally, a navigation system is developed, and the performance of the pedestrian navigation system is evaluated using indoor and outdoor field tests, and the results show that position error is less than 3% of total distance travelled.

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Pedestrian Inertial Navigation With Self‐Contained Aiding

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Indoor pedestrian navigation using an INS/EKF framework for yaw drift reduction and a foot-mounted IMU

Cited by 401 publications

References 3 publications

Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings

Improved heuristic drift elimination with magnetically-aided dominant directions (MiHDE) for pedestrian navigation in complex buildings

Hidden Markov Model-based Pedestrian Navigation System using MEMS Inertial Sensors

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