Integrated navigation for pedestrian with building heading algorithm and inertial measurement unit

Ying-bo, Cai; Sun, Qian; Zhang, Ya; Yu, Chunzhu; Bai, Hongmei

doi:10.1109/iccais.2016.7822454

Cited by 2 publications

(1 citation statement)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The main algorithm structure of the pedestrian navigation system was proposed by Foxlin [8]; this is a shoe-mounted method using the extended Kalman filter, zero-velocity updates, and a strapdown inertial navigation system (SINS). Over the past few years, some researchers have tried to add other information sources to this main algorithm structure, such as building heading information proposed by Cai et al [9], prior maps used by Lategahn et al [10], and visual sensors used by Flores et al [11], to solve the problem of system error drift. However, the applications of these methods are limited to only some specific situations, and the system accuracy is affected heavily by the selected sensors.…”

Section: Introductionmentioning

confidence: 99%

3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition

Wang

Kong

et al. 2018

Complexity

View full text Add to dashboard Cite

An inertial measurement unit-based pedestrian navigation system that relies on the intelligent learning algorithm is useful for various applications, especially under some severe conditions, such as the tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments, such as those involving fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an optimal gait recognition algorithm to improve the accuracy of gait detection. Then a learning-based moving direction determination method was proposed. With the Kalman filter and a zero-velocity update algorithm, different gaits could be accurately recognized, such as going upstairs, downstairs, and walking flat. According to the recognition results, the position change in the vertical direction could be reasonably corrected. The obtained 3D trajectory involving both horizontal and vertical movements has shown that the accuracy is significantly improved in practical complex environments.

show abstract

Section: Introductionmentioning

confidence: 99%

3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition

Wang

Kong

et al. 2018

Complexity

View full text Add to dashboard Cite

show abstract

Pedestrian Trajectory Reconstruction for Indoor Movement Based on Foot-Mounted IMU

Wang,

Wang

2024

IECE trans. intell. syst.

View full text Add to dashboard Cite

A pedestrian navigation system (PNS) that only relies on the foot-mounted IMU is useful for various applications, especially under some severe conditions, such as tracking of firefighters and miners. Due to the complexity of the indoor environment, signal occlusion problems could lead to the failure of certain positioning methods. In complex environments such as fire rescue and emergency rescue, the barometric altimeter fails because of the influence of air pressure and temperature. This paper used an improved zero velocity detection algorithm to improve the accuracy of gait detection. Then, combine the Kalman filter with the zero velocity update algorithm to recognize gait accurately and take corresponding actions. Finally, the trajectory involving both horizontal and vertical movement was obtained, and the 3D positioning accuracy reached 97.5%. The proposed method avoids the redundancy of data fusion and can be used in complex unknown environments.

show abstract

Integrated navigation for pedestrian with building heading algorithm and inertial measurement unit

Cited by 2 publications

References 15 publications

3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition

3D Reconstruction of Pedestrian Trajectory with Moving Direction Learning and Optimal Gait Recognition

Pedestrian Trajectory Reconstruction for Indoor Movement Based on Foot-Mounted IMU

Contact Info

Product

Resources

About