scholarly journals A Pedestrian Navigation System Based on Low Cost IMU

2014 ◽  
Vol 67 (6) ◽  
pp. 929-949 ◽  
Author(s):  
Yan Li ◽  
Jianguo Jack Wang
Keyword(s):  
Constant Velocity ◽  
Inertial Measurement Unit ◽  
Stance Phase ◽  
Low Cost ◽  
Stair Climbing ◽  
Measurement Unit ◽  
Phase Detection ◽  
New Approach ◽  
Pedestrian Navigation ◽  
Zero Velocity

For indoor pedestrian navigation with a shoe-mounted inertial measurement unit (IMU), the zero velocity update (ZUPT) technique is implemented to constrain the sensors' error. ZUPT uses the fact that a stance phase appears in each step at zero velocity to correct IMU errors periodically. This paper introduces three main contributions we have achieved based on ZUPT. Since correct stance phase detection is critical for the success of applying ZUPT, we have developed a new approach to detect the stance phase of different gait styles, including walking, running and stair climbing. As the extension of ZUPT, we have proposed a new concept called constant velocity update (CUPT) to correct IMU errors on a moving platform with constant velocity, such as elevators or escalators where ZUPT is infeasible. A closed-loop step-wise smoothing algorithm has also been developed to eliminate discontinuities in the trajectory caused by sharp corrections. Experimental results demonstrate the effectiveness of the proposed algorithms.

Study of Zero Velocity Update for Both Low- and High-Speed Human Activities

2011 ◽  
Vol 2 (2) ◽  
pp. 46-67 ◽  
Author(s):  
R. Zhang ◽  
M. Loschonsky ◽  
L.M. Reindl
Keyword(s):  
Human Activities ◽  
Inertial Measurement Unit ◽  
High Speed ◽  
Inertial Sensor ◽  
Stair Climbing ◽  
Measurement Unit ◽  
Test Cases ◽  
Zero Velocity ◽  
Wide Range ◽  
Algorithm Implementation

Previous studies show that inertial sensor-based personal positioning benefited from Zero Velocity Update (ZUPT) method by resetting the foot speed at every foot step. However, only the solution for normal pedestrian movement with small velocity like walking was given. This paper presents a novel ZUPT system which can be used in a wide range of human activities, including walking, running, and stair climbing by using two inertial measurement unit (IMU) modules. One is attached on the centre of the human body for human activities’ classification and recognition. The other one is mounted on the foot for ZUPT algorithm implementation based on the result of activities’ recognition. Test cases include stair climbing by walking and running, walking, fast walking, and running. In all cases, most of the steps are able to be detected and the new ZUPT system can be successfully implemented.

scholarly journals Robust Self-Contained Pedestrian Navigation by Fusing the IMU and Compass Measurements via UFIR Filtering

2018 ◽  
Vol 2018 ◽  
pp. 1-6 ◽  
Author(s):  
Meng Hou ◽  
Yuan Xu ◽  
Xiao Liu
Keyword(s):  
Inertial Measurement Unit ◽  
Stance Phase ◽  
Finite Impulse Response ◽  
Measurement Unit ◽  
Test Results ◽  
Robust Performance ◽  
Pedestrian Navigation ◽  
Noise Statistics ◽  
Navigation Scheme ◽  
Real Test

In order to overcome the poor observability of yaw measurement for foot-mounted inertial measurement unit (IMU), an integrated IMU+Compass scheme for self-contained pedestrian navigation is presented. In this mode, the compass measurement is used to provide the accurate yaw to improve the accuracy of the attitude transformation matrix for the foot-mounted IMU solution. And then, when the person is in a stance phase during walk, a unbiased finite impulse response (UFIR) filter based on the self-contained pedestrian navigation scheme is investigated, which just needs the state vector size MU and the filtering horizon size NU, while ignoring the noise statistics compared with the Kalman filter (KF). Finally, a real test has been done to verify the performance of the proposed self-contained pedestrian navigation using the IMU and compass measurements via UFIR filter. The test results show that the proposed filter has robust performance compared with the KF.

scholarly journals LSS-RM: Using Multi-Mounted Devices to Construct a Lightweight Site-Survey Radio Map for WiFi Positioning

Micromachines ◽  
2018 ◽  
Vol 9 (9) ◽  
pp. 458 ◽  
Author(s):  
Wei Yang ◽  
Chundi Xiu ◽  
Jiarui Ye ◽  
Zhixing Lin ◽  
Haisong Wei ◽  
...  
Keyword(s):  
Large Scale ◽  
Stance Phase ◽  
Low Cost ◽  
Detection Algorithm ◽  
Measurement Unit ◽  
Survey Method ◽  
Phase Detection ◽  
Time Consumption ◽  
Site Survey ◽  
Radio Map

A WiFi-received signal strength index (RSSI) fingerprinting-based indoor positioning system (WiFi-RSSI IPS) is widely studied due to advantages of low cost and high accuracy, especially in a complex indoor environment where performance of the ranging method is limited. The key drawback that limits the large-scale deployment of WiFi-RSSI IPS is time-consuming offline site surveys. To solve this problem, we developed a method using multi-mounted devices to construct a lightweight site-survey radio map (LSS-RM) for WiFi positioning. A smartphone was mounted on the foot (Phone-F) and another on the waist (Phone-W) to scan WiFi-RSSI and simultaneously sample microelectromechanical system inertial measurement-unit (MEMS-IMU) readings, including triaxial accelerometer, gyroscope, and magnetometer measurements. The offline site-survey phase in LSS-RM is a client–server model of a data collection and preprocessing process, and a post calibration process. Reference-point (RP) coordinates were estimated using the pedestrian dead-reckoning algorithm. The heading was calculated with a corner detected by Phone-W and the preassigned site-survey trajectory. Step number and stride length were estimated using Phone-F based on the stance-phase detection algorithm. Finally, the WiFi-RSSI radio map was constructed with the RP coordinates and timestamps of each stance phase. Experimental results show that our LSS-RM method can reduce the time consumption of constructing a WiFi-RSSI radio map from 54 min to 7.6 min compared with the manual site-survey method. The average positioning error was below 2.5 m with three rounds along the preassigned site-survey trajectory. LSS-RM aims to reduce offline site-survey time consumption, which would cut down on manpower. It can be used in the large-scale implementation of WiFi-RSSI IPS, such as shopping malls, hospitals, and parking lots.

scholarly journals Gait Phase Detection for Lower-Limb Exoskeletons using Foot Motion Data from a Single Inertial Measurement Unit in Hemiparetic Individuals

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2988 ◽  
Author(s):  
Miguel D. Sánchez Sánchez Manchola ◽  
María J. Pinto Pinto Bernal ◽  
Marcela Munera ◽  
Carlos A. Cifuentes
Keyword(s):  
Lower Limb ◽  
Inertial Measurement Unit ◽  
Low Cost ◽  
Linear Acceleration ◽  
Measurement Unit ◽  
Phase Detection ◽  
Inertial Measurement ◽  
Phase Partitioning ◽  
Motion Data ◽  
Gait Phase

Due to the recent rise in the use of lower-limb exoskeletons as an alternative for gait rehabilitation, gait phase detection has become an increasingly important feature in the control of these devices. In addition, highly functional, low-cost recovery devices are needed in developing countries, since limited budgets are allocated specifically for biomedical advances. To achieve this goal, this paper presents two gait phase partitioning algorithms that use motion data from a single inertial measurement unit (IMU) placed on the foot instep. For these data, sagittal angular velocity and linear acceleration signals were extracted from nine healthy subjects and nine pathological subjects. Pressure patterns from force sensitive resistors (FSR) instrumented on a custom insole were used as reference values. The performance of a threshold-based (TB) algorithm and a hidden Markov model (HMM) based algorithm, trained by means of subject-specific and standardized parameters approaches, were compared during treadmill walking tasks in terms of timing errors and the goodness index. The findings indicate that HMM outperforms TB for this hardware configuration. In addition, the HMM-based classifier trained by an intra-subject approach showed excellent reliability for the evaluation of mean time, i.e., its intra-class correlation coefficient (ICC) was greater than 0 . 75 . In conclusion, the HMM-based method proposed here can be implemented for gait phase recognition, such as to evaluate gait variability in patients and to control robotic orthoses for lower-limb rehabilitation.

Accurate 3D Localization Using RGB-TOF Camera and IMU for Industrial Mobile Robots

Robotica ◽  
2021 ◽  
pp. 1-18
Author(s):  
Majid Yekkehfallah ◽  
Ming Yang ◽  
Zhiao Cai ◽  
Liang Li ◽  
Chuanxiang Wang
Keyword(s):  
Mobile Robots ◽  
Inertial Measurement Unit ◽  
Indoor Environment ◽  
State Of The Art ◽  
Low Cost ◽  
Estimation Algorithm ◽  
Measurement Unit ◽  
3D Localization ◽  
Fast Motion ◽  
Tof Camera

SUMMARY Localization based on visual natural landmarks is one of the state-of-the-art localization methods for automated vehicles that is, however, limited in fast motion and low-texture environments, which can lead to failure. This paper proposes an approach to solve these limitations with an extended Kalman filter (EKF) based on a state estimation algorithm that fuses information from a low-cost MEMS Inertial Measurement Unit and a Time-of-Flight camera. We demonstrate our results in an indoor environment. We show that the proposed approach does not require any global reflective landmark for localization and is fast, accurate, and easy to use with mobile robots.

Studying the Сonsistency of Extended Kalman Filter in Pedestrian Navigation with Foot-Mounted SINS

2021 ◽  
Vol 29 (2) ◽  
pp. 59-77
Author(s):  
Yu.V. Bolotin ◽  
◽  
A.V. Bragin ◽  
D.V. Gulevskii ◽  
◽  
...  
Keyword(s):  
Kalman Filter ◽  
Extended Kalman Filter ◽  
Stance Phase ◽  
Navigation Systems ◽  
Velocity Data ◽  
Inertial Navigation Systems ◽  
Pedestrian Navigation ◽  
Zero Velocity ◽  
Velocity Condition ◽  
Dynamic Errors

The paper focuses on pedestrian navigation with foot-mounted strapdown inertial navigation systems (SINS). Zero velocity updates (ZUPT) during the stance phase are commonly applied in such systems to improve the accuracy. Zero velocity data are processed by the extended Kalman filter (EKF). Zero velocity condition is written in two forms: in reference and body frames. The first form traditional for pedestrian navigation is shown to provide an inconsistent EKF. The second form provides a correct ZUPT algorithm, which is naturally written in so-called dynamic errors. The analyzed algorithm for data fusion from two SINS is based on the bound on foot-to-foot distance. It is shown how EKF inconsistency can be manifested, and how it can be avoided by proceeding back to dynamic errors. The results are obtained analytically using observability theory and covariance analysis.

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