A single foot-mounted pedestrian navigation algorithm based on maximum gait displacement constraint in three-dimensional space

2021 ◽  
Author(s):  
Jianyu Wang ◽  
Jinhao Liu ◽  
Xiangbo Xu ◽  
Zhibin Yu ◽  
Zhe Li
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
Dimensional Space ◽  
Inertial Navigation ◽  
Inequality Constraint ◽  
Spatial Distance ◽  
Walking Distance ◽  
Measurement Unit ◽  
Positioning Accuracy ◽  
Constraint Method

Abstract Inertial navigation technology composed of inertial sensors is widely used in foot-mounted pedestrian positioning. However, inertial sensors are susceptible to noise, which affects the performance of the system. The zero-velocity update (ZUPT) as a traditional method is utilized to suppress the cumulative error. Unfortunately, the walking distance calculated by a Kalman filter still has position error. To improve the positioning accuracy, a nonlinear Kalman filter with spatial distance inequality constraint for single foot is proposed in this work. Since the stride distance between adjacent stance phases has an upper bound in plane and height, an inertial navigation system (INS) established by one inertial measurement unit (IMU) is adopted to constrain the stride process. Eventually, the performance of the proposed method is verified by experiments. Compared to the single foot-mounted ZUPT method, the proposed method suppresses the plane error and the height error by 46.04% and 65.48%, respectively. For the dual foot constraint method, the proposed constraint method can reduce the number of sensors while ensuring the positioning accuracy. Moreover, the height error is reduced by 59.98% on average by optimizing the constraint algorithm. The experimental results show that the trajectory estimated by the proposed method is closer to the actual path.

scholarly journals Dual MIMU Pedestrian Navigation by Inequality Constraint Kalman Filtering

2016 ◽  
Author(s):  
Wei Shi ◽  
Yang Wang ◽  
Yuanxin Wu
Keyword(s):  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Inequality Constraint ◽  
Time Range ◽  
Navigation Systems ◽  
Inertial Navigation Systems ◽  
Pedestrian Navigation ◽  
External Assistance ◽  
Constraint Method

The foot-mounted inertial navigation system is an important application of pedestrian navigation as it in principle does not rely any external assistance. A real-time range decomposition constraint method is proposed in this paper to combine the information of dual foot-mounted inertial navigation systems. It is well known that low-cost inertial sensors with ZUPT (zero-velocity update) and range decomposition constraint perform better than in either single way. This paper recommends that the distance of separation between the position estimates of feet-mounted inertial navigation systems be restricted in the ellipsoidal constraint which relates to the maximum step and leg height. The performance of the proposed method is studied utilizing experimental data. The results indicate that the method can effectively correct the dual navigation systems’ position over the existing spherical constraint.

An Improved and Efficient Algorithm for SINS/GPS/Doppler Integrated Navigation Systems

2012 ◽  
Vol 245 ◽  
pp. 323-329 ◽  
Author(s):  
Muhammad Ushaq ◽  
Jian Cheng Fang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Efficient Manner ◽  
Position Errors

Inertial navigation systems exhibit position errors that tend to grow with time in an unbounded mode. This degradation is due, in part, to errors in the initialization of the inertial measurement unit and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Mitigation to this growth and bounding the errors is to update the inertial navigation system periodically with external position (and/or velocity, attitude) fixes. The synergistic effect is obtained through external measurements updating the inertial navigation system using Kalman filter algorithm. It is a natural requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertia Navigation System (SINS), Global Positioning System (GPS) and Doppler radar is presented using a centralized linear Kalman filter by treating vector measurements with uncorrelated errors as scalars. Two main advantages have been obtained with this improved scheme. First is the reduced computation time as the number of arithmetic computation required for processing a vector as successive scalar measurements is significantly less than the corresponding number of operations for vector measurement processing. Second advantage is the improved numerical accuracy as avoiding matrix inversion in the implementation of covariance equations improves the robustness of the covariance computations against round off errors.

Enhanced Accuracy Navigation Solutions Realized through SINS/GPS Integrated Navigation System

2013 ◽  
Vol 332 ◽  
pp. 79-85
Author(s):  
Outamazirt Fariz ◽  
Muhammad Ushaq ◽  
Yan Lin ◽  
Fu Li
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensor ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Position Errors ◽  
Strapdown Inertial Navigation

Strapdown Inertial Navigation Systems (SINS) displays position errors which grow with time in an unbounded manner. This degradation is due to the errors in the initialization of the inertial measurement unit, and inertial sensor imperfections such as accelerometer biases and gyroscope drifts. Improvement to this unbounded growth in errors can be made by updating the inertial navigation system solutions periodically with external position fixes, velocity fixes, attitude fixes or any combination of these fixes. The increased accuracy is obtained through external measurements updating inertial navigation system using Kalman filter algorithm. It is the basic requirement that the inertial data and data from the external aids be combined in an optimal and efficient manner. In this paper an efficient method for integration of Strapdown Inertial Navigation System (SINS), Global Positioning System (GPS) is presented using a centralized linear Kalman filter.

scholarly journals Research on the Shearer Positioning Method Based on the MEMS Inertial Sensors/Odometer Integrated Navigation System and RTS Smoother

Micromachines ◽  
2021 ◽  
Vol 12 (12) ◽  
pp. 1527
Author(s):  
Jiangtao Zheng ◽  
Sihai Li ◽  
Shiming Liu ◽  
Bofan Guan ◽  
Dong Wei ◽  
...  
Keyword(s):  
Inertial Measurement Unit ◽  
Inertial Sensors ◽  
Optimal Estimation ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Estimation Model ◽  
Positioning Accuracy ◽  
Inertial Measurement ◽  
Integrated Navigation System ◽  
Positioning Method

The shearer positioning method with an inertial measurement unit and the odometer is feasible in the longwall coal-mining process. However, the positioning accuracy will continue to decrease, especially for the micro-electromechanical inertial measurement unit (MIMU). In order to further improve the positioning accuracy of the shearer without adding other external sensors, the positioning method of the Rauch-Tung-Striebel (RTS) smoother-aided MIMU and odometer is proposed. A Kalman filter (KF) with the velocity and position measurements, which are provided by the odometer and closing path optimal estimation model (CPOEM), respectively, is established. The observability analysis is discussed to study the possible conditions under which the error states of KF can be estimated. A RTS smoother with the above-mentioned KF as the forward filter is built. Finally, the experiments of simulating the movement of the shearer through a mobile carrier were carried out, with a longitudinal movement distance of 44.6 m and a lateral advance distance of 1.2 m. The results show that the proposed method can effectively improve the positioning accuracy. In addition, the odometer scale factor and mounting angles can be estimated in real time.

scholarly journals Implementation of Hybrid Indoor Positioning System based on Wi-Fi and PDR in Smartphone

2019 ◽  
Vol 8 (2S6) ◽  
pp. 357-361
Keyword(s):  
Kalman Filter ◽  
Inertial Sensors ◽  
Dead Reckoning ◽  
Indoor Positioning ◽  
Initial Position ◽  
Mobile User ◽  
Sensor Data ◽  
Positioning System ◽  
Fusion Algorithm ◽  
Positioning Accuracy

This paper proposed thehybridindoor positioning system in smartphone for positioning accuracy by fusion of wireless-fidelity (Wi-Fi) signals and inertial sensors from pedestrian dead reckoning (PDR) in smartphone. The proposed system uses Wi-Fi as the source of received signal strength indicator (RSSI) for fingerprint and smartphones sensor data from PDR. RSSI signals are used to determine the initial position and reduce error accumulation of PDR while smartphone sensor data are used to estimate user trajectory. Extended Kalman Filter (EKF) is the fusion algorithm used for its similarity with Kalman Filter (KF) but with advantages of processing non-linear progressions. An estimated 49 steps were detected which is identical to the 50 steps taken in the experiment while showing a trajectory similar to the actual route taken by the mobile user. A benefit of using built-in smartphone sensors is its cost-effectiveness and availability that does not require additional hardware. In addition, a nonlinear EKF is used to enhance the positioning accuracy in the proposed system. Further studies will be made in the potential of indoor positioning algorithm including the effect of noise interference on sensors and RSSI and the accumulated errors resulting from walking

Aiding Low Cost Inertial Navigation with Building Heading for Pedestrian Navigation

2011 ◽  
Vol 64 (2) ◽  
pp. 219-233 ◽  
Author(s):  
Khairi Abdulrahim ◽  
Chris Hide ◽  
Terry Moore ◽  
Chris Hill
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Sensors ◽  
Low Cost ◽  
Inertial Navigation ◽  
Field Trials ◽  
Basic Knowledge ◽  
Effective Solution ◽  
Pedestrian Navigation

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.

A Fault Tolerant SINS/GPS/CNS Integrated Navigation Scheme Realized through Federated Kalman Filter

2013 ◽  
Vol 332 ◽  
pp. 104-110 ◽  
Author(s):  
Muhammad Ushaq ◽  
Fang Jian Cheng ◽  
Ali Jamshaid
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Research Work ◽  
Inertial Navigation ◽  
Satellite Navigation ◽  
Measurement Unit ◽  
Integrated Navigation ◽  
Satellite Navigation System ◽  
Celestial Navigation ◽  
Federated Kalman Filter

The complementary characteristics of the Strapdown Inertial Navigation System (SINS) and external non-inertial navigation aids like Global Positioning System (GPS) and Celestial Navigation System (CNS) make the integrated navigation system an appealing and cost effective solution for various applications. SINS exhibits position errors owing to errors in initialization of the inertial measurement unit (IMU) and the inherent accelerometer biases and gyroscope drifts. SINS also suffer from diverging azimuth errors and an exponentially increasing vertical channel error. Pitch and roll errors also exhibit unbounded growth with time. To mitigate this behavior of SINS, periodic corrections are opted for through measurements from external non-inertial navigation aids. These corrections can be in the form of position fixing, velocity fixing and attitude fixing from external aids like GPS, GLONASS (Russian Satellite Navigation System), BEIDU(Chinese Satellite Navigation System) and Celestial Navigation Systems (CNS) etc. In this research work GPS and CNS are used as external aids for SINS and the navigation solutions of all three systems (SINS, GPS and CNS) are fused using Federated Kalman Filter (FKF). The FKF differs from the conventional Central Kalman Filter (CKF) because each measurement is processed in Local Filters (LFs), and the results are combined in a Master Filter (MF). FKF is a partitioned estimation method that uses a two stage data processing scheme, in which the outputs of sensor related LFs are subsequently combined by a large MF. Each LF is dedicated to a separate sensor subsystem, and uses data from the common reference such as SINS. The SINS acts as a cardinal system in the combination, and its data is also available as measurement input for the master filter. In this research work, information from the GPS and the CNS are dedicated to the corresponding LFs. Each LF provides its solutions to the master filter all information is fused together forming a global solution. Simulation for the proposed architecture has validated the effectiveness of the scheme, by showing the substantial precision improvement in the solutions of position, velocity and attitude as compared to the pure SINS or any other standalone system.

scholarly journals Mobile robot positioning algorithm based on Kalman filtering method in network environment

2020 ◽  
Vol 327 ◽  
pp. 03005
Author(s):  
Shuang Zhang
Keyword(s):  
Mobile Robot ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Measurement Unit ◽  
Relative Positioning ◽  
Positioning Accuracy ◽  
Reference Information ◽  
Absolute Positioning ◽  
Positioning Method

Positioning is the basic link in a multi-mobile robot control system, and is also a problem that must be solved before completing a specified task. The positioning method can be generally divided into relative positioning and absolute positioning. Absolute positioning method refers to that the robot calculates its current position by acquiring the reference information of some known positions in the outside world, calculating the relationship between itself and the reference information. Absolute positioning generally adopts methods based on beacons, environment map matching, and visual positioning. The relative positioning method mainly uses the inertial navigation system INS. The inertial navigation system directly fixes the inertial measurement unit composed of the gyroscope and the accelerometer to the target device, and uses the inertial devices such as the gyroscope and the accelerometer to measure the triaxial angular velocity and The three-axis acceleration information is measured and integrated, and the mobile robot coordinates are updated in real time. Combined with the initial inertial information of the target device, navigation information such as the attitude, speed, and position of the target device is obtained through integral operation [1-2]. The inertial navigation system does not depend on external information when it is working, and is not easily damaged by interference. As an autonomous navigation system, it has the advantages of high data update rate and high short-term positioning accuracy [3]. However, under the long-term operation of inertial navigation, due to the cumulative error of integration, the positioning accuracy is seriously degraded, so it is necessary to seek an external positioning method to correct its position information [4]

scholarly journals FPGA Control of a Mobile Inverted Pendulum Robot

1970 ◽  
Vol 8 (1-2) ◽  
pp. 188-196
Author(s):  
Bikram Adhikari ◽  
Deepak Gurung ◽  
Giresh Singh Kunwar ◽  
Prashanta Gyawali
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
Pid Control ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inverted Pendulum ◽  
Inertial Sensors ◽  
Inertial Navigation ◽  
Soft Core ◽  
Quadrature Phase

The inverted pendulum is a classic problem in dynamics and control theory due to its inherently unstable nature. In the system tested, Field Programmable Gate Arrays (FPGAs) are used for the implementation of control and sensor fusion algorithms in the inertial navigation system of a Mobile Inverted Pendulum (MIP) robot. Additionally, the performance of digital PID control and Kalman filter algorithms are tested in this FPGA system. The test platform for tuning Kalman filter is designed using optical encoders as a standard reference. PWM signal generation and quadrature phase decoding of encoder pulses is accomplished using hardware description language in FPGA. The values from the inertial sensors and quadrature phase decoded values are fed into MicroBlaze, a 32-bit soft-core RISC processor, within the FPGA. The overall system demonstrates the use of low cost inertial sensors to balance a two wheeled robot. The system is presently able to balance on its own and it also serves as an extremely reconfigurable FPGA based platform to facilitate future modifications, updates and enhancements with more complex control and sensor fusion techniques.Key Terms: Mobile Inverted Pendulum System; Inverted Pendulum Robot; Inertial Navigation System; FPGA; Kalman Filter; PID Control; Soft-core ProcessorDOI: http://dx.doi.org/10.3126/jie.v8i1-2.5111Journal of the Institute of EngineeringVol. 8, No. 1&2, 2010/2011Page: 188-196Uploaded Date: 20 July, 2011

Kalman Filter-Based Sensor Fusion for Improving Localization of AGV

2012 ◽  
Vol 488-489 ◽  
pp. 1818-1822 ◽  
Author(s):  
In Seong Lee ◽  
Jae Yong Kim ◽  
Jun Ha Lee ◽  
Jung Min Kim ◽  
Sung Sin Kim
Keyword(s):  
Kalman Filter ◽  
Sensor Fusion ◽  
High Precision ◽  
Navigation System ◽  
High Speed ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Positioning Accuracy ◽  
Global Positioning ◽  
Precision Device

This paper proposes localization using sensor fusion with a laser navigation and an inertial navigation system for indoor mobile. The laser navigation is a device that measures angle and distance between the robot and the reflectors. Although it is the high-precision device for indoor global positioning, there is a problem that the accuracy of laser navigation significantly drops while moving at high speed and rapid turning. To solve this problem, the laser navigation was fused to inertial navigation system through Kalman filter. For experiment, we use omnidirectional robot with Mecanum Wheels and analyze the positioning accuracy according to driving direction of the robot.

Sign in / Sign up

Export Citation Format

Share Document