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.

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

GPS/INS Integration During GPS Outages Using Machine Learning Augmented with Kalman Filter

2021 ◽  
Vol 16 ◽  
pp. 294-301
Author(s):  
Reshma Verma ◽  
Lakshmi Shrinivasan ◽  
K Shreedarshan
Keyword(s):  
Machine Learning ◽  
Kalman Filter ◽  
Global Positioning System ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation ◽  
Supervised Machine Learning ◽  
Positioning System ◽  
Position Information ◽  
Global Positioning

Nowadays a tremendous progress has been witnessed in Global Positioning System (GPS) and Inertial Navigation System (INS). The Global Positioning System provides information as long as there is an unobstructed line of sight and it suffers from multipath effect. To enhance the performance of an integrated Global Positioning System and Inertial Navigation System (GPS/INS) during GPS outages, a novel hybrid fusion algorithm is proposed to provide a pseudo position information to assist the integrated navigation system. A new model that directly relates the velocity, angular rate and specific force of INS to the increments of the GPS position is established. Combined with a Kalman filter the hybrid system is able to predict and estimate a pseud GPS position when GPS signal is unavailable. Field test data are collected to experimentally evaluate the proposed model. In this paper, the obtained GPS/INS datasets are pre-processed and semi-supervised machine learning technique has been used. These datasets are then passed into Kalman filtering for the estimation/prediction of GPS positions which were lost due to GPS outages. Hence, to bridge out the gaps of GPS outages Kalman Filter plays a major role in prediction. The comparative results of Kaman filter and extended Kalman filter are computed. The simulation results show that the GPS positions have been predicted taking into account some factors/measurements of a vehicle, the trajectory of the vehicle, the entire simulation was done using Anaconda (Jupyter Notebook).

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.

scholarly journals Application of Federal Kalman Filter with Neural Networks in the Velocity and Attitude Matching of Transfer Alignment

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Lijun Song ◽  
Zhongxing Duan ◽  
Bo He ◽  
Zhe Li
Keyword(s):  
Neural Networks ◽  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Estimation Error ◽  
Inertial Navigation ◽  
Transfer Alignment ◽  
The Neural Networks ◽  
Initial Attitude ◽  
Noise Statistics

The centralized Kalman filter is always applied in the velocity and attitude matching of Transfer Alignment (TA). But the centralized Kalman has many disadvantages, such as large amount of calculation, poor real-time performance, and low reliability. In the paper, the federal Kalman filter (FKF) based on neural networks is used in the velocity and attitude matching of TA, the Kalman filter is adjusted by the neural networks in the two subfilters, the federal filter is used to fuse the information of the two subfilters, and the global suboptimal state estimation is obtained. The result of simulation shows that the federal Kalman filter based on neural networks is better in estimating the initial attitude misalignment angle of inertial navigation system (INS) when the system dynamic model and noise statistics characteristics of inertial navigation system are unclear, and the estimation error is smaller and the accuracy is higher.

Incremental predictive Kalman filter for alignment of inertial navigation system

2018 ◽  
pp. 095441001879432
Author(s):  
Mahdi Fathi ◽  
Nematollah Ghahramani ◽  
Mohammad Ali Shahi Ashtiani ◽  
Ali Mohammadi ◽  
Mohsen Fallah
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Inertial Navigation System ◽  
Inertial Navigation

Error Modelling and Optimal Estimation of Laser Scanning Aided Inertial Navigation System in GNSS-Denied Environments

2018 ◽  
Vol 72 (3) ◽  
pp. 741-758 ◽  
Author(s):  
W.I. Liu ◽  
Zhixiong Li ◽  
Zhichao Zhang
Keyword(s):  
Kalman Filter ◽  
Navigation System ◽  
Laser Scanning ◽  
Inertial Navigation System ◽  
Laser Scanner ◽  
Inertial Navigation ◽  
Integration Algorithm ◽  
Biased Estimation ◽  
State Estimations ◽  
Estimation And Filtering

A Laser Scanning aided Inertial Navigation System (LSINS) is able to provide highly accurate position and attitude information by aggregating laser scanning and inertial measurements under the assumption that the rigid transformation between sensors is known. However, a LSINS is inevitably subject to biased estimation and filtering divergence errors due to inconsistent state estimations between the inertial measurement unit and the laser scanner. To bridge this gap, this paper presents a novel integration algorithm for LSINS to reduce the inconsistences between different sensors. In this new integration algorithm, the Radial Basis Function Neural Networks (RBFNN) and Singular Value Decomposition Unscented Kalman Filter (SVDUKF) are used together to avoid inconsistent state estimations. Optimal error estimation in the LSINS integration process is achieved to reduce the biased estimation and filtering divergence errors through the error state and measurement error model built by the proposed method. Experimental tests were conducted to evaluate the navigation performance of the proposed method in Global Navigation Satellite System (GNSS)-denied environments. The navigation results demonstrate that the relationship between the laser scanner coordinates and the inertial sensor coordinates can be established to reduce sensor measurement inconsistencies, and LSINS position accuracy can be improved by 23·6% using the proposed integration method compared with the popular Extended Kalman Filter (EKF) algorithm.

Sign in / Sign up

Export Citation Format

Share Document