scholarly journals INVESTIGATION OF DIFFERENT LOW-COST LAND VEHICLE NAVIGATION SYSTEMS BASED ON CPD SENSORS AND VEHICLE INFORMATION

2020 ◽  
Vol V-1-2020 ◽  
pp. 189-197
Author(s):  
M. Moussa ◽  
A. Moussa ◽  
M. Elhabiby ◽  
N. El-Sheimy
Keyword(s):  
Inertial Sensor ◽  
Low Cost ◽  
Integrated System ◽  
Steering Wheel ◽  
Vehicle Navigation ◽  
Portable Devices ◽  
Navigation Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Vehicle Information

Abstract. Recently, many companies and research centres have been working on research and development of navigation technologies for self-driving cars. Many navigation technologies were developed based on the fusion of various sensors. However, most of these techniques used expensive sensors and consequently increase the overall cost of such cars. Therefore, low-cost sensors are now a rich research topic in land vehicle navigation. Consumer Portable Devices (CPDs) such as smartphones and tablets are being widely used and contain many sensors (e.g. cameras, barometers, magnetometers, accelerometers, gyroscopes, and GNSS receivers) that can be used in the land vehicle navigation applications.This paper investigates various land vehicle navigation systems based on low-cost self-contained inertial sensors in CPD, vehicle information and on-board sensors with a focus on GNSS denied environment. Vehicle motion information such as forward speed is acquired from On-Board Diagnosis II (OBD-II) while the land vehicle heading change is estimated using CPD attached to the steering wheel. Additionally, a low-cost on-board GNSS/inertial integrated system is also employed. The paper investigates many navigation schemes such as different Dead Reckoning (DR) systems, Reduced Inertial Sensor System (RISS) based systems, and aided loosely coupled GNSS/inertial integrated system.An experimental road test is performed, and different simulated GNSS signal outages were applied to the data. The results show that the modified RISS system based on OBD-II velocity, onboard gyroscopes, accelerometers, and CPD-based heading change provides a better navigation estimation than the typical RISS system for 90s GNSS signal outage. On the other hand, typical inertial aided with CPD heading change, OBD-II velocity updates, and Non-Holonomic Constraint (NHC) provide the best navigation result.

scholarly journals Steering Angle Assisted Vehicular Navigation Using Portable Devices in GNSS-Denied Environments

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1618 ◽  
Author(s):  
Mohamed Moussa ◽  
Adel Moussa ◽  
Naser El-Sheimy
Keyword(s):  
Low Cost ◽  
Measurement Unit ◽  
Steering Wheel ◽  
Portable Devices ◽  
Steering Angle ◽  
Navigation Solution ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Novel Approach ◽  
Vehicle Acceleration

Recently, land vehicle navigation, and especially by the use of low-cost sensors, has been the object of a huge level of research interest. Consumer Portable Devices (CPDs) such as tablets and smartphones are being widely used by many consumers all over the world. CPDs contain sensors (accelerometers, gyroscopes, magnetometer, etc.) that can be used for many land vehicle applications such as navigation. This paper presents a novel approach for estimating steering wheel angles using CPD accelerometers by attaching CPDs to the steering wheel. The land vehicle change of heading is then computed from the estimated steering wheel angle. The calculated change of heading is used to update the navigation filter to aid the onboard Inertial Measurement Unit (IMU) through the use of an Extended Kalman Filter (EKF) in GNSS-denied environments. Four main factors that may affect the steering wheel angle accuracy are considered and modeled during steering angle estimations: static onboard IMU leveling, inclination angle of the steering wheel, vehicle acceleration, and vehicle inclination. In addition, these factors are assessed for their effects on the final result. Therefore, three methods are proposed for steering angle estimation: non-compensated, partially-compensated, and fully-compensated methods. A road experimental test was carried out using a Pixhawk (PX4) navigation system, iPad Air, and the OBD-II interface. The average Root Mean Square Error (RMSE) of the change of heading estimated by the proposed method was 0.033 rad/s. A navigation solution was estimated while changes of heading and forward velocity updates were used to aid the IMU during different GNSS signal outages. The estimated navigation solution is enhanced when applying the proposed updates to the navigation filter by 91% and 97% for 60 s and 120 s of GNSS signal outage, respectively, compared to the IMU standalone solution.

A Kalman Filter Model for an Integrated Land Vehicle Navigation System

1995 ◽  
Vol 48 (2) ◽  
pp. 293-302 ◽  
Author(s):  
Allison N. Ramjattan ◽  
Paul A. Cross
Keyword(s):  
Dead Reckoning ◽  
Integrated System ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Filter Model ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Vehicle Navigation System ◽  
Vehicle Position

Unlike in the case of airborne and offshore applications, GPS cannot be used continuously for land vehicle navigation due to the loss of satellite signals by obstructions from buildings, trees, etc. With the increasing trend in various sectors of the economy towards efficient fleet management, the challenges of providing a system capable of providing high-accuracy vehicle position and location anywhere, continuously, has led to renewed interest in the area of integrated navigation systems. In order to satisfy these conditions, an integrated system comprising GPS and gyro/odometer dead reckoning has been developed. This paper gives a description of the implemented system and shows some of the practical results that can be obtained using Kalman filtering algorithms.

GPS/INS/Odometer Integrated System Using Fuzzy Neural Network for Land Vehicle Navigation Applications

2014 ◽  
Vol 67 (6) ◽  
pp. 967-983 ◽  
Author(s):  
Zengke Li ◽  
Jian Wang ◽  
Binghao Li ◽  
Jingxiang Gao ◽  
Xinglong Tan
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Integrated System ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Positioning Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Fuzzy Neural

The integration of Global Positioning Systems (GPS) with Inertial Navigation Systems (INS) has been very actively studied and widely applied for many years. Some sensors and artificial intelligence methods have been applied to handle GPS outages in GPS/INS integrated navigation. However, the integrated system using the above method still results in seriously degraded navigation solutions over long GPS outages. To deal with the problem, this paper presents a GPS/INS/odometer integrated system using a fuzzy neural network (FNN) for land vehicle navigation applications. Provided that the measurement type of GPS and odometer is the same, the topology of a FNN used in a GPS/INS/odometer integrated system is constructed. The information from GPS, odometer and IMU is input into a FNN system for network training during signal availability, while the FNN model receives the observations from IMU and odometer to generate odometer velocity correction to enhance resolution accuracy over long GPS outages. An actual experiment was performed to validate the new algorithm. The results indicate that the proposed method can improve the position, velocity and attitude accuracy of the integrated system, especially the position parameters, over long GPS outages.

GPS-based Land Vehicle Navigation System Assisted by a Low-Cost Gyro-Free INS Using Neural Network

2004 ◽  
Vol 57 (3) ◽  
pp. 417-428 ◽  
Author(s):  
Jau-Hsiung Wang ◽  
Yang Gao
Keyword(s):  
Neural Network ◽  
Navigation System ◽  
Urban Areas ◽  
Low Cost ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Land Vehicle ◽  
Land Vehicle Navigation ◽  
Artificial Neural Network Ann ◽  
Signal Fading

GPS-based land vehicle navigation systems are subject to signal fading in urban areas and require aid from other enabling sensors. A low-cost gyro-free inertial navigation system (INS) without accumulated attitude errors and complicated initializations could be an effective solution to the problem. This paper investigates a Constrained Navigation Algorithm (CNA) and the Artificial Neural Network (ANN) technique to compensate velocity output from a gyro-free INS. The vehicle's heading will be calibrated by a full circle test so that the magnetometer's bias and scale factor error could be removed. Experiments with a vehicle driven over level terrain have been conducted to assess the performance of the compensated gyro-free INS solutions. The effect of the architecture of Neural Network on prediction performance has also been discussed as well as the applicability of the proposed solution to land vehicle navigation with GPS outages.

Performance Analysis of Medium Accuracy FOG-Based IMU Integrated with Optical Odometer for Land Vehicle Navigation

2012 ◽  
Vol 245 ◽  
pp. 334-339 ◽  
Author(s):  
Muhammad Ilyas ◽  
Ren Zhang ◽  
Qiu Shi Qian ◽  
Yun Chun Yang
Keyword(s):  
Kalman Filter ◽  
Performance Analysis ◽  
Fiber Optics ◽  
Low Cost ◽  
Measurement Noise ◽  
Vehicle Navigation ◽  
Navigation Systems ◽  
Integrated Navigation ◽  
Land Vehicle ◽  
Land Vehicle Navigation

The aim of this work is to research the feasibility of using optical odometer as the aided sensor for accuracy improvement of medium accuracy (FOG)-based IMU for land vehicle navigation. Usually GNSS is integrated with low cost SINS (strapdown inertial navigation systems) for land vehicle navigation but GNSS is not always reliable in land vehicle applications. The focus is on analysis of position and velocity accuracy of SINS/Odometer integration using close loop kalman filter. Integrated navigation algorithm for vehicle states estimation and correction have been designed and implemented. The measurement error model for odometer in navigation frame is developed for Kalman filter implementation. As the prior knowledge of measurement noise which represents the stochastic properties of odometer is not exactly known, so an adaptive Kalman filter (AKF) is also proposed for online estimation of the measurement noise matrix in order to improve the accuracy. For the performance analysis of the designed system, field test is carried out and results show that the accuracy of the medium accuracy fiber optics gyro (FOG)-based SINS is improved and the systems is capable for land vehicle navigation application

A Universal Approach for Processing any MEMS Inertial Sensor Configuration for Land-Vehicle Navigation

2007 ◽  
Vol 60 (2) ◽  
pp. 233-245 ◽  
Author(s):  
Xiaoji Niu ◽  
Sameh Nasser ◽  
Chris Goodall ◽  
Naser El-Sheimy
Keyword(s):  
Inertial Sensor ◽  
Low Cost ◽  
Navigation Systems ◽  
Micro Electro Mechanical Systems ◽  
Land Vehicle ◽  
Mems Sensor ◽  
Navigation Algorithm ◽  
Main Challenge ◽  
Universal Approach ◽  
Sensor Configuration

Recent navigation systems integrating GPS with Micro-Electro-Mechanical Systems (MEMS) Inertial Measuring Units (IMUs) have shown promising results for several applications based on low-cost devices such as vehicular and personal navigation. However, as a trend in the navigation market, some applications require further reductions in size and cost. To meet such requirements, a MEMS full IMU configuration (three gyros and three accelerometers) may be simplified. In this context, different partial IMU configurations such as one gyro plus three accelerometers or one gyro plus two accelerometers could be investigated. The main challenge in this case is to develop a specific navigation algorithm for each configuration since this is a time-consuming and costly task. In this paper, a universal approach for processing any MEMS sensor configuration for land vehicular navigation is introduced. The proposed method is based on the assumption that the omitted sensors provide relatively less navigation information and hence, their output can be replaced by pseudo constant signals plus noise. Using standard IMU/GPS navigation algorithms, signals from existing sensors and pseudo signals for the omitted sensors are processed as a full IMU. The proposed approach is tested using land-vehicle MEMS/GPS data and implemented with different sensor configurations. Compared to the full IMU case, the results indicate the differences are within the expected levels and that the accuracy obtained meets the requirements of several land-vehicle applications.

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