A reliable multisensor fusion strategy for land vehicle positioning using low-cost sensors

Author(s):  
Xu Li ◽  
Wei Chen ◽  
Chingyao Chan
2007 ◽  
Vol 2007 (1) ◽  
pp. 062616 ◽  
Author(s):  
Jianchen Gao ◽  
MarkG Petovello ◽  
MElizabeth Cannon

2007 ◽  
Vol 2007 ◽  
pp. 1-14 ◽  
Author(s):  
Jianchen Gao ◽  
Mark G. Petovello ◽  
M. Elizabeth Cannon

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1618 ◽  
Author(s):  
Mohamed Moussa ◽  
Adel Moussa ◽  
Naser El-Sheimy

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.


2007 ◽  
Vol 60 (2) ◽  
pp. 233-245 ◽  
Author(s):  
Xiaoji Niu ◽  
Sameh Nasser ◽  
Chris Goodall ◽  
Naser El-Sheimy

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.


2017 ◽  
Vol 25 (2) ◽  
pp. 161-172 ◽  
Author(s):  
Joong-hee Han ◽  
Jay Hyoun Kwon ◽  
Chang-Ki Hong ◽  
Yong Lee

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