A Bimodal Approach for Land Vehicle Localization

Kim, Seong-Baek; Choi, Kyoung-Ho; Lee, Seung Yong; Choi, Jihoon; Hwang, Taesung; Jang, Byungtae; Lee, Jong-Hun

doi:10.4218/etrij.04.0203.0030

Cited by 14 publications

(4 citation statements)

References 16 publications

(22 reference statements)

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“…The error of the inertial measurement unit (IMU) is one of the main sources of error for a SINS. The IMU mainly consists of inertial sensors such as accelerometers and gyros which measure the specific force and the angular rate of a vehicle, respectively, and output the navigation information through some algorithms [ 5 , 6 ]. Error compensation for SINS is an effective method to improve the accuracy of SINS navigation, whereas error calibration is a prerequisite for error compensation.…”

Section: Introductionmentioning

confidence: 99%

A Separated Calibration Method for Inertial Measurement Units Mounted on Three-Axis Turntables

Dong

Ren

Chen

et al. 2018

Sensors

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Inertial Measurement Unit (IMU) calibration accuracy is easily affected by turntable errors, so the primary aim of this study is to reduce the dependence on the turntable’s precision during the calibration process. Firstly, the indicated-output of the IMU considering turntable errors is constructed and with the introduction of turntable errors, the functional relationship between turntable errors and the indicated-output was derived. Then, based on a D-suboptimal design, a calibration method for simultaneously identifying the IMU error model parameters and the turntable errors was proposed. Simulation results showed that some turntable errors could thus be effectively calibrated and automatically compensated. Finally, the theoretical validity was verified through experiments. Compared with the traditional method, the method proposed in this paper can significantly reduce the influence of the turntable errors on the IMU calibration accuracy.

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Section: Introductionmentioning

confidence: 99%

A Separated Calibration Method for Inertial Measurement Units Mounted on Three-Axis Turntables

Dong

Ren

Chen

et al. 2018

Sensors

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“…Two means of localization have dominated the community: the use of external sensors to localize the robot (Kim et al, 2004;Borenstein and Feng, 1996b;Kim et al, 1999;Chung et al, 2001;Murata and Hirose, 1993;Adam et al, 1999;Chong et al, 1999;Jensfelt and Kristensen, 2001;Fox et al, 1999) and the filtering of odometry information to ascertain the robot's location relative to a starting location (Borenstein and Feng, 1996a;Larsen et al, 1998;Roy and Thrun, 1999;Martinelli et al, 2003;Wang, 1988;Kelly, 2004). The filtering, commonly referred to as dead-reckoning or deduced reckoning, suffers from inaccuracies caused by imprecise kinematic parameters and wheel slippage.…”

Section: Introductionmentioning

confidence: 99%

Relative localization using path odometry information

2006

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All mobile bases suffer from localization errors. Previous approaches to accommodate for localization errors either use external sensors such as lasers or sonars, or use internal sensors like encoders. An encoder's information is integrated to derive the robot's position; this is called odometry. A combination of external and internal sensors will ultimately solve the localization error problem, but this paper focuses only on processing the odometry information. We solve the localization problem by forming a new odometry error model for the synchro-drive robot then use a novel procedure to accurately estimate the error parameters of the odometry error model. This new procedure drives the robot through a known path and then uses the shape of the resulting path to estimate the model parameters. Experimental results validate that the proposed method precisely estimates the error parameters and that the derived odometry error model of the synchro-drive robot is correct.

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“…To guarantee proper operation of these systems such as the localization systems [1]- [5], and the vehicle dynamics aiding systems that may include ESP (electronic stability program) and ROM (roll over mitigation), it is crucial that the sensor measurement information is reliable enough. Unfortunately, however, it must be noted that the highly accurate sensors are only available at a high cost.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Offset Error Compensation of 6D IMU Mounted on Ground Vehicles Using Disturbance Observer

Oh¹,

Noh²,

Choi³

2013

JACN

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Abstract-This paper mainly deals with the offset error compensation algorithm related with the 6D IMU (inertial measurement unit) that measures the linear accelerations and angular velocities about the longitudinal, lateral, and vertical axis of ground vehicles. It is assumed that the independent wheel speed data and steering wheel angle information are provided for the sensor compensation algorithm. Using a disturbance observer, through designing a linear model and inverse model of the vehicle motion, the offset errors of the accelerometers are estimated. The stability of the entire compensational system is verified, and finally, the performance of the suggested algorithm is tested based on a well-known vehicle dynamics simulation tool, CarSim.Index Terms-Bicycle model, disturbance observer, inertial measurement unit, offset compensation, signal processing.

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A Bimodal Approach for Land Vehicle Localization

Abstract: ABSTRACTIn this paper, we present a novel idea to integrate a low cost inertial measurement unit (IMU) and

Cited by 14 publications

References 16 publications

A Separated Calibration Method for Inertial Measurement Units Mounted on Three-Axis Turntables

A Separated Calibration Method for Inertial Measurement Units Mounted on Three-Axis Turntables

Relative localization using path odometry information

Real-Time Offset Error Compensation of 6D IMU Mounted on Ground Vehicles Using Disturbance Observer

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